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In This Issue Differential Effects of Sucrose and Plant Growth Regulator on Shoot Multiplication and Bulbil Formation in Oxalis Versicolour In Vitro Preparation of Activated Carbon from Sindora Siamensis Seed and Canarium Sublatum Guillaumin fruit for Methylene Blue Adsorption Use of Filter Media Made from Vetiver Grass Root Ash for Water Treatment A Review of ResourceConstrained Project Scheduling Problems (RCPSP) Approaches and Solutions Volume 5 Issue 4 (October 2014) ISSN 2228-9860 eISSN 1906-9642

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Comparison between Analytical Results and Response of the Laboratory-Scaled Truss Bridges under the Moving Car Load

Cover Photo is from Samarn Srisa-ard research article in this issue (Preparation of Activated Carbon from Sindora Siamensis Seed and Canarium Sublatum Guillaumin fruit for Methylene Blue Adsorption). The photo depicts SEM micrographs for (a) raw ash, (b) SSAC for activated at 500째C, (c) SSAC for activated at 600째C and (d) SSAC for activated at 700째C.

2014 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

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International Editorial Board

Editor-in-Chief Ahmad Sanusi Hassan, PhD Associate Professor Universiti Sains Malaysia, MALAYSIA

Executive Editor Boonsap Witchayangkoon, PhD Associate Professor Thammasat University, THAILAND

Noble Editorial Board:

Professor Dr.Mikio SATOMURA (Shizuoka University, JAPAN) Professor Dr.Chuen-Sheng Cheng (Yuan Ze University, TAIWAN) Professor Dr.I Nyoman Pujawan (Sepuluh Nopember Institute of Technology, INDONESIA) Professor Dr.Neven Duić (University of Zagreb, CROATIA) Professor Dr.Lee, Yong-Chang (Incheon City College SOUTH KOREA) Professor Dr.Dewan M. Nuruzzaman (Dhaka University of Engineering & Technology, BANGLADESH) Professor Dr. Lutero Carmo de Lima (State University of Ceará, BRAZIL )

Scientific and Technical Committee & Editorial Review Board on Engineering, Technologies and Applied Sciences:

Associate Prof. Dr. Paulo Cesar Lima Segantine (University of São Paulo, BRASIL) Associate Prof. Dr. Kurt B. Wurm (New Mexico State University, USA ) Associate Prof. Dr. Truong Vu Bang Giang (Vietnam National University, Hanoi, VIETNAM ) Dr.H. Mustafa Palancıoğlu (Erciyes University, TURKEY) Associate Prof.Dr.Peter Kuntu-Mensah (Texas A&M University-Corpus Christi, USA) Associate Prof. Dr. Masato SAITOH (Saitama University, JAPAN ) Assistant Prof.Dr. Zoe D. Ziaka (International Hellenic University, GREECE ) Associate Prof.Dr. Junji SHIKATA (Yokohama National University, JAPAN) Assistant Prof.Dr. Akeel Noori Abdul Hameed (University of Sharjah, UAE) Assistant Prof.Dr. Rohit Srivastava (Indian Institute of Technology Bombay, INDIA) Madam Wan Mariah Wan Harun (Universiti Sains Malaysia, MALAYSIA ) Dr. David Kuria (Kimathi University College of Technology, KENYA ) Dr. Mazran bin Ismail (Universiti Sains Malaysia, MALAYSIA ) Dr. Salahaddin Yasin Baper (Salahaddin University - Hawler, IRAQ ) Dr. Foong Swee Yeok (Universiti Sains Malaysia, MALAYSIA)

2014 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies Volume 5 Issue 4 (October, 2014) http://TuEngr.com

ISSN 2228-9860 eISSN 1906-9642

FEATURE PEER-REVIEWED ARTICLES

 Differential Effects of Sucrose and Plant Growth

Regulator on Shoot Multiplication and Bulbil Formation in Oxalis Versicolour In Vitro  Preparation of Activated Carbon from Sindora Siamensis

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235

Seed and Canarium Sublatum Guillaumin fruit for Methylene Blue Adsorption  Use of Filter Media Made from Vetiver Grass Root Ash for

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Water Treatment  A Review of Resource-Constrained Project Scheduling

253

Problems (RCPSP) Approaches and Solutions  Comparison between Analytical Results and Response of

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the Laboratory-Scaled Truss Bridges under the Moving Car Load Contacts & Offices: Associate Professor Dr. Ahmad Sanusi Hassan (Editor-in-Chief), School of Housing, Building and Planning, UNIVERSITI SAINS MALAYSIA, 11800 Minden, Penang, MALAYSIA. Tel: +60-4-653-2835 Fax: +60-4-657 6523, Sanusi@usm.my Associate Professor Dr. Boonsap Witchayangkoon (Executive Editor), Faculty of Engineering, THAMMASAT UNIVERSITY, Klong-Luang, Pathumtani, 12120, THAILAND. Tel: +66-2-5643005 Ext 3101. Fax: +66-2-5643022 Editor@TuEngr.com Postal Paid in Malaysia.

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2014 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

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Differential Effects of Sucrose and Plant Growth Regulator on Shoot Multiplication and Bulbil Formation in Oxalis Versicolour In Vitro a

a

Nattapong Chanchula , Tassanai Jaruwattanaphan , and Anchalee Jala

b*

a

Department of Horticulture, Faculty of Agriculture, Kasetsart University, Bangkhen, Bangkok, THAILAND, 10220, THAILAND b Department of Biotechnology, Faculty of Science and Technology, Thammasat University, Rangsit Campus, Pathumthani, THAILAND, 12121, THAILAND ARTICLEINFO

Article history: Received 24 March 2014 Received in revised form 04 June 2014 Accepted 16 June 2014 Available online 19 June 2014 Keywords: Bicolor Flower; Bulb; Tissue culture; NAA; BA.

A B S T RA C T

Explants from young leaves and stem nodes of Oxalis versicolour were used and cultured on MS medium supplemented with different concentration of 2,4-D. The best result showed that cluster of callus were formed and proliferated around the base of explants on MS medium supplemented with 0.1 mg/l 2,4-D. Callus transferred to MS medium supplemented with various concentrations of NAA and BA. After nine weeks, callus regenerated to be new shoots. The highest average length of stolon was from MS medium supplemented with 0.1mg/l NAA and 0.1 mg/l BA and number of plantlets was from MS medium supplemented with 4.0 mg/l NAA and 5.0 mg/l BA. Plantlets were cultured on MS medium supplemented with different concentrations of sucrose for ten weeks. It was found that all parameters: number of plantlets, bulbil sized, length of stolon, and number of nodes were significant difference (p≤0.05). Number of flowers and sized of flowers found only in MS medium supplemented with 9–10 % of sucrose.

2014 INT TRANS J ENG MANAG SCI TECH.

1. Introduction Bicolored Oxalis Versicolor (Candy Cane Sorrel) is a unique bulb with really spectacular *Corresponding author (Anchalee Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450, E-mail addresses: anchaleejala@yahoo.com.

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flowers. Oxalis is belonging to Oxalidaceae. Their origin is in Americas and South Africa.

The toxic principle is soluble oxalate. This oxalis is very beautiful in full bloom. They are even more stunning when they have not quite opened up completely and display a striking red and white striped pattern. They can be planted in the garden or in a planter on the patio or window-box. The foliage is three very narrow petals on each leaf and is not much like the clover we are used to seeing. The bulbs are very tiny, about the size of a fingernail. The thin plants will spring up in no time at all and are to produce many dainty 1/2" flowers and watering once the soil has become dry, however, do not soak.

This plant propagated by growing bulb. Plant tissue culture is one choice that can micropropagated and increase about 10000 plants within 2 months. Direct micropropagation system through enhanced young leaves or axillary bud development and organogenesis has been reported for different species of gladiolus (Jala, 2013). Various results have also been reported for the role of cytokinins in plant regeneration from callus initiated from different organs of Gladiolus such as young leaves and cormel slices (Kamo 1994). Since the callus initiation and regeneration depend variously on cultivated varieties, explants and growth regulators used in culture media (Kamo 1994, 1995), the present investigation was undertaken to determine the proper concentrations of growth regulators for callus initiation and regeneration of a locally cultivated, taking young leaves as explant.

2. Materials and Methods 2.1 Culture Establishment and Growth Oxalis plants were grown in green house. Young leaf and stem nodes were used as explants. Explants were surface sterilized by soaking with 70% alcohol for 10 sec, followed by 10%(v/v) Clorox (NaOCl) containing 15 drops/l Tween 20 for 10 min, 5% (v/v) Clorox for 15 min and washed with sterilized distilled water 3 times for 5 min each, to remove the Clorox. The ends of the explants were cut off in both sides and cultured on MS (Murashige and Skoog, 1962). After 2 weeks, cleaned cultures were transferred to MS medium supplemented with different combination of 2,4-D 2% sucrose, 0.25% gelrite at pH 5.7 and autoclaving at 121o C for 20 min. The cultures were maintained at 25 ¹ 2° C under a 16-hour photoperiod with illumination provided by cool fluorescent lamps at an intensity of 60 ¾molm-2 sec-1 (TLD 36 w/853350 lm Phillips, Thailand). These cultures were maintained in a

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proliferating state by subculturing every 3 weeks into the same medium 4 times.

2.2 Multiplication Callus were cultured on MS medium supplemented with combination of (0.1, 1, 2, 3, 4 mg/l) NAA and (0.1, 1, 2, 3, 4, 5 mg/l) BA and various concentrations of ( 2,3,4,5,6,7,8,9,and 10 %) sucrose. These treatments were subcultured every 3 weeks for 4 times. Shoot proliferation, bulb formation and their growth are displayed in Table 2 and 3.

3. Statistical Analysis Experiments were set up in Completely Randomized Design (CRD). Each treatment consisted of 20 replicates for the first second and third experiment. The test of statistical significance was done by applying DMRT at 1% and 5% confidence level using SAS statistical software.

Table 1: Effect of 2,4-D on sized and color of callus after cultured for 8 weeks.

2,4-D conc. (mg/l) Sized of callus Color of callus 0.1 4.60 ± 0.45a Light green 1 1.64 ± 0.39b Light green 2 1.29 ± 0.32b Light green 3 1.76 ± 0.38b cream 5 1.68 ± 0.23b Cream F-test ** % C.V. 16.68 ** Mean within the same column followed by the same alphabet were not significant difference using DMRT, p≤0.05.

1cm

A

1cm

B

Figure 1: Callus induction on MS medium supplemented with 2,4–D A - 0.1 mg/l, B - 5.0 mg/l.

4. Results After 2 weeks, explants from the disinfestation process resulted in a survival rate about 75 %, These explants were cultured on MS medium supplemented with different concentration of 2,4-D. After cultured for 4 weeks, callus were formed and proliferated around the base of explants which attached to the medium. The sized of callus were *Corresponding author (Anchalee Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450, E-mail addresses: anchaleejala@yahoo.com.

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significant differences (p≤0.05). Cluster of callus were formed with different sized and depended on concentration of 2,4-D, as showed on Table 1 and Figure 1.

4.1 Result of Multiplication Callus transferred to MS medium supplemented with various concentrations of NAA and BA. After cultured for 4 weeks, it was found that callus regenerated to be new shoots.

Average length of stolon and number of plantlets were depended on concentration of NAA and BA in the MS medium and were highly significant difference (p≤0.01) (Table 2). The result showed that MS medium supplemented with 0.4 mg/l NAA and 0.5 mg/l BA gave the highest number of plantlets (71.6 plantlets). MS medium supplements with 0.1 mg/l NAA and 0.1 mg/l BA gave the longest length of stolon (11.10cm).

Table 2: Effect of NAA and BA concentration on inducing length of stolon and number of plantlets after cultured for 9 weeks.

Plant growth regulator NAA (mg/l) BA(mg/l) 0 0 0.1 0.1 0.5 0.5 1.0 1.0 2.0 2.0 4.0 F-test % C.V.

4.0 5.0

length of stolon (cm)

Number of plantlets

13.30a ± 4.62 11.10a ± 2.80 7.40b ± 2.73 4.60c ± 1.12 5.10c ± 1.77 4.15c ± 0.70 1.49d ± 0.26 0.47d ± 0.13 0.61d ± 0.08 0.50d ± 0.11 0.46d ± 0.11 ** 43.57

1.10a ± 0.31 4.10b ± 2.55 3.00b ± 2.10 18.20b ± 4.90 20.40c ± 10.30 26.40c ± 13.80 24.50c ± 12.34 46.20c ± 8.05 44.50d ± 7.61 49.20d ± 9.64 71.60d ± 5.12 ** 28.91

**Mean within the same column followed by the same alphabet were not significant difference using DMRT, p≤0.01.

Plantlets were cultured on MS medium supplemented with different concentrations of sucrose for 10 weeks. It was found that all parameters: number of plantlets, bulbil sized, length of stolon, number of nodes, number of flowers and sized of flowers were significant difference ( p≤0.05) (Table 3). Two and Three percentage of Sucrose gave the best result that gave the highest number of plantlets (10.5 – 10.75 plantlets, respectively) (Figure 5), number of nodes (10.75 – 10.50 nodes per, respectively) and length of stolon (29.25-28.5 mm, respectively). The biggest sized of bulbil were formed at 9–10 percentage of sucrose (18.65– 19.05 mm, respectively) (Figure 4). At the ninth week, plant were flowering in the MS medium only contained 9-10 percentage of sucrose. Ten percentage of sucrose gave the highest number of flowers and the biggest sized of flower, also (Figure 3).

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Table 3: Effect of sucrose concentration on number of plantlets, bulbil size, length of stolon , number of nodes, number of flower, and size of flowers after culture for 10 weeks.

Sucrose (%) 2 3 4 5 6 7 8 9 10 F-test % C.V.

No. of plantlets

10.75a ± 1.50 10.50ab ± 1.73 5.50d ± 0.58 9.50abc ± 4.20 6.50cd ± 1.29 7.75bcd ± 1.26 8.75bcd ± 0.65 9.25abcd ±0.96 12.75a ± 0.65 * 19.96

Size of Bulbil (mm) 9.72e ± 0.92 9.92e ± 0.82 11.22de ± 1.21 13.05cd ± 0.31 16.07b ± 0.70 14.80bc ± 1.98 15.32bc ± 0.52 18.65a ± 0.47 19.05a ± 1.97 * 8.09

Length of stolon(mm) 29.25a ± 0.96 28.50a ± 0.58 26.50a ± 1.29 19.25b ± 0.96 19.50b ± 1.91 19.25b ± 2.22 20.25b ± 1.70 18.50b ± 1.00 13.25b ± 2.36 * 7.23

Number of nodes 10.75a ± 0.96 10.50a ± 0.58 8.25b ± 0.96 7.75b ± 1.89 8.00b ± 0.81 7.50b ± 0.58 8.25b ± 0.50 8.00b ± 0.82 7.75b ± 1.25 * 11.88

Number of flowers 0.50b ± 0.57 4.75a ± 0.95 * 30.10

Size of flower (cm) 2.80b ± 3.23 6.07a ± 1.00 * 60.29

Figure 3: Characteristic of Oxalis versicolour flower (the end of arrow) which cultured in MS medium supplemented with 10 % sucrose.

Figure 4: Characteristics of Oxalis versicolour bulbil cultured in MS medium supplemented with various concentrations of sucrose. A 2%sucrose, B. 3% sucrose, C.4%sucrose, D.5% sucrose , E. 6%sucrose, F.7%sucrose, G. 8%sucrose, H.9% sucrose, I.10% sucrose. (bar = 1cm) *Corresponding author (Anchalee Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450, E-mail addresses: anchaleejala@yahoo.com.

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B

A 1cm

1cm

Figure 5: Various Characteristics of Oxalis versicolour after cultured on MS medium with various concentration of sucrose. A - plantlets which cultured in MS medium with free sucrose, B- Plantlet in MS medium with 10% sucrose.

5. Discussion Since the callus initiation and regeneration was depend variously on cultivated varieties, explants and growth regulators used in culture media (Kamo 1994, 1995). The result showed that callus transferred to MS medium supplemented with various concentrations of NAA and BA formed different characters. After cultured for 4 weeks, callus regeneratedto be new shoots and the highest number of plantlets was in MS medium and supplemented with 0.4 mg/l NAA and 0.5mg/l BA, the longest Average length of stolon and number of plantlets were from MS medium supplemented with 0.1 mg/l NAA and 0.1 mg/l BA. This result was the same as Jala and Wassamon (2012) did in Gynostemma pentaphyllum, Gladiolus (Jala, 2013), and Globba sp. ( Jala et al.,2013).

The result in this experiment showed that when cultured explants of Oxalis versicolour on MS medium supplemented with different concentration of (2, 3, 4, 5, 6, 7, 8, 9, 10 %) sucrose, the result showed that all parameters were significant difference (p≤0.05). The stimulation on bulbil sized, stolon length, number of node as Murashige and Skoog(1962) described their medium. Sucrose is the most common carbon source as well as anosmotic agent for plant tissue and organ culture. Sucrose also supports the maintenance of osmotic potential and the conservation of water in cells. However, high sucrose concentration in the media restricts the photosynthetic efficiency of cultured plants by reducing the levels of chlorophyll, key enzymes for photosynthesis and epicuticular waxes promoting the formation of structurally and physiologically abnormal stomata (Hazarika, 2006). On the other hand, earlier studies have shown that plantlets growing under tissue culture conditions do not fix enough CO2 to sustain growth in the absence of sucrose, which is mainly due to limited CO2 inside the vessel (Gautheret, 1955). Media with 3% sucrose have been the staple since Murashige and Skoog (1962) described their MS medium. Indeed, sucrose concentrations above 2.5 % repress proliferation of callus of various plants (Malamug et al., 1991). Low concentrations of sucrose favor the initiation of numerous shoots in tobacco callus and

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depress the growth of callus (Barg et al., 1977). The effect of high concentration of sucrose are especially when conditions in vitro are in adequate for significant photosynthesis (Muller et al., 2011). The result showed that plantlets in the bottle were flowering by increasing concentrations of sucrose (Table 3). This result was the same as Aloni et al., (1996,1997) mentioned that light perceived by the plant increased the availability of sucrose to the flowers due to increase photosynthesis and translocation. Sucrose had taken up but the sink organ is metabolized, and Zrenner et al.,(1995) studied and indicated that sucrose synthase is a regulatory enzyme that controls sucrose cleavage and starch biosynthesis in sink tissue.

6. Conclusion Young leaves and stem nodes of Oxalis versicolour were used as explants and cultured on MS medium supplemented with different concentration of 2,4-D. It was found that callus were proliferated at the base of explants and 0.1 mg/l 2,4-D gave the biggest sized of callus(4.6 mm) and their color was light green. Callus transferred to MS medium supplemented with various concentrations of NAA and BA. Callus

regenerated to be new

shoots and maximum shoots was formed in MS medium supplemented with 4.0 mg/l NAA and 5.0 mg/l BA.

Plantlets were cultured on MS medium supplemented with different

concentrations of sucrose (2, 3, 4, 5, 6, 7, 8, 9, 10 %) for 10 weeks. It was found that 2and 3% sucrose gave the best result in number of plantlets, bulbil sized, length of stolon, number of nodes. Only nine and ten percentages of sucrose gave flowers. Ten percentages of sucrose gave the highest number of flowers and the biggest sized of flowers.

7. References Aloni B, L. Karni, Z. aidman, A.A. Schaffer. 1996. Changes of carbohydrates in pepper (Capsicum annuum L.) flowers in relation to their abscission under different shading regimes. Annals of Botany 78: 163-168.

Aloni, B., Z. Aloni, L. KarniI, Z., and A. A. Schaffer. 1997. Relationship between Sucrose Supply, Sucrose-cleaving Enzymes and Flower Abortion in Pepper. Annals of Botany 79: 601 – 605.

Altman, A., R. Goren. 1971. Promotion of callus formation by abscisic acid in citrus bud cultures. Plant Physiol 47: 844-846.

Arigita L, M.J. Cañal, T.R. Sánchez, A. González . 2010. CO2-enriched microenvironment affects sucrose and macronutrients absorption and promotes autotrophy in the in vitro culture of kiwi (Actinidia deliciosa Chev. Liang and Ferguson). In Vitro Cell Dev-Pl 46: 312–322.

Barg, R. and N. Umiel. 1977. Effects of sugar concentrations on growth, greening and shoot formation in callus cultures from four genetic lines of tobacco. Z Pflanzenphysiol 81: 161166. *Corresponding author (Anchalee Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450, E-mail addresses: anchaleejala@yahoo.com.

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Dragan, V. and B.S. Vinterhalte. 1999. Hormone-Like Effects of Sucrose in Plant in vitro Cultures. Phyton (Austria) Special issue: Plant Physiology".v39: 57-60.

Hazarika BN (2006) Morpho-physiological disorders in in vitro culture of plants. Sci Hort 108: 105–120.

Jala, A. and Wassamon Patchpoonporn. 2012. Effect of BA and NAA and 2,4-D on Micropropagation of Jiaogulan(Gynostemma pentaphyllum Makino). International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

3(4):363-370.

Jala, A. 2013 The Effect of the 2,4-Dichlorophenoxy Acetic Acid, Benzyl Adenine and Paclobutrazol, on Vegetative Tissue-Derived Somatic Embryogenenesis in Turmeric (Curcuma var. Chatti). International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. 4(2):105-111.

Jala, A., Nattapong Chanchula and Thunya Taychasinpitak. 2013. Multiplication New Shoots from Embryo Culture on Globba spp. ). International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. 4(3):207-214.

Kamo K. 1994. Effect of phytohormones on plant regeneration from callus of Gladiolus cultivar “Jenny Lee”. In Vitro Cell. Dev. Biol. 30P : 26 - 31.

Kamo K. 1995. A cultivar comparison of plant regeneration from suspension cells, callus, and cormel slices of Gladiolus. In vitro Cell. Dev. Biol. 31P : 113 - 115.

Malamug J.J.F., H. Inden and T. Asahira. 1991. Plantlet regeneration and propagation from ginger callus. Scientia Hort. 48: 89–97.

Muller B, Pantin F, Génard M, Turc O, Freixes S, et al. (2011) Water deficits uncouple growth from photosynthesis, increase C content, and modify the relationships between C and growth in sink organs. J . Bot. 62: 1715–1729.

Murashige, T . and F.A. Skoog. 1962. A revised medium for rapid growth and bioassays with tobacco tissue culture. Physiol. Plant. 15 : 473 - 492..

Gautheret R.J.1955. The nutrition of plant tissue cultures. Annu Rev Plant Phys. 6: 433–484.

Zrenner R, Salanoubat M, Willmitzer L, Sonnewald U. 1995. Evidence of the crucial role of sucrose synthase for sink strength using transgenic potato plants. Plant Journal 7: 97-108.

Nattapong CHANCHULA is a PhD candidate in Department of Horticulture, Faculty of Agriculture, Kasetsart University, Bangkhen, Bangkok, THAILAND. His main research is in Plant cell technology and floriculture crop improvement.

Dr. Tassanai JARUWATTANAPHAN obtained her PhD in Biology (Plant Systematic and Evolution) from Chiba University, Japan. He is working at Department of Horticulture, Faculty of Agriculture, Kasetsart University, Bangkhen, Bangkok, THAILAND. His research interests have encompassed plant systematics, plant molecular phylogenetic, plant cytogenetic, and biodiversity of horticulture crops.

Dr. Anchalee JALA is an Associate Professor in Department of Biotechnology, Faculty of Science and Technology, Thammasat University, Rangsit Campus, Pathumtani , THAILAND. Her teaching is in the areas of botany and plant tissue culture. She is also very active in plant tissue culture research.

Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines in the journal’s website.

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Nattapong Chanchula, Tassanai Jaruwattanaphan, and Anchalee Jala

2014 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://TuEngr.com

Preparation of Activated Carbon from Sindora Siamensis Seed and Canarium Sublatum Guillaumin fruit for Methylene Blue Adsorption Samarn Srisa-ard a* a

Department of Chemistry, Faculty of Science and Technology, Rajabhat Maha Sarakham University, THAILAND

ARTICLEINFO

Article history: Received 02 June 2014 Received in revised form 20 June 2014 Accepted 23 June 2014 Available online 24 June 2014 Keywords: scanning electron microscope; Langmuir isotherm model; Adsorption isotherm; Chemical activation; Iodine number; Zinc chloride; MB; SEM.

A B S T RA C T

Activated carbons produced from Sindora Siamensis (SSAC) seed and Canarium Sublatum Guillaumin (CSGAC) fruit were prepared by chemical activation with zinc chloride, their characteristics and their methylene blue (MB) adsorptions were investigated. The effects of zinc chloride concentrations and activation temperatures were examined. The surface chemical characteristics of activated carbons were determined by scanning electron microscope (SEM).

Adsorption capacity was demonstrated with iodine numbers. The Langmuir and Freundlich equilibrium isotherm theories were applied to describe MB adsorption. The equilibrium adsorption results were complied with Langmuir isotherm model and its maximum monolayer adsorption capacity for SSAC and CSGAC are 672.6 and 487.6 mg/g for MB adsorption. The value of RL was found to be below 1.0, indicating that the resultant activated carbon was favorable for MB adsorption.

These results indicate that SSAC and CSGAC shells could be utilized as a renewable resource to develop activated carbon which is a potential adsorbent for MB. 2014 INT TRANS J ENG MANAG SCI TECH.

1. Introduction Activated carbon is the most commonly use and most effective adsorbent because of its high adsorptive capacity (Chen et al., 2003; Daifullah et al., 2007). Therefore, it has been widely used as adsorbent (Hung et al., 2005) and in catalysis (Lee et al., 2006) or separation *Corresponding author (S. Srisa-ard). Tel: +66-43-754246. E-mail: samarm.rmu@gmail.com.

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processes (Rodriguez-Reinoso et al., 2002) such as purification of drinking water, treatment of exhaust gas and waste water. However, its application fields are restricted due to its high cost. Presently, the use of low cost plants and agricultural wastes is considered promising adsorbents for adsorption applications. In recent years, a lot of research has been reported on activated carbons from plants or agricultural wastes, such as olive mill waste (Abdelkreem, 2013), seaweed (Rathinam et al., 2011), cherry stones (Jaramillo et al., 2009), fluted pumpkin seed shell (Verla et al., 2012), fluted pumpkin stem waste (Ekpete et al., 2011), rice straw (Gao et al., 2011), cattail (Shi et al., 2011), coconut husk (Tan et al., 2008), coconut shells (Yang et al., 2010), tobacco residues (Kilic et al., 2011), sugar cane bagasse, and sunflower seed hull (Liu et al., 2010), etc.

The Sindora Siamensis (SS) and Canarium Sublatum Guillaumin (CSG) are a large evergreen tree found in open semi–deciduous forests in Cambodia, Laos, Malaysia, Thailand, and Vietnam. The diagnostic characters of SS are deciduous or evergreen tree, bark smooth to slightly fissure. Leave compounds rachis swollen at the base. Flower in panicle is yellow– green. Fruit an ovoid–round spiny, flattened pod often with blobs of white resin (Sam et al., 2004). The CSG is 20–35 m tall. Branchlets are brown tomentose when young, glabrescent, lenticellate, with conspicuous leaf scars.

The objective of this study is to produce activated carbon from a Sindora Siamensis (SSAC) seed and Canarium Sublatum Guillaumin (CSGAC) fruit by chemical activation using ZnCl2 and adsorption capacity of activated carbons are also investigated. ZnCl2, as an important chemical activating agent, has been widely used in the process of activated carbon preparation (Horng et al., 2010; Rathinam et al., 2011; Gao et al., 2013). It is an efficient dehydration reagent that promotes the decomposition of carbonaceous material, induces the charging and aromatization of the carbon, restricts the formation of tar and increases the carbon yield. Thus to our best knowledge, however, have not yet been reported the activation of SS seed and CSG fruit with ZnCl2 chemical activation. The major novelty of this work is the production of activated carbons from ZnCl2 impregnated SS seed and CSG fruit samples by chemical activation technique in various SS/ZnCl2 and CSG/ZnCl2 ratios and a temperature range of 500–700°C. The study covers the effect of operating parameters such as temperature of activation and impregnation ratio on the product quality. The product quality is characterized based on the iodine number and MB adsorption isotherm.

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2. Experiment 2.1 Materials and Preparation of Activated Carbon Sindora Siamensis seed and Canarium Sublatum Guillaumin fruit were collected from Maha Sarakham province of Thailand. The precursor was crushed and sieved in order to get a standardized particle dimension. Five to forty grams of ZnCl2 were dissolved in 200 mL of distilled water, and then 20 g of raw ash was mixed with the ZnCl2 solution and stirred at approximately 85°C for 2 hr. The mixtures were dehydrated in an oven at 110°C for about 24 hr. The resulting activated carbons were then chemically activated at 500, 600 and 700°C for 3 hr in atmosphere. The activated samples were washed in 3 M hydrochloric solution by heating at around 90°C for 30 min to remove the zinc compounds. Then, they were washed several times with warm distilled water, and finally with cold distilled water. The washed samples were dried at 110°C for 24 hr to prepare the activated carbons. Each sample was stored in sealed bottle vial.

2.2 Sample Characterization The iodine number is a technique employed to determine the adsorption capacity of activated carbons. The iodine number indicates the porosity of the activated carbon and it is defined as the amount of iodine adsorbed by 1 g of carbon at the mg level. Iodine number can be used as an approximation for surface area and microporosity of active carbons with good precision. The iodine adsorption was determined using the sodium thiosulfate volumetric method (ASTM, 2006). It is a measure of activity level (higher number indicates higher degree of activation), often reported in mg/g. It is a measure of the micropore content of the activated carbon by adsorption of iodine from solution (Elliott et al., 1989). The procedure of the iodine number determination is as follows: activated carbons were weighed out into conical flasks (sample weight ranged between 100 and 400 mg). Ten millilitres of 5% (in weight) hydrochloric acid solution were added to each flask and then mixed until the carbon became wet. The mixtures were then boiled for 30 s and finally cooled. One hundred millilitres of 0.05 M standard iodine solution were added to each flask. The contents were vigorously shaken for 30 s and then immediately filtered. A 50 ml aliquot of each filtrate was titrated by a standardized 0.1 M sodium thiosulfate solution. The morphologies of SSAC and CSGAC were examined by a LEO/1450 scanning electron microscope (SEM). The samples were dried at 105°C for 2 hr and coated with a thin gold film to give electrical conduction on the carbon external surface. *Corresponding author (S. Srisa-ard). Tel: +66-43-754246. E-mail: samarm.rmu@gmail.com.

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2.3 Adsorption Isotherm Methylene blue kinetic and isotherm adsorption experiments were carried out to evaluate the adsorption performance. The equilibrium adsorption research was completed by adding a fixed amount of activated carbon into 25 ml different initial concentrations of MB. The kinetic adsorption studies were performed by adding 0.2 g activated carbon into 200 ml different initial concentrations of MB. The aqueous samples were taken at pre–set time intervals and their concentrations were determined. Concentration determination of all the samples was filtered before they were measured by UV spectrophotometer (Perkin Elmer/lamda 12) at the maximum absorption wavelength of 664 nm.

The Langmuir and Freundlich equilibrium isotherm theories were applied to describe the MB adsorption. Langmuir equation can be represented by the following Eq. (1) (Singh et al., 2008; Fierro et al., 2008): C Ce 1 = + e qe K L Qmax Qmax

(1)

where Ce (mg/L) is the equilibrium concentration of MB in solution, qe (mg/g) is the amount of MB adsorbed at the equilibrium time, Qmax (mg/g) is the maximum capacity, and KL (L/mg) is the Langmuir constant related to the free energy or net enthalpy of adsorption.

Freundlich model is an empirical equation assuming heterogeneous adsorptive energies on the adsorbent surface, which can be written as Eq. (2) (Valente et al., 2011): 1

log qe = log KF + log Ce đ?‘›

(2)

where KF is the Freundlich constant and taken as an indicator of adsorption capacity, in (L/mg) and 1/n is an empirical constant related to the magnitude of adsorption driving force (Fan et al., 2008).

3. Results and Discussion 3.1 Production Yield The production yields (%yield) of SSAC and CSGAC are listed in Table 1, the yield values of SSAC were 85.26, 75.26, and 72.86% for activated at 500, 600, and 700°C, respectively. The yield values of CSGAC were 84.94, 77.46, and 73.02% for activated at 500, 238

Samarn Srisa-ard

600, and 700°C, respectively. It is obviously seen that the yield decreases with increasing temperature. As generally recognized, O and H atoms could be evolved into CO, CO2, H2O, CH4, aldehydes or tar in the carbonization process of lignocellulosic materials. Moreover, the yield is slightly difference in various ZnCl2/SS or ZnCl2/CSG ratios.

Hence, the yield

depends on the amount of carbon removed by combining with O and H atoms. However, ZnCl2 would selectively stripe H and O away from SSAC and CSGAC as H2O and H2 rather than CO, CO2 or hydrocarbons (Caturla et al., 1991).

Table 1: Yield (%) and capacity of the prepared carbons in the adsorption of iodine numbers.

Iodine number (mg/g) Yield (%) Temperature (°C)/Types of activated carbon 500 600 700

SSAC 85.26 75.26 72.86

CSGAC 84.94 77.50 73.02

SSAC 148.23 147.40 132.24

CSGAC 146.33 142.09 118.58

3.2 Iodine Number The capacities of the prepared carbons in the adsorption of iodine with SSAC and CSGAC are list in Table 1. It is found that the iodine numbers for iodine adsorbed on SSAC were 148.23, 147.40, and 132.24 mg/g for activated at 500, 600, and 700°C, respectively and iodine numbers for iodine adsorbed on CSGAC are 146.33, 142.09, and 118.58 mg/g for activated at 500, 600, and 700°C, respectively. This data indicate that the activated carbon prepared from SS is more suitable for iodine adsorption than CSGAC, which the preparation of activated carbon at 500°C is the highest porosity in both SSAC and CSGAC. The tested activated carbons adsorb significant amounts of iodine which are strongly related to the degree of activation.

3.3 Textural Characterization by SEM The morphology in terms of amorphous, pore size and pore distribution of SSAC and CSGAC was investigated. The SEM photographs of raw ash, SSAC and CSGAC for activation at 500–700°C are shown in Figures 1 and 2. The result showed that the highly amorphous structure could be prepared from the SS seed and CSG fruit. The fibrillary structures of all SSAC and CSGAC reveal that the pores are not cross–linked. Figure 1a–1d

*Corresponding author (S. Srisa-ard). Tel: +66-43-754246. E-mail: samarm.rmu@gmail.com.

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Figure 1: SEM micrographs for (a) raw ash, (b) SSAC for activated at 500°C, (c) SSAC for activated at 600°C and (d) SSAC for activated at 700°C

Figure 2: SEM micrographs for (a) raw ash, (b) CSGAC for activated at 500°C, (c) CSGAC for activated at 600°C and (d) CSGAC for activated at 700°C shows that the surface of SSAC is relatively organized with large pores. Whereas, Figure 2a– 2c indicates the external surface of CSGAC is similarly to SSAC but for CSGAC activated at 700°C (see Figure 2d) giving small pore volumes. It has been suggested that the cavities resulted from the evaporation of ZnCl2 during carbonization, leaving the space previously occupied by the ZnCl2 (Hu et al., 2001).

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3.4 Methylene Blue Adsorption Capacity The Langmuir and Freundlich equilibrium isotherm theories were applied to describe the MB adsorption. Langmuir equation can be represented by the following Eq. (1). Freundlich model is an empirical equation assuming heterogeneous adsorptive energies on the adsorbent surface, which can be written as Eq. (2). The essential features of Langmuir isotherm can be expressed by the term of separation factor or equilibrium parameter (RL), which is defined as RL = 1/(1 + KLC0) (where C0 is the initial concentration of adsorbate and KL is its Langmuir constant). The RL value refers to the nature of adsorption as irreversible (RL = 0), favorable (0 < RL < 1), linear (RL = 1) or unfavorable (RL > 1) (Bouhamed et al., 2012; Liu et al., 2010).

The Langmuir and Freundlich calculated parameters are given in Tables 2 and 3. The qe values of SSAC for activated at 500, 600, and 700째C were 441.6, 672.6, and 462.6 mg/g, respectively. And the qe values of CSGAC for activated at 500, 600, and 700째C were 429.3, 487.6, and 203.8 mg/g, respectively. This data indicate that the activated carbon prepared from SS is more suitable for iodine adsorption than CSG, which the preparation of activated carbon at 600째C is the highest porosity in both SSAC and CSGAC. The values of RL are found to be below 1 for MB adsorption, indicating that the resultant activated carbons were favorable for MB adsorption. Average correlation coefficients R2 for SSAC and CSGAC were 0.95 and 0.89, respectively, indicating that the two models fits to the experimental data.

The Freundlich isotherm model shows a better description for MB adsorption data with average R2 for SSAC and CSGAC in the range of 0.95 and 0.87, respectively. Freundlich parameters KF and 1/n were obtained from the slope and intercept, respectively, which demonstrate whether the adsorption is favorable or not. A high value of KF, is indicative of a high adsorption capacity (Salame et al., 2003). In short, 1/n is a measure of the surface heterogeneity, ranging between 0 and 1, as its value gets closer to zero, the surface become more heterogeneous (Ahmaruzzaman et al., 2005). Since all the value of 1/n is less than 1, it indicates a favorable adsorption (Mohanty et al., 2005).

The lower determination SSAC coefficients R2 of the Langmuir suggest that the isotherm data do not fit the Langmuir model. The higher determination SSAC coefficients R2 of the Freundlich equation suggest that the Freundlich equation can be used to fit the experimental adsorption data and evaluate the maximum dye adsorption capacities of the three adsorbents. Whereas, the determination CSGAC coefficients R2 of the Langmuir is equal *Corresponding author (S. Srisa-ard). Tel: +66-43-754246. E-mail: samarm.rmu@gmail.com.

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to Freundlich suggesting that the adsorption capacities not difference.

The results also

indicate that the adsorption of MB by the three adsorbents takes place in a monolayer adsorption manner.

Table 2: Parameters of Langmuir adsorption isotherm models for MB adsorbed by the different temperatures.

Temperature SSAC CSGAC Qe KL Qe KL (째C)/Types of RL R2 RL R2 (mg/g) (L/mg) activated carbon (mg/g) (L/mg) 500 441.6 0.075 0.382 0.9302 429.3 0.043 0.510 0.8942 600 672.6 0.078 0.373 0.9554 487.6 0.120 0.285 0.7909 700 462.6 0.091 0.340 0.9597 203.8 0.009 0.822 0.9997

Table 3: Parameters of Freundlich adsorption isotherm models for MB adsorbed by the different temperatures.

SSAC CSGAC Temperature (째C)/Types KF KF of activated carbon 1/n R2 1/n R2 (L/mg) (L/mg) 500 2.077 0.391 0.9798 5.941 0.348 0.8729 600 2.228 0.315 0.9583 5.906 0.409 0.3254 700 2.008 0.409 0.9247 3.862 0.658 0.9315

4. Conclusion Activated carbons were produced from Sindora Siamensis seed and Canarium Sublatum Guillaumin fruit by chemical activation with zinc chloride, their characteristics and their methylene blue adsorptions were investigated. The effects of zinc chloride concentrations and activation temperature were examined. The morphology, pore size, and pore distribution characteristics of activated carbons were determined by SEM method. Adsorption capacity was demonstrated with iodine numbers. The Langmuir and Freundlich equilibrium isotherm theories were applied to describe methylene blue adsorptions. The equilibrium adsorption results were complied with Langmuir isotherm model and its maximum monolayer adsorption capacity for SSAC and CSGAC are 672.6.0 and 487.6 mg/g for methylene blue adsorption.

The value of RL was found to be below 1.0, indicating that the resultant activated carbon was favorable for phenol adsorption. These results indicate that SSAC and CSGAC shells could be utilized as a renewable resource to develop activated carbon which is a potential adsorbent for methylene blue.

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5. Acknowledgement The authors gratefully acknowledge the Department of Chemistry, Faculty of Science and Technology and Research and Development Institute, Rajabhat Maha Sarakham University for financial support of this research.

6. References Abdelkreem, M. (2013). Adsorption of phenol from industrial wastewater using olive mill waste. APCBEE Procedia. 5, 349–357.

Ahmaruzzaman, M. & Sharma, D.K. (2005). Adsorption of phenols from wastewater. Journal of Colloid and Interface Science. 287, 14–24.

ASTM, (2006). Standard Test Method for Determination of Iodine Number of Activated Carbon. ASTM Committee on Standards, ASTM D 4607–94; ASTM: Philadelphia, PA. Bouhamed, F., Elouear, Z., & Bouzid, J. (2012). Adsorptive removal of copper (II) from aqueous solutions on activated carbon prepared from Tunisian date stones: equilibrium, kinetics and thermodynamics. Journal of the Taiwan Institute of Chemical Engineers. 43, 741–749.

Caturla, F., Molina–Sabio, M. & Rodriguez–Reinoso, F. (1991). Preparation of activated carbon by chemical activation with ZnCl2. Carbon 29, 999–1007.

Chen, J.P., Yoon, J.T. & Yiacoumi, S. (2003). Effects of chemical and physical properties of influent on copper sorption onto activated carbon fixed–bed columns. Carbon. 41, 1635– 1644.

Daifullah, A.A.M., Yakout, S.M. & Elreefy, S.A. (2007). Adsorption of fluoride in aqueous solutions using KMnO4–modified activated carbon derived fromsteam pyrolysis of rice straw. Journal of Hazardous Materials. 147, 633–643.

Ekpete, O.A., Horseall, M.J.N.R. & Tarawou, T. (2011). Evaluation of Activated Carbon from Fluted Pumpkin Stem Waste for Phenol and Chlorophenol Adsorption in a Fixed– Bed Micro–Column. Journal of Applied Sciences and Environmental Management.

15(1), 141–146.

Elliott, C., Colby, T. & Iticks, H. (1989). Activated carbon obliterans alter aspiration of activated charcoal, Chest Journal. 96, 672–674.

Fan, H.J., Yang, H.S., Tsai, Y.S. & Furuya, E. (2008). Prediction of individual Freundlich isotherms from binary and ternary phenolic compounds mixtures. Chemosphere. 71, 886– 893.

Fierro, V., Torné–Fernández, V., Montané, D. & Celzard, A. (2008). Adsorption of phenol onto activated carbons having different textural and surface properties. Microporous Mesoporous Materials. 111, 276–284.

Gao, J.J., Qin, Y.B., Zhou, T., Cao, D.D., Xu, P., Hochstetter, D. & Wang, Y.F. (2013). *Corresponding author (S. Srisa-ard). Tel: +66-43-754246. E-mail: samarm.rmu@gmail.com.

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Adsorption of methylene blue onto activated carbon produced from tea (Camellia sinensis L.) seed shells: kinetics, equilibrium, and thermodynamics studies. Journal of Zhejiang University-SCIENCE B (Biomedicine & Biotechnology). 14(7), 650–658.

Gao, P., Liu, Z.H., Xue, G., Han, B. & Zhou, M.H. (2011). Preparation and characterization of activated carbon produced from rice straw by (NH4)2HPO4 activation. Bioresource Technology. 102, 3645–3648.

Horng, L.T. (2010). Development of mesoporous structure and high adsorption capacity of biomass-based activated carbon by phosphoric acid and zinc chloride activation.

Chemical Engineering Journal. 158, 129–142.

Hu, Z.H., Srinivasan, M.P. & Ni, Y.M. (2001). Novel activation process for preparing highly microporous and mesoporous activated carbons. Carbon. 39, 877–86.

Hung, Y.T., Lo, H.H., Wang, L.K., Taricska, J.R. & Li, K.H. (2005). Granular activated carbon adsorption, Handbook of Environmental Engineering. New Jersey; Humana Press.

Jaramillo, J., Gómez-Serrano, V. & Alvarez, P.M. (2009). Enhanced adsorption of metal ions onto functionalized granular activated carbons prepared from cherry stones. Journal of Hazardous Materials. 161(2–3), 670–676.

Kilic, M., Apaydin–Varol, E. & Pütün, A.E. (2011). Adsorptive removal of phenol from aqueous solutions on activated carbon prepared from tobacco residues: equilibrium, kinetics and thermodynamics. Journal of Hazardous Materials. 189(1–2), 397–403 Lee, J., Kim, J. & Hyeon, T. (2006). Recent progress in the synthesis of porous carbon materials. Advanced Material. 18, 2073–2094.

Liu, Q.S., Zheng, T., Wang, P., Jiang, J.P. & Li, N. (2010). Adsorption isotherm, kinetic andmechanism studies of some substituted phenols on activated carbon fibers. Chemical Engineering Journal. 157, 348–56.

Mohanty, K., Das, D. & Biswas, M,N. (2005). Adsorption of phenol from aqueous solutions using activated carbons prepared from Tectona grandis sawdust by ZnCl2 activation.

Chemical Engineering Journal. 115, 121–31.

Rathinam, A., Rao, J.R. & Nair, B.U. (2011). Adsorption of phenol onto activated carbon from seaweed: determination of the optimal experimental parameters using factorial design. Journal of the Taiwan Institute of Chemical Engineers. 42(6), 952–956 Rodriguez–Reinoso, F. (2002). Production and application of activated carbon, Handbook of Porous Solids, Weinheim; Wiley–VCH. Salame, I.I. & Bandosz, T.J. (2003). Role of surface chemistry in adsorption of phenol on activated carbons. Journal of Colloid and Interface Science. 264, 307–312.

Sam, H.V., Nanthavong, K. & Keßler, P.J.A. (2004). Trees of Laos and Vietnam. Blumea. 49, 201–349.

Shi, Q., Zhang, J., Zhang, C., Li, C., Zhang, B., Hu, W., Xu, J. & Zhao, R. (2010).

Preparation of activated carbon from cattail and its application for dyes removal. Journal of Environmental Sciences. 22(1), 91–97.

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Singh, K.P., Malik, A., Sinha, S. & Ojha, P. (2008). Liquid-phase adsorption of phenols using activated carbons derived from agricultural waste material. Journal of Hazardous Materials. 150, 626–641.

Tan, I.A.W., Ahmad, A.L. & Hameed, B.H. (2008). Preparation of activated carbon from coconut husk: Optimization study on removal of 2,4,6–trichlorophenol using response surface methodology. Journal of Hazardous Materials. 153, 709–717.

Valente Nabais, J.M., Laginhas, C.E.C., Carrott, P.J.M. & Ribeiro Carrott, M.M.L. (2011).

Production of activated carbons from almond shell. Fuel Process Technology. 92, 234– 240.

Verla, A.W., Horsfall, M., Verla, E.N. & Spiff, A.I. (2012). Carbon prepared from fluted pumpkin (Telfairia occidentalis Hook.F) Mesocarp by physical activation. International Journal of Chemical Science and Technology. 2(3), 224–230.

Yang, K., Peng, J., Srinivasakannan, C., Zhang, L., Xia, H. & Duan, X. (2010). Preparation of high surface area activated carbon from coconut shells using microwave heating.

Bioresource Technology. 101, 6163–6169.

Samarn Srisa-ard works at the Department of Chemistry at Rajabhat Maha Sarakham University. He received a B.Sc. in Chemistry from Khon Kaen University, THAILAND, and M.Sc. in Physical Chemistry from Chiangmai University, THAILAND. Mr. Srisa-ard is interested in applications of chemistry in everyday life.

Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines in the journal’s website.

*Corresponding author (S. Srisa-ard). Tel: +66-43-754246. E-mail: samarm.rmu@gmail.com.

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Use of Filter Media Made from Vetiver Grass Root Ash for Water Treatment Krittiya Lertpocasombut a* and Maruay Onsod a, b a

Department of Civil Engineering, Faculty of Engineering, Thammasat University Rangsit Campus, Pathumtani 12120, THAILAND

b

Quality System & Product Auditing Manager, Goodyear (Thailand) Public Co. Ltd.,

Pathumtani, 12120, THAILAND ARTICLEINFO

Article history: Received 18 June 2014 Received in revised form 06 July 2014 Accepted 09 July 2014 Available online 11 July 2014 Keywords: natural material; polyvinyl alcohol; pellet; turbidity;

A B S T RA C T

Vetiver grass and its usages have been widely reported in many researches. This research aimed to use the vetiver grass root ash (VGRA) to filter water to improve its quality. The investigation of the physical and chemical properties of the VGRA indicated the product from natural material ash could be used as a filter medium.

The distribution of particle size, the specific gravity, and the Iodine number of the VGRA were determined as well as the ratio of the VGRA to polyvinyl alcohol (PVA) as a binding polymer were analyzed. The results revealed that the best mold of the VGRA pellet as a filter media was a circular shape. The ratio of the VGRA to PVA was 7.33 to 1. This was consistent with the results of the linear shrinkage that were 13.35 Âą 2.10 which was the best form of the VGRA product to be used as a filter medium for water turbidity treatment.

2014 INT TRANS J ENG MANAG SCI TECH.

1. Introduction The water used must be safe and free from dangerous bacteria. It is necessary to have the water quality meet the required standard. The use of plants to improve water quality is one method that has received considerable attention. In general, natural materials are cheap and readily available in every country. Many researchers [1][2][3] have reported both viable or nonviable properties of vetiver grass and its usages. Vetiver grass is regarded as a miracle plant that is the subject of ongoing research.

This research focuses on studying the properties of vetiver grass root ash to produce the filter material suitable for improving water quality. The filter material was formed with polyvinyl alcohol at various *Corresponding author (Krittiya Lertpocasombut).

Tel: +66-2-5643005 x3109.

E-mail: lkrittiy@engr.tu.ac.th. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642.

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247

percentages for the purpose of studying the performance of the filter media produced in terms of the linear shrinkage, the porosity and the apparent density of the product. Then the product selected was used as a filter media for water turbidity treatment.

2. Materials and Methods 2.1 Materials and Chemicals The vetiver grass used was grown for at least 18 months with a length of root not less than 0.50 m. The polyvinyl alcohol (PVA) used was commercial grade with the molecular weight of 30,000 – 50,000. Water used for the experiment was deionized water.

2.2 Preparation of the Vetiver Grass Root Root of vetiver grass of Nakhon Sawan species was chosen due to its availability and abundance. It was washed thoroughly five times with clean water and then dried under the sun for at least 48 hours. The dried root was burned at 550 ºC for 15 to 30 minutes and ground for size reduction in a laboratory blender at a speed of 3,000 rpm for 15 minutes. At this stage, the properties of the vetiver grass root ash (VGRA) were determined for the particle size distribution following the ASTM D2862 Standard test method, the specific gravity of soil solid by water pycnometer following the standard ASTM 854, and the Iodine number following the standard ASTM D4607.

2.3 The VGRA Pellet The VGRA particles were sifted through a sieve of 50, 100 and 200 mesh sizes, divided into 3 sieve sizes and combined with PVA in ratios varying from 6 to 18 % wt. by wt. The slip behavior flow of the 3 sieve sizes of the VGRA with various percentages of PVA was determined. The linear shrinkage of the VGRA pellet formed was taken into account after drying at 125 ºC for 2 hours.

2.4 Experimental Setup for Water Turbidity Removal Figure 1 shows a schematic diagram of the experimental set up. It was comprised of 3-filter units, and a receiving vessel attached to available suction pump.

Figure 1: The schematic diagram of the experimental unit.

The raw water sample was taken from the natural river source at the intake of the water supply

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Krittiya Lertpocasombut and Maruay Onsod

plant located in Pathum Thani province in Thailand. The inlet and exit water turbidity of the filtration unit was measured by UV spectrophotometer. The pressure, the flow rate and the particles capture performances were measured under the experimental conditions.

3. Results and Discussions 3.1 The Physical and Chemical Properties of the VGRA After grinding, the composition of the ash of the vetiver grass root was determined by XRF following the Standard ASTM C114 and results are shown in Table 1.

Table 1 The chemical composition of the VGRA.

Chemical Composition Carbon Dioxide (CO2) Silicon Dioxide (SiO2) Aluminum Oxide (Al2O3) Iron Oxide (Fe2O3) Other

% Weight 52.67 44.10 1.03 2.11 0.10

% Standard Error 0.16 0.14 0.17 0.14 0.21

It was found that the vetiver grass root ash (VGRA) contained 44.10 % silicon dioxide which was similar to that of the results from AIT research group [3] on the use of vetiver grass ash to replace cement in which composition of silicon dioxide in the ash of the vetiver grass up to 57.48 % was reported. The silica content in the VGRA makes it feasible to further study of the use of roots of vetiver grass as a filter material when combined with a polymer such as polyvinyl alcohol (PVA), which was selected for this study.

The mean particle size is useful when determining the specific gravity of the substance.

Root of the vetiver grass after burning at 550 °C was crushed in a laboratory blender to a mean particle size of 0.068 ¹ 0.003 mm by sieve analysis. This size is similar to fine sand (0.05-0.25 mm).

The specific gravity of the VGRA by the ASTM standards for classification was found to be in the range of 2.26 to 2.60. It was comparable to the compounds like carbon (2.26), gypsum (2.3), clays (2.6), sand and silica (2.6), with a similar specific gravity.

Three sieve sizes (50, 100 and 200 mesh) of VGRA were evaluated for Iodine number according to the ASTM standards. This value was considered to examine the assumption that the VGRA has the ability to adsorb. The results showed that the Iodine number of the VGRA (36 - 46 g/kg) was comparable to the standard carbon N550 and N660. The VGRA of 200 *Corresponding author (Krittiya Lertpocasombut).

Tel: +66-2-5643005 x3109.

E-mail: lkrittiy@engr.tu.ac.th. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642.

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mesh sieve size, which was comprised mostly of the smallest particle size, gave the highest Iodine adsorption number.

3.2 The Ratio of the VGRA to PVA Pellets of VGRA were formed by casting. The VGRA of sieve sizes 50, 100 and 200 mesh were combined with the PVA to give concentrations of 6, 8, 12 and 18 % in order to measure the flow

Plastic Viscosity (mPas)

behavior and stability of the slip. The results are shown in Figure 2.

PVA 18 %

y = 1.0029x + 148.25 R² = 0.9286

PVA 12 % PVA 8 % PVA 6 %

y = 0.2436x + 115.27 R² = 0.9681 y = 0.0733x + 88.497 R² = 0.9924 y = 0.1189x + 52.61 R² = 0.9577

Mesh Number Figure 2: Plastic viscosity values of the VGRA slip at different ratios of PVA.

(%shringkage)

50 Mesh

y = -66.571x + 42.503 R² = 0.7555

100 Mesh 200 Mesh

y = -73.595x + 21.445 R² = 0.8594 y = -127.62x + 25.488 R² = 0.5476

Concentration of PVA (%)

Figure 3: The linear shrinkage of the VGRA of 3 sieve sizes at different ratios of PVA. R2 values ranging from 93 to 99 % were obtained for all ratios of the VGRA to PVA. The maximum R2 value was found with the mixture of 12 % PVA producing the optimum flow and

stability of the slip.

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Krittiya Lertpocasombut and Maruay Onsod

3.3 The Linear Shrinkage of the VGRA Pellet Hollow casting was used to test for the linear and volumetric shrinkages of the VGRA which had been combined with PVA in different ratios to form a pellet. The results are shown in Fig. 3.

The VGRA of 100 mesh sieve size showed the highest coefficient of determination with 86 % corresponding to the VGRA to PVA ratio of 7.33 to 1.

3.4 The Capability of the VGRA Filter Media A water volume of 0.25 L was filtered through the VGRA pellets produced from 50, 100 and 200 mesh sieve sizes with the PVA content of 12 % and a constant pressure of 2 bars.

With the filtration pressure constant, the flow rates of water filtered through the 3 sets of VGRA pellets declined as the mesh number of the VGRA pellets increased from 50, 100 and 200 mesh. The flow rates were 444, 309 and 151 L/m2 · h, respectively. On comparing these values with the flow rates of microfiltration membrane (10 – 20 L/m2 · h) [4], they were about 10 times higher. The flow rate decreased similarly when the water was filtered through the VGRA pellets under a constant pressure of 7 bars as well. However, all VGRA pellets produced from 50 mesh were cracked as were some of the 100 and 200 mesh pellets also.

The efficiency of particles capture was 67, 73 and 73 % and their residual values were 67, 55 and 54 ppm respectively under a pressure of 2 bars. No similar research on filter media has been found to make a comparison. One explanation of the effect of removal of coliform bacteria (as suspended solid) with modified homemade filter media could be that as the treatment time progressed, the adsorbent sites of the media had a tendency towards saturation [5].

3.5 The Effects of Turbidity on the VGRA Filter Media With the constant pressure of 2 bars and the water sample used from the natural river source, the effluent turbidity reached a steady value of 30 ± 1 NTU when flowing through the VGRA pellet of 100 mesh sieve size, whereas, the VGRA pellet of 50 mesh sieve size exhibited lower efficiency of removal turbidity (up to 50 NTU) as shown in Table 2.

Table 2 Turbidity of the water before and after passed through the VGRA pellet.

Turbidity (NTU) Inlet Exit

50 122 50

Mesh Number 100 122 31

200 122 30

From these results, removal efficiency of 75 % was obtained for inlet turbidity of 122 NTU and a flow rate of 309 L/m2 · h for filtration using the 100-mesh VGRA pellet with PVA 12 % as the filter media.

*Corresponding author (Krittiya Lertpocasombut).

Tel: +66-2-5643005 x3109.

E-mail: lkrittiy@engr.tu.ac.th. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642.

Online Available at http://tuengr.com/V05/0247.pdf.

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4. Conclusions The vetiver grass root ash (VGRA) containing up to 44.10 % of silicon oxide with a mean particle size of 0.068 mm was used as a filter material. Its specific gravity was in the range of 2.26 to 2.60, similar to silt and fine sand. The VGRA with 200 mesh sieve size showed acceptable results with the maximum Iodine number of adsorption, the flow behavior of slip in forming, and a suitable PVA ratio.

However, the linear shrinkage of the VGRA pellet formed with 100 mesh sieve size resulted in the best coefficient of determination compared to the VGRA pellet with 200 mesh and 50 mesh sieve sizes, respectively. The filtration experiment confirmed that the 100-mesh VGRA pellet formed with PVA content of 12 % was the optimal composition as a filter media under 2-bar pressure for water turbidity removal.

5. References [1]

Thiramongkol, V., & Baebprasert, B. The vetiver grass pot: Production and use.

Department of Science Service, Bangkok, Thailand, pp.350-352, www.prvn.rdpb.go.th/files/CP-4-6.pdf, [18 July 2012].

[2]

Ash, R., & Truong, P. (2004). The use of vetiver grass for sewage treatment. Sewage Management QEPA Conference, Cairns, Australia, April 5-7, 2004. www.vetiver.com/AUS_ekeshire01.pdf, [18 July 2012].

[3]

Nimityongskul, P. & Chomchalow, N. Utilization of vetiver grass as construction materials. AIT. www.prvn.rdpb.go.th/files/7-05.pdf, [18 July 2012].

[4]

Lertpocasombut, K. (2004). Hollow fiber microfiltration membrane in activated sludge and its chemical cleaning for water reuse. Proceedings of the International Symposium on the Development of Water Resource Management System in Mekong Watershed, Hanoi, Vietnam, December 3-4, 2004, 46-52.

[5]

Devi, R., Alemayehu, E., Singh, V., Kumar, A., & Mengistie, E. (2008). Removal of fluoride, arsenic and coliform bacteria from drinking water by modified homemade filter media. Bioresource technology, 99(7), 2269-2274.

Dr.Krittiya Lertpocasombut is an Associate Professor in the Department of Civil Engineering, Faculty of Engineering, Thammasat University, Thailand. She received a B.Sc. in Chemistry from Chulalongkorn University, Thailand, an M.Sc. in Environmental Engineering, Asian Institute of Technology (A.I.T.), D.E.A. Diplome dâ&#x20AC;&#x2122;Etudes Approfondies in Water Purification and Treatment Engineering from INSA de Toulouse, France, and a PhD in Water Purification and Treatment Engineering, Institut National des Sciences Appliquees (INSA), Toulouse, France. Dr. Lertpocasombut is interested in water and wastewater treatment; wastewater recycled by membrane technology; water supply sludge treatment and its reuse/recycle.

Maruay Onsod is Quality System & Product Auditing Manager, Goodyear (Thailand) Public Co. Ltd., Thailand. She obtained a B.Sc (Chemistry) from Rajamagala University of Technology (Thanyaburi). She is a master candidate in the Department of Civil Engineering, Faculty of Engineering, Thammasat University.

She is interested in water treatment technologies.

Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines in the journalâ&#x20AC;&#x2122;s website.

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Krittiya Lertpocasombut and Maruay Onsod

2014 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://TuEngr.com

A Review of Resource-Constrained Project Scheduling Problems (RCPSP) Approaches and Solutions Mohammad Abdolshah a* a

Engineering Faculty, Islamic Azad University, Semnan Branch, Semnan, Iran

ARTICLEINFO

Article history: Received 17 June 2014 Received in revised form 08 July 2014 Accepted 10 July 2014 Available online 11 July 2014

Keywords: Exact salvation; Heuristics; Meta-heuristics; Deterministic.

A B S T RA C T

Resource-constrained project scheduling problems are one of the most famous proposed problems in operational research and optimization topic. Using of discrete models by considering complexity of the problems requires designing efficient algorithms for solving them. On the other hand, this series of topics and generally project management are given attention in recent decades.

Competition features of today’s world, lead in time implementation of project with required quality to be important. Those factors lead to be given attention to resource-constrained project scheduling problems and their solutions theoretically and practically by academic researches and practitioners. The purpose of the paper is determining different methods and approaches that are used for solving the mentioned problems simultaneously or separately. The various described models in literature that consist of more than 200 published papers in most well-known journals, are collected and proposed in table format. In this research by studying these papers, in addition clarifying features of the developed models and the gaps, practitioners of projects implementation in various organizations can choose appropriate model for their projects by considering organizational conditions, types of resources and their organization’s activities’ technological specifications.

2014 INT TRANS J ENG MANAG SCI TECH.

1. Introduction Project planning is determination of time sequencing or scheduling plan for conducting a series of related activities that are constituents of project. In this case, Project disintegrate to some activity by methods like work breakdown structure (WBS). These activities are connected with each other because there are various logical relations between them. Logical and Immediate relations between each two activities are explained by controller like Finish to start (FS) relation, *Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

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start to start (SS) relation, finish to finish (FF) relation, and start to finish (SF) relation. Also, in more complicated projects it is possible to define more controllers like parallel implemented between two activities(Hadju, 1997In fact dependence of activities is based on their priority of implementation; it means it is possible that implementation of an activity depends on implementation of the others, this is called that project has priority constraints between activities.

But in addition to these limitations, May bean other type of constraints, as resources constraints exist in project. So in project planning in addition to considering priority constraints, planning should be compatible with resources constraints. The objective of scheduling and sequencing activities is optimal allocation of limited resources over time. In fact scheduling is determination of activities which must be done in the specified time and sequencing, determine order of activities which must be done. Those project planning problems which do not have limitations of resources or consider them, are known as project scheduling problems without resourceconstrained and those problems which have resource-constrained and these limitations are considered in planning project, called resource-constrained project scheduling problems (RCPSP). This problem is one of the most complicated problems of operation research which has considerable progress in developing exact solution and innovative methods at recent decades and recently new optimization methods are used to solve it” (Mohring et al, 2003).For implementing each activity requires different resources such as time, capital, human power and etc. These resources are often divided into two categories: Renewable like human power and nonrenewable such as capital. Each activity can be implemented in several modes such as manually, semi-mechanized and mechanized. Implementation of each mood needs different type and amount of resources (Drexl et al, 1993). In resource-constrained project scheduling problems for implementing each activity like i needs rik unit of resource k = 1,…,m , at per unit of activity’s execution time (di). Meanwhile k resource has bk constraints per unit of time. The parameters (di ,ri , bk)are non-negative and determined. This problem’s objective often is determining start time and mode of implementation of each activity for minimizing the project’s execution time. It is obvious that the problem solution must provide constraints that are related to activities’ logical relations, and consider resource constraints too. There are two optimal and heuristics approach for solving the problem (Herroelen et al, 1998). The realistic solution instances of the problem because of complexity, extension and difficulty with optimal approaches like mathematical planning, dynamic planning or branch and bound, is impractical (Brucker et al, 1998).

2. Solving Methods Before suing of computer in project scheduling problems, researches scheduled projects manually so it was too time consuming and was not a good guaranty for achieving an optimal result. In the last of 1950 decade, developing critical path techniques and evaluating and overlooking the project led that projects had capability to be described by network diagrams as 254

Mohammad Abdolshah

works and activities were defined by network structure. Nevertheless, within the techniques, only time was considered and limitation of using resources was not studied. Meanwhile projectâ&#x20AC;&#x2122;s constraint is one of the main problems of project planning in real world, during two recent decades types of project scheduling planning techniques under resource constrained conditions were proposed, implemented and controlled which generally are divided to exact and approximate methods. In fact it can be told that resource-constrained project scheduling problem has more than 40 years history. There are two approaches, optimal and heuristics, for solving the problem (Herroelen et al, 1998). Each of the methods has disadvantages and advantages. The exact methods have ability to obtain and guaranty optimal result. In these methods, all solving problem spaces are searched to find optimal answer from solving space. Although essential calculations for these methods are so many and as a results, they are so slow but guaranty the general optimization of problem, in fact the realistic solution instances of the problem because of complexity, extension and difficulty with optimal approaches like mathematical planning, dynamic planning or branch and bound, is impractical (Brucker et al, 1998). Of course the application of optimal approaches for solving smaller instances of the problem are reported in the literature. For instance, the paper refers interested reader to (Deckro et al, 1991) about mathematical planning, to (Icmeli et al, 1996. Carruthers et al, 1996) for numerical methods such as dynamic planning, to (Petrovic 1968, Demeulemeester 1998) about branch and bound methods. And for overcoming the computational problems of the methods, approximate methods are proposed. In these methods, Instead of the whole space of problem solution, a part of it is searched so they do not guaranty the optimal results and try to achieve a good approximate answer but they are quick methods and at the right time they achieve a good answer for huge problems. Many of the heuristics solving approaches for resource-constrained project scheduling problems are studied at 2006 (Kolisch et al, 2006). They categorized the approaches in 4 groups as (1) Priority rule- based approaches like Random sampling (Coelho et al, 2003); (2) Approaches based on meta-heuristics methods such as genetic algorithm (Alcaraz et al, 2003.

Tareghian et al, 2007), tabu search algorithm (Nonobe et al, 2002), simulated annealing (SA) algorithm (Valls et al. 2004) ant systems (Merkle et al, 2002); (3) Non â&#x20AC;&#x201C; Standard metaheuristics approaches like scatter search algorithm (Fleszar et al, 2004); and at last (4) approaches based on other heuristics methods such as forward and backward Improvement (FBI) (Tormos et al. 2003), Network analysis (Sprecher, 2002). This paper categorizes solving models that are discussed in past literature, as 3 diagrams

3. Exact solving methods RCPSP are as general format of sequence of operations of NP hard problems type. The *Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

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optimal solutions, which are mentioned in literature, are: Zero-one mathematical planning and numerical implicit methods such as dynamic planning and branch and bound method. At recent decades, solving the problems is improved widely which are tested in two series problem. These series are: Series of 110 problems designed by Peterson and Series of 480 problems by Klisch.

Algorithms are evaluated base on how many problems are solved by them at how much time.

The series of Peterson problems include 110 problems instances that are designed by Peterson.

Series of problems have 7 to 50 activities and 1 to 3 renewable resources. During last decades, this series was a criterion for evaluating validity and ability of optimal and close to optimal procedure. In 1995, Klisch questioned validity of Petersonâ&#x20AC;&#x2122;s series that leads to develop ProGen. Network producer software that is able to produce RCPSP pattern with pre-determinate and 30 types of activity and 4 types of renewable resource, see Figure 1.

Exact solutions Mathematical planning

Numerical methods

determinstic methodes

Synthetic Methods

Stochastic

Dynamic planning

Dynamic planning

Markov chain

Branch and Bound method

Critical path PERT

Goal theory

Integer planning

Simulation

System analysis

Zero-one method

Synthetic

Linear planning

Figure 1: Exact solution categories

3.1 Heuristics solutions A brief definition of a heuristics method is a technique that search close solutions to optimal with acceptable computational cost, but in fact unlike the exact solutions which guaranty finding the optimal answer if there are, they do not guaranty for achieving to an optimal result.

Heuristics methods sometimes find the optimal answer and most of the time they reach to good answer. And these methods usually require less time and memory than exact solutions. The heuristics in scheduling often are defined as scheduling rules with dispatch rules. Often the rules are complex to be defined and for a specific type of the problem with a special series of restrictions and assumptions, are appropriate. The heuristics are used for searching combinational space of permutations in sequences of tasks or determining the conceivability of allocating resource, time and task during creation of scheduling or combining sequencing and scheduling. Heuristics scheduling are applied on series of tasks and determine at what time Mohammad Abdolshah 256

which task must be done. If a task can be done in more than one implementation condition or on series of resources, heuristics determines which resource or implementation is used. The heuristics solutions are be used for major problems pattern.

Parallel scheduling Generation scheduling

Serial scheduling Serial double scheduling

Constructive

Search- based

Parallel Single pass

priority rule

Multi pass

Forward backward

Priority-based

Improvement

Neighborhood search forward backward improvment

decomposition Based on exact methods

random sampling

sampling method

iteratation

Heuristics methods

baised random sampling regret bsed random sampling

column generation exact method

relaxation Lagrange Hybrid

Combination withith several heuristics

Figure 2: Heuristics methods categories

3.2 Meta-heuristics Solutions During last 20 years, a new type of estimated algorithm has been created which essentially tries to combine basis heuristics methods with an objective of efficient and effective search in search space in frameworks of upper level. The meta-heuristics methods are the last generations of heuristics algorithms and widely used for solving RCPSP too. In fact, the meta-heuristics are strategies in order to guiding search process. Participant techniques in meta-heuristics algorithms are in range of simple procedure, local search to complex learning processes.

3.2.1 Trajectory Methods It works on single solutions and includes meta-heuristics based on local search. It means that algorithm start form primary condition (primary solution) and describes a trajectory in search space. Each movement is take place if the result solution is better actual one. Upon finding local minimal, the algorithms end such as Tabu search, iterated local search and variable neighborhood search. Their common features are describing a trajectory in search space during search process.

3.2.2 Population methods They do search process which combine meta-heuristics evolution with exact methods or *Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

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other meta-heuristics, and combination of types of heuristics and meta heuristics in order to achieving optimal answers, can be observed in meta-heuristics methods, Figure 3.

Figure 3: Meta Heuristics methods categories

4. Approaches Most of the studies in planning and project scheduling assume that there are complete information for solving scheduling problem which must be solve and the obtained basis scheduling plan will be implemented in a static environment too. Although there are many uncertainly in a relation with activities implementation that take place with implementation of project gradually which includes the following categories in diagram? In this section, there is review of basis approaches in project planning and scheduling at exact and unreliability conditions. It will be discussed about application potential of each of the methods in project uncertainly planning with definitive network structure.

Figure 4 show types of RCPSP

approaches.

Types of approaches on RCPSP

Deterministic Reactive

Non- Deterministic

Proactive (Robust)

Stochastic

Number of approaches Fuzzy

Sensetive analyse

disceret

Integrated

Figure 4: Types of RCPSP approaches

4.1 Deterministic approach In this approach, all problemsâ&#x20AC;&#x2122; parameters are assumed definitive and determined and it has 258

Mohammad Abdolshah

rich position in RCPSP literature and is used for relaxation of the assumption in most of the papers. These kinds of papers because of simplifying real conditions have defects and practically, restrict efficiency of model in real projects.

Figure 5: FRCPSP categories.

4.2 Proactive (Robust) approach Objective of the proactive scheduling is producing basis-scheduling stable so in order to be protected against interruptions during implementation of project. Temporary protections (Gao 1995)increase duration of activities based on unreliability of amount of resources, which are used for activities. Resources that have possibility of failure or violation are called probable to violation resources. Protected duration of the activity includes main duration added to waiting duration of violation. Then basic scheduling is provided by problem solution with protected durations.

4.3 Reactive approach In Reactive scheduling, uncertainly are not given attention at creating basis scheduling but when uncertainly occur, the approach tries to answer, correcting and re-optimize the basis scheduling. Generally, the approachâ&#x20AC;&#x2122;s main correction is on correcting and optimizing the basis scheduling if unanticipated events are occurred. The basis scheduling can be designed based on various strategies. On the other hand, answering to occurred changes can be based on very *Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

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simple techniques such as Right shift rule (Sadeh et al. 1993) that they are influenced because of the defect in resources or precedence relations, transferred to the right which means their implementation time are postponed, it’s obvious that the method is not a such good idea because it does not reschedule. The similar strategies are called schedule repair actions.

4.4 Fuzzy approach Fans of activity ambiguous express the probability distribution function of activities leads to ambiguity and imprecise of estimation. The probability distribution function of an activity is ambiguous as long as information of its past, was not gained. A human expert should estimate the probability distribution function of an activity that often is non-recurring and exclusive.

4.5 Stochastic project approach Objective of stochastic project scheduling with resource constrained, is project scheduling which is such that despite of activity duration uncertainly, precedence relations (Finish to start with zero lag) and renewable resource-constrained, minimizes make span. The studies on stochastic project scheduling are partly sporadic. Most of the studies are known as “stochastic project scheduling with resource-constrained” which are studied in next section.

5. Review of Solutions and Approaches of Resource-constrained Project Scheduling In order to review researches procedure and researches’ opportunities, all of the researches are studied as two perspectives “ solution methods and approaches” in more than 200 papers of valid journals and after removing the similar articles, the chosen articles was studied and extracted their points and the results are shown by following tables. Tables 1 and 2 show the results of research about types of solution methods and approaches in RCPCP literature. There are brief explanations about important results of research in considerations column.

Table 1: RCPSP researches based on solution methods Authors

Year

Exact method

Solutions Not exact method

Heuristics

1. D.C. 2012 Paraskevopoul os et al.

AILS, SAILS

2. Chen Fang, Ling Wang

2012

SSGS

3. Mohamed Haouari et al

2012 Dynamic programm ing, lower

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Mohammad Abdolshah

Meta heuristics

SFLA

Other

Specifications Propos solution methodology, namely SAILS, operates on the event list and relies on a scatter search framework. The latter incorporates an Adaptive Iterated Local Search (AILS), as an improvement method, and integrates an event-list based solution combination method.

Encode the virtual frog as the extended activity list (EAL) and decode it by the SFLA-specific serial schedule generation scheme (SSSGS) and To enhance the exploitation ability, a combined local search including permutation-based local search (PBLS) and forward–backward improvement (FBI) is performed.

Propose three classes of lower bounds that are based on the concept of Enhanced energetic reasoning

bounds 4. Ling Wangn, ChenFang

2012

5. Thomas S. 2012 Kyriakidis et al.

6. KoorushZiarat 2011 i etal.

7. Shu-Shun Liu& ChangJung Wang

2011

EDA

In the EDA the individuals are encoded based on the activitymode list (AML) and decoded by the multi-mode serial schedule generation scheme (MSSGS), and a novel probability model and an updating mechanism are proposed for well sampling the promising searching region.

Present new mixed-integer linear programming models

SSGS

Bee algorithms

Proposed algorithms iteratively solve the RCPSP by utilizing intelligent behaviors of honeybees. Each algorithm has three main phases: initialization, update, and termination.

A generic model is proposed to maximize the total profit of selected projects for construction and R&D departments given scheduling problems with various resource constraints during specified time intervals The procedure is basically a combination of four descent procedures that use simulation to evaluate the objective function

MILP

CP

8. FilipDeblaere 2011 et al.

Simulation -based Descent (SBD), SSGS

9. SiamakBarada 2011 ran et al.

10. Mohammad Ranjbar et al.

SSGS, MFBI, MPBLS

2011 Branchandbound algorithm

11. R. Čapek et al 2011

Linear programm ing model

12. MariemTrojet 2011 et al.

13. Ling Wang, 2011 Chen Fang

CP

IRSA

SSGS

14. José Coelho, Mario Vanhoucke

2011

15. Ruey-Maw Chen

2011

SSGS

16. Shanshan Wu 2011 et al.

17. Mahdi Mobini 2011 et al.

SSGS SSGS

18. OumarKone et 2011 al.

MILP

19. LucioBianco& 2011 Massimiliano Caramia

Lower bound

20. Agustín Barrios et al.

2011

21. AnuragAgarw 2011

DGA

HMA

Presents a hybrid met heuristic algorithm based on scatter search and path linking algorithms to solve the stochastic MRCPSP Present a branch-and-bound algorithm in which the branching scheme starts from a graph representing a set of conjunctions In the search tree; each node is branched to two child nodes based on the two opposite directions of each undirected arc of disjunctions.

A heuristic algorithm based on priority schedule construction with an un-scheduling step is proposed for the nested version of the problem and it is used to solve the case study of the wire harnesses production.

Provide a decision support framework under the constraints as a margin of cooperation/ negotiation with subcontractors Hybrid Individuals are encoded based on the extended active list EDA (EAL) and decoded by serial schedule generation scheme (HEDA) (SGS), a Forward–Backward iteration (FBI) and a permutation based local search method (PBLS) are incorporated into the EDA based search to enhance the exploitation ability A novel The algorithm splits the problem into a mode assignment step metaand a single mode project-scheduling step. The mode heuristic assignment step is solved using a fast and efficient SAT solver.

JPSO The justification technique is combined with PSO as the proposed justification particle swarm optimization (JPSO), which includes other designed mechanisms.

CBIIA The proposed CBIIA is based on the traits of an artificial immune system, chaotic generator and parallel mutation AIA The proposed algorithm benefits from local search mechanisms as well as mechanism that enhances the diversity of the search directions Make a comparative study of several-mixed integer linear programming (MILP) formulations for resource-constrained project scheduling problems (RCPSPs).

The lower bound is based on a relaxation of the resource constraints among independent activities and on a solution of the relaxed problem suitably represented by means of an AON acyclic network.

The heuristic is a two-phased genetic algorithm with different representation, fitness, crossover operator, etc., in each of them.

Neurogene A new hybrid of a neural network approach and the genetic

*Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

261

al et al.

22. Francisco 2011 Ballestıín, RosaBlanco 23. FilipDeblaere 2011 Branchet al. andbound 24. TarunBhaskar 2011 et al.

SSGS IDA

SPEA2, NSGA2 and PSA TS

DCSGS

Heuristic HUDD-PS

26. José Fernando 2011 Gonçalves et al.

FBI, SSGS

Genetic algorithm

27. Vincent Van Peteghem, Mario Vanhoucke 28. Reza Zamani

2011

SSGS

Scatter search algorithm

2011

SSGS

A hybrid decomposi tion procedure

30. BehzadAshtia 2011 ni et al.

31. Francisco 2011 Ballestín et al.

32. Jie Zhu et al.

2011

33. Mohammad Jaberi

2011

34. Hong Zhang,Feng Xing

2010

35. E. Klerides, E. 2010 Hadjiconstanti nou 36. Qi Hao et al.

2010

37. Svio B. Rodrigues, Denise S. Yamashita 38. Sonda Elloumi, Philippe

2010

262

Propose and evaluate a number of dedicated exact reactive scheduling procedures as well as a TABU search heuristic for repairing a disrupted schedule Propose a non-recursive heuristic method based on priority rule for a new scheduling scheme and call it priority rule as Schedule Performance Index Different approaches to solving the continuous part of the problem were presented an exact approach requiring solving a convex mathematical programming problem, a heuristic approach to the continuous resource allocation problem (heuristic HUDD-PS), and the approach based on the continuous resource discretization.

Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions.

Combination of improvement methods and the introduction of two local searches into one overall solution procedure leads to promising computational results

SPI

25. GrzegorzWali 2011 góra

29. Olivier 2011 Lambrechts et al.

tic algorithms approach approach Extensive computational results help decide which algorithms or techniques are the most promising for the problem.

Time buffering using the STC SSGS, localsearch SSGS, evolutiona ry algorithm

Genetic algorithm

SSGS

PottsMFA PSO

Two-stage stochastic integer programm ing A dynamic algorithm MMBA algorithm

2010

Mohammad Abdolshah

The procedure finds an initial schedule for the project, and refines it through a decomposition process, To achieve further reduction, the refined schedule is over-refined by a genetic algorithm Suggest to either implement time buffering based on the first surrogate objective function or using the STC heuristic

A hybrid rank-based evolutiona

FLC

A two-phase local-search procedure is developed to produce high-quality pre-processor policies for SRCPSP instance, first phase is devoted to finding good priority lists Works on a population consisting of several distance-orderpreserving activity lists representing feasible or infeasible schedules. The algorithm uses the conglomerate-based crossover operator During the genetic process of the proposed GA, an offspring generator was introduced to generate a feasible activity list from parent chromosomes A Potts mean field feedback artificial neural network is designed and integrated into the scheduling scheme so as to automatically select the suitable activity for each stage of project scheduling Present a fuzzy-multi-objective particle swarm optimization to solve the fuzzy TCQT problem. The time, cost and quality are described by fuzzy numbers and a fuzzy multi-attribute utility methodology incorporated with constrained fuzzy arithmetic operations is adopted to evaluate the selected construction methods Propose a path-based two-stage stochastic integer programming approach in which the execution modes are determined in the first stage while the second stage performs activity scheduling according to the realizations of activity durations A dynamic algorithm based on partial task networks ,practical heuristics for conflict detection, project prioritization and conflict resolution The new algorithm consists of a hybrid method where an initial feasible solution is found heuristically Introduce clustering algorithms to compute densities. In this way enforce that neighbor solutions belong to the same cluster and are assigned the same density.

Fortemps 39. AnisKooli et al.

2010

40. Jairo R. MontoyaTorres et al.

2010

Integer programm ing

ry algorithm

SSGS, PSGS

genetic algorithm

41. SiamakBarada 2010 ran et al.

SSGS, PSGS

42. Moslem 2010 Shahsavar et al.

43. C.U. 2010 F端ndeling, N. Trautmann

A hybrid scatter search Genetic algorithm

A novel method of SGS

44. Ruey-Maw Chen et al.

2010

A novel PSO

45. Wang Chen et 2010 al.

SSGS

ACOSS

46. Vincent Van 2010 Peteghem, Mario Vanhoucke 47. E. Klerides, E. 2010 Integer Hadjiconstanti programm nou ing

SSGS

GA

48. Andrei Horbach 49. Angela H. L. Chen, ChiuhCheng Chyu

2010

Lower bounds 2010 Branchandbound

50. WANG Hong 2010 et al.

51. Reza Zamani

2010

SSGS, PSGS, FBI

Parallel complete anytime procedure

52. JiupingXu&Z 2010 he Zhang 53. Isabel Correia 2010 et al.

54. Wang 2010 Xianggang1 & Huang Wei

The twophase hybrid metaheuris tic GA

Upper bound

Hybrid genetic algorithm

New feasibility tests for the energetic reasoning are introduced based on new integer programming (IP) formulations.

Propose an alternative representation of the chromosomes using a multi-array object-oriented model in order to take advantage of programming features in most common languages for the design of decision support systems The path re-linking algorithm and two operators like crossover and prominent permutation-based are applied to solve the problem Genetic algorithm (GA) is designed using a new three-stage process that utilizes design of experiments and response surface methodology.

Present a priority-rule method based on a novel schedulegeneration scheme and a consistency test for efficient scheduling of individual activities that iteratively determines a feasible resource-usage profile for each activity The delay local search enables some activities delayed and altering the decided start processing time. The bidirectional scheduling rule which combines forward and backward scheduling to expand the searching area in the solution space for obtaining potential optimal solution.

Algorithm combines a local search strategy, ant colony optimization (ACO), and a scatter search (SS) in an iterative process Apply a bi-population genetic algorithm, which makes use of two separate populations and extend the serial schedule generation scheme by introducing a mode improvement procedure.

Propose a path-based two-stage stochastic integer programming approach in which the execution modes are determined in the first stage while the second stage performs activity scheduling according to the realizations of activity durations Solver is lightweight and shows good performance both in finding feasible solutions and in proving lower bounds Using a branch-and-bound algorithm to solve the mode assignment problem in the first phase; then, by transforming a multi-mode case into a single-mode problem, the second phase was activated and the memetic algorithm was applied to achieve good quality solutions Algorithm employs a standardized random key (SRK) vector representation with an additional gene that determines whether the serial or parallel schedule generation scheme (SGS) is to be used as the decoding procedure. The iterative forward-backward improvement as the local search procedure is applied upon all generated solutions Procedure finds a sequence of solutions in which every solution improves the previous one. To accelerate the convergence of the sequence to the optimal solution, the procedure simultaneously works in the forward and backward directions FLC Choose the hybrid genetic algorithm (HGA), and apply fuzzy logic control (FLC) to hybrid genetic algorithm (FLC-HGA) for enhancing the optimization quality and stability A mixed-integer linear programming formulation, proposes a two-phase heuristic procedure for obtaining such bound. In the first phase, a feasible schedule is constructed. In the second phase, an attempt is made to improve this schedule by means of a local search procedure.

Hybrid Hybrid intelligent algorithm integrated by genetic algorithm intelligent and fuzzy simulation is designed to solve the above two fuzzy algorithm programming models.

*Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

263

55. H. R. Yoosefzadeh et al.

2010

56. Angelo Oddi et al.

2010

PSGS

57. Doreen 2010 Krüger& Armin Scholl

Priority Rules

Mixedinteger model

Different flattening algorithms within the ifs metaheuristic strategy

58. YuryNikulin& 2010 Andreas Drexl

Pareto Simulated Annealing

59. Tyson R. Browning &Ali A. Yassine

2010

A random generator

60. Fawaz S. AlAnzi et al.

61. M. Ranjbar& F. Kianfar

2010

62. N. Damak et al.

2009

Lower bound SSGS, a local search

2010

63. PengWuliang, 2009 Wang Chengen 64. Liang Yan et 2009 al.

New heuristic approach

GA

Differentia l evolution (DE) algorithm.

Improved genetic algorithm

65. Po-Han Chen, 2009 Seyed Mohsen Shahandashti

Hybrid of GA-SA

66. Po-Han 2009 Chen,HaijieW eng

Twophase GA (genetic algorithm)

67. VikramTiwari 2009 et al.

IP

68. Jiaqiong 2009 Chen, Ronald G. Askin 69. Mohammad 2009 Ranjbar et al.

MIP

70. Antonio Lova 2009 et al.

264

SSGS SSGS, PSGS

Mohammad Abdolshah

A hybrid scatter search a hybrid Genetic Algorithm (MMHGA)

Compared the performance of forward, backward, bidirectional and tri-directional planning schemes in the context of different priority rules, The result obtained by each combination is an upper bound (UB) on the optimal project duration Iterative flattening search (ifs) is a meta-heuristic strategy for solving multi-capacity scheduling problems. Given an initial solution, ifs iteratively applies: a relaxation-step, and a flattening-step

At first develop a framework for considering resource transfers in single- and multi-project environments.

Afterwards, define the multi-project scheduling problem with transfer times (RCMPPTT) and formulate it in a basic and an extended version as integer linear programs Eventually, it is supplemented for the first time by cost considerations A multi-criteria meta-heuristic, in order to get a representative approximation of the Pareto front Present the first multi-network problem generator, The generator produces “near-strongly random” networks quickly, and can produce increasingly more strongly random networks at greater computational expense. Then identify a tradeoff between the degree of randomness and computational time A lower bound that uses a linear programming scheme for the RCPSP. Developed a linear model for the problem, an enumeration procedure for generation of feasible work problems and a meta-heuristic, based on the Genetic Algorithm (GA), for solving the problem. Also developed a local search incorporated with GA to improve the solutions' quality Focus on the performance of this algorithm to solve the problem within small time per activity.

According to the characteristics of the proposed problem, an improved genetic algorithm was presented Combining the RCPSP model with the five heuristic, By comparing with those generated by the manual decisionmaking method, the results generated by heuristic algorithm indicate high efficiency First attempts to use meta-heuristics and non-traditional techniques, can be seen that GASA Hybrid has better performance than GA, SA, MSA, and some most popular heuristic methods The developed two-phase GA model works well. With further development to allow for multiple resource types, the two phase GA model could be generalized and applied to all sorts of resource-constrained project scheduling problems, including interruption and overlap of activities Formulate the problem with a rework, quality-enhancing component and solve the resulting problem using commercial optimization procedures.

Two versions of the Mixed Integer Program (MIP) Using path re-linking methodology as a solution combination method.

A new parameter has been designed and its efficiency stated.

In the evolution process characteristic of the GAs, fitness function plays a crucial role

71. J.J.M. Mendes 2009 et al.

72. Kuo-Ching Ying et al.

2009

73. WU Yu et al.

2009

74. JörgHomberge 2011 r 75. C.C. Chyu& Z.J. Chen

2009

76. M. D. Mahdi Mobini et al.

2009

77. Christian 2009 Artigues& Cyril Briand 78. Shu-Shun 2008 Liu,ChangJung Wang 79. Nai-Hsin Pan 2008 et al.

80. Stijn Van de Vonder et al.

2008

81. Francisco Ballestı´n et al.

82. R. AlvarezValdes et al.

2008 2008

83. J.F. Gonçalves 2008 et al.

84. Hédi Chtourou& Mohamed Haouari

CMAS

Several variable neighborh ood search (VNS)algo rithms SSGS, Enhanced PSGS scatter search

CP

PSGS, RFDFF, VADE, STC SSGS, DJGA, 1_DJGA Several heuristic algorithms SSGS

2008

Twostagepriorityrule-based

85. Haitao Li, 2008 Keith Womer

Constraint programm ing

86. LuongDuc Long, ArioOhsato

2008

87. Mohammad Ranjbar

2008

A random key based genetic algorithm A hybrid– directional planning scheme Timed colored Petri net (TCPN)

a new heuristic algorithm

A new polynomia l algorithm

An improved TS model

GA

Developed a procedure (named P1)

The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm.

To evaluate the effectiveness of the proposed scheme, different planning directions are incorporated into some metaheuristics, including GA, SA, and TS Firstly a novel mapping mechanism between traditional network diagram such as CPM (critical path method)/PERT (program evaluation and review technique) and TCPN was presented Multiple solutions consists of several self-interested schedule agents, each of which plans a single project decent rally and autonomously.

Developed by using insertion move and two swap to generate various neighborhood structures, and making use of the wellknown backward–forward scheduling, a proposed future profit priority rule, or a short-term VNS as the local refinement scheme (D-VNS).

Decode to the solutions using both serial and parallel SGS and serial-SGS was used during the iterations of the algorithm. In the proposed ESS, three operators were used to generate new solutions from existing solutions in the reference set As a basic search framework For reinsertion neighborhoods Presented model, constructed by Constraint Programming (CP), considers resource usage and cash flow in project scheduling to fulfill management requirements.

Develop an improved TS model by modifying the way of finding a starting solution instead of traditional TS algorithm, minimum moment algorithm (MMA) Multiple efficient heuristic and meta-heuristic procedures are proposed to allocate buffers throughout the schedule show how three basic elements of many heuristics for the RCPSP – codification, serial SGS and double justification – can be adapted to deal with interruption Procedures. Heuristic algorithms based on GRASP and Path re-linking are then developed and tested on existing test instances Schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm The first stage solves the RCPSP for minimizing the makespan only using a priority-rule-based heuristic, namely an enhanced multi-pass random-biased serial schedule generation scheme. Then similarly solved for maximizing the schedule robustness while considering the makespan obtained in the first stage as an acceptance threshold.

A constraint programming (CP) based solution approach is proposed and implemented in one case study The proposed method is useful for both project planning and execution which is well known priority heuristic rules and standard genetic algorithm Proposes a new heuristic algorithm for this problem based on filter-and-fan method incorporated with a local search, exploring in the defined neighborhood space

*Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

265

88. Marek Mika et 2008 al.

89. Mario 2008 BranchVanhoucke andbound

SSGS

90. Shih-Tang Lo 2008 et al.

91. Vicente Valls 2008 et al.

92. L.-E. Drezet, J.C. Billaut

TS

ant colony optimizati on (ACO) SSGS

Hybrid Genetic Algorithm (HGA)

MILP Twoformulatio phase n heuristic algorithm 93. Mario 2008 BranchVanhoucke, andDieter Debels bound 94. B. Jarboui et 2008 Combinat al. orial PSO (CPSO) algorithm 95. Sanjay Kumar 2008 Adaptive Shukla et al.

SSGS samplesort simulated annealing

2008

96. Olivier 2008 Lambrechts et al.

97. Olivier Liess& Philippe Michelon 98. A. A. Lazarev& E. R. Gafarov 99. MajidSabzehp arvar& S. Mohammad SeyedHosseini 100. Jean Damay et al.

2008

SSGS

Constraint programm ing

Time slackbased techniques , TS

2008 Branchandbound 2008 Linear mixed integer programm ing 2007 Linear programm ing 101. ShahramShadr 2007 GA okh, FereydoonKia nfar 102. Mohammad 2007 SSGS AmetaR. Ranjbar, heuristic FereydoonKia algorithm nfar 103. JirachaiBuddh 2007 SSGS, akulsomsi, Priority David S. Kim rule-based 104. Stijn Van de 2007 SSGS, Vonder et al.

PSGS, weighted-

266

An application of a local search meta-heuristic TABU search for the considered problem has been described First aim at the construction of efficient meta-heuristic solution procedures to solve the PRCPSP-FT and the PDTRTP-FT where set-up times are incorporated between pre-emptive sub-activities, Second try to extend this approach to a flexible activity assumptions problem setting Present a modified ACO approach named DDACS for a multi-constraint multiprocessor scheduling problem The proposed DDACS algorithm modifies the latest starting time of each job in the dynamic rule for each iteration HGA introduces several changes in the GA paradigm: a crossover operator specific for the RCPSP; a local improvement operator that is applied to all generated schedules a new way to select the parents to be combined; and a two-phase strategy by which the second phase re-starts the evolution from a neighborâ&#x20AC;&#x2122;s population of the best schedule found in the first phase.

The first phase is a greedy algorithm, whose solution is used in the second phase as an initial solution for a TABU search algorithm

Mohammad Abdolshah

Present adapted lower bound and upper bound calculations for the PDTRTP-FT.

FLC

CPSO algorithm outperforms the simulated annealing algorithm and it is close to the PSO algorithm. Also used a local search method to optimize the sequence associated to each assignment.

Propose a parallel intelligent search technique named the fuzzy based adaptive sample-sort simulated annealing (FASSA) heuristic. The basic ingredients of the proposed heuristic are the serial schedule generation scheme (SGS), sample sort simulated annealing (SSA), and the fuzzy logic controller (FLC).

Develop an approach for inserting explicit idle time into the project schedule in order to protect it as well as possible from disruptions caused by resource un-availabilities.

Classical Constraint Programming approach for the (RCPSP) except that the timetable algorithm is not considered.

Prove that method like branch-and-bound (branch & bounds, Constraint Programming, and so on) with the lower estimate LBM be ineffective.

Time horizon can be continuous in this model thus dealing with different processing time units

A time-indexed linear formulation of the non-preemptive version of the RCPSP involving these feasible subsets 690 problems are solved and their optimal solutions are used for the performance tests of the genetic algorithm Based on the genetic algorithm and a new method based on the resource utilization ratio is developed for generation of crossover points and also a local search method is incorporated with the algorithm Both deterministic multi-pass and stochastic multi-pass heuristics have been constructed Present a sampling procedure that combines the schemes with multiple priority lists. Also describe a heuristic for the weighted earlinessâ&#x20AC;&#x201C;tardiness problem

105. Jacques 2007 Carlier& Emmanuel NĂŠron 106. M. Rabbani et 2007 al.

earliness tardiness heuristic

Improving simulated annealing with variable neighborh ood search

2007

109. Lin-Yu Tseng, 2006 Shih-Chieh Chen

A hybrid meta heuristic ANGEL

110. Amir Azaron, 2006 NonReza linear Tavakkoliprogramm Moghaddam ing 111. Luciano 2006 An LessaLorenzo evolutiona ni et al. ry algorithm 112. Dieter Debels 2006 SSGS et al.

113. Hong Zhang et al.

2006

Propose an explicit enumeration of the redundant resources and a characterization of the non-dominated ones

A new heuristic algorithm

107. VĂŠroniqueBou 2007 ffard& Jacques A. Ferland

108. RinaAgarwal et al.

Enumerati on algorithm

PSGS

114. John-Paris 2006 a heuristic Pantouvakis, method Odysseus G. Manoliadis 115. Guidong Zhu 2006 A branch et al. and cut 116. I-Tung Yang, 2005 Linear Chi-Yi Chang programm ing 117. Marek Mika et 2005 al.

SSGS

118. M.A. AlFawzan, Mohamed Haouari 119. KwanWoo Kim

2005

SSGS

2005

SSGS

In order to prevent creating a lower bound for the mean project completion time, the most critical chain is determined and its standard deviation is added to project completion time as the project buffer Consistent with the fact that the simulated annealing approach performs better than the TABU search approach for RCPSP Furthermore, the performance of the simulated annealing method can be improved with a variable neighborhood search approach Artificial The performance of the proposed AIS algorithm on test immune problem, reported in literature is found to be superior, when system compared with GA, fuzzy-GA, LFT, GRU, SIO, MINSLK, RSM, RAN, and MJP ANGEL combines ant colony optimization (ACO), genetic algorithm (GA) and local search strategy. Also proposes an efficient local search procedure that is applied to yield a better solution when ACO or GA obtains a solution. A final search is applied upon the termination of ACO and GA The dynamic PERT network, representing as a network of queues, was transformed into an equivalent classical PERT network An algorithm based on differential evolution algorithm was selected to serve as a solution procedure.

A new metaheuristic(E M)

The procedure is a population-based evolutionary method that combines elements from scatter search, a generic populationbased evolutionary search method, and from a recently introduced heuristic method for the optimization of unconstrained continuous functions based on an analogy with electromagnetism theory A PSO-based method including its corresponding framework is proposed for solving the RCPSB

Particle swarm optimizati on (PSO)

A heuristic method is developed based on traditional CPM scheduling Calculations and leveling algorithms Based on the integer linear programming (ILP) formulation of the problem Present a chance-constrained programming model, derive its deterministic equivalent, and solve the equivalent by classical linear programming techniques., Model verification is performed by Monte Carlo simulations Applications of two local search meta-heuristics

Simulated annealing and TABU search TABU search algorithm Hybrid genetic

Develop a TABU search algorithm in order to generate an approximate set of efficient solutions FLC

The proposed new approach is based on the design of genetic operators with fuzzy logic controller (FLC) through

*Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

267

120. Tamás Kis

2005 a branchand-cut algorithm

121. Sophie Demassey& Chiristian Artigues

2005

algorithm with fuzzy logic controller (FLCHGA)

Lower A bound, heuristic linear method programm ing 122. Krzysztof 2004 SSGS, Fleszar, Khalil variable S. Hindi neighborh ood search(VN S) 123. Juite Wang 2004 SSGS Genetic algorithm

124. I.E. 2004 Diakoulakis et al.

125. Reza Zamani

2004

Time window

126. ChristophMell 2004 entien

127. Vicente Valls 2004 & Francisco Ballestín

128. Philippe Baptiste & Sophie Demassey 129. Mireille Palpant et al.

2004

130. A. LIM et al.

2004

2004

131. Christian 2003 Artigues et al.

268

SSGS, PSGS

A relaxationbased beamsearch solution Convex Search, Homogene ous Interval Algorithm (back ward, forward) Tight LP bounds SSGS

Mohammad Abdolshah

Formalize the problem by means of a mixed integer-linear program, prove that feasible solution existence is NPcomplete in the strong sense and propose a branch-and-cut algorithm for finding optimal solutions Propose a cooperation method between constraint programming and integer programming to compute lower bounds for the RCPSP. In addition to the use of VNS to explore the solution space, the effectiveness of the scheme is due to progressively reducing the solution space by repeatedly improving both lower and upper bounds, as well as by discovering additional valid precedence to augment the existing set.

Adapt a Branch-and-Bound algorithm for resourceconstrained project scheduling by Bell and Park (1990) to the fuzzy case. And propose GA approach can obtain the robust schedule with acceptable performance

Evolution Strategies (ES)

Under two discrete solution encodings; one works on vectors of priority values and the other is based on convex combinations of priority rules

SA

Procedure consists of a SA component and a time-windowing process. The SA component generates a base schedule and the time-windowing process improves the base. The combination of three factors contributes to the efficiency of the simulated annealing component Present a relaxation-based beam-search solution heuristic.

Exploiting a duality relationship between temporal scheduling and min-cost network flow problems solves the relaxations.

Scatter search

Procedure incorporates various strategies for generating and evolving a population of schedules. It is the result of combining four innovative basic procedures

14 more lower bounds are improved in an average CPU time of 284.6 seconds LSSPER A hybrid framework

PSGS, a new

initializing the revised serial method which outperforms the non-preemptive scheduling with precedence and resources constraints

Present the Local Search with Sub-Problem Exact Resolution (LSSPER) method based on large neighborhood search for solving the problem This hybrid framework has a two-level structure. TS and GA heuristic searches were used in the high level components of algorithms. For the low level components, a CP-based iterative randomized method and a Minimal Critical Setbased method were used to resolve temporal and resource conflicts. The four combinations of these – Tabu_CP, Tabu_MCS, GA_CP, GA_MCS – were tested on two sets of real test data Show that such an algorithm is of great interest for robust rescheduling in a dynamic environment

polynomia l insertion algorithm SSGS

132. Vicente Valls 2003 et al.

133. J. Carlier& E. 2003 Néron

Linear lower bounds (LLB)

134. DimitriGolenk 2003 o-Ginzburg et al.

135. Roland 2003 BranchHeilmann andbound 2003

SSGS

137. M Kamrul Ahsan& DeBi Tsao

2003

bi-criteria search strategy of a heuristic learning

139. Chiu-Chi Wei 2002 et al.

Enhanced TOC method

140. AmedeoCesta 2002 & Angelo Oddi

A heuristic algorithm( ISES) a new heuristic

141. A Sprecher

2002

142. Mario Vanhouck et al.

2001 Branchandbound

Non-standard implementation of fundamental concepts of TABU search without explicitly using memory structures embedded in a population-based framework, makes use of the TO representation of schedules First application that we present is a general linear programming scheme for computing a makespan lower bound. The second application consists in associating redundant resources with LLB Algorithm can be used for CAAN models which cover a broad spectrum of alternative stochastic networks

RCGPS algorithm

136. Kwan Woo Kim et al.

138. J Alcaraz et al. 2003

A new meta heuristic algorithm CARA,

Hybrid genetic algorithm (HGA) with fuzzy logic controller (FLC)

GA

143. Birger Franck 2001 BranchSSGS, et al. andheuristic bound procedures 144. GunduzUluso 2001 Genetic y et al. algorithm (GA) 145. Sönke 2001 SSGS Genetic

FLC

The solution method is a depth-first search based branch-andbound procedure. It makes use of a branching strategy where the branching rule is selected dynamically. The solution approach is an integration approach where the modes and start times are determined simultaneously.

Based on the design of genetic operators with FLC and the initialization with the serial method, to find optimal or nearoptimal initial solutions which has been shown superior for large-scale RCPSP

Formulate a state-space representation of a heuristic search algorithm with a bi-criteria partial schedule selection technique. The heuristic solves problems in two phases. Also propose a variable weighting technique based on initial problem complexity measures.

Before the genetic algorithm itself is started, apply a preprocessing procedure over the project data, in order to reduce the search space.(to reduce the volume of the data and speed up the execution of their algorithm for this problem.)

The enhanced TOC project scheduling technique determines the lower bound of the project length by using the combination of the existing heuristic algorithms, used to conduct the activity duration cut and establish project buffer, feeding buffer and resource buffer Use of an iterative sampling procedure which relies, on a constraint satisfaction problem solving (CSP) search procedure The strategy combines elements of exact and heuristic solution procedures. It relies on decomposition of a problem into sub-problems, near optimal solution of the sub-problems, and concatenation of the sub-problem solutions. The algorithm significantly outperforms the truncated exact branch-and bound algorithm on larger instances.

Introduce a depth-first branch-and-bound algorithm which makes use of extra precedence relations to resolve resource conflicts and relies on a fast recursive search algorithm for the unconstrained weighted earliness–tardiness problem to compute lower bounds Propose several truncated branch-and-bound techniques, priority-rule methods, and schedule-improvement procedures of types TABU search and genetic algorithm Use a special crossover operator that can exploit the multicomponent nature of the problem.

Extending the genetic algorithm framework by local search

*Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

269

Hartmann

Algorithm

146. A. Kimms

2001

147. J. Prashant 2001 Reddy et al.

148. Antonio 2001 Lova&PilarTo rmos

Tight Upper Bounds

149. Joanna 2001 Jozefowska et al.

Lagrangia n relaxation SSGS, PSGS, New heuristics SSGS

150. PilarTormos& 2001 Antonio Lova

SSGS, PSGS, hybrid multi-pass method

151. Roland Heilmann

Multi– pass priority– rule method Eight agentbased algorithms

2001

152. Gary Knotts et 2000 al.

153. ChristophSch 2000 Branch– windt & andNorbert bound Trautmann algorithm 154. Erik 2000 Branch– Demeulemeest ander et al. bound algorithm 155. Ulrich 2000 TimeDorndrof et al. oriented branchandbound 156. Arno Sprecher 2000 Branchandbound 157. Tam P. W. M. 1999 & E. Palaneeswaran

1998

160. Abel A.Fernandez

1998

270

A new simulated annealing algorithm

Two versions of the simulated annealing approach are discussed: SA without penalty function and SA with penalty function Technique is a hybrid multi-pass method that combines random sampling procedures with a backward–forward also the algorithm includes as a determinant characteristic the alternative use of the serial and parallel schedule generation schemes in such a way that it benefits from the properties provided for both of them.

The heuristic is a multi–pass priority–rule method with back planning which is based on an integration approach and embedded in random sampling Develop and experimentally evaluate eight agent-based algorithms, algorithms differ in the priority rules used to control agent access to resources Solve to feasibility by a simple batching heuristic and the subsequent solution of the corresponding batch scheduling problem by a truncated version of the branch–and–bound algorithm within one minute Present a depth-first branch-and-bound procedure for the discrete time/resource trade-off problem in project networks (DTRTP) Describe a time-oriented branch and bound algorithm that uses constraint propagation techniques

The main purpose of this paper is direct focus to a branchand-bound concept A new heuristic method

158. Shue Li1999 Yen,RezaZam ani 159. Paul R. Thomas &Said Salhi

Geneticalgorithm

concepts used two local search methods. One was designed to deal with the feasibility problem of the MRCPSP, while the other was used to improve the schedules found by the GA Derive tight upper bounds on the basis of a Lagrangian relaxation of the resource constraints And also use this approach as a basis for a heuristic Describe Petri-net-aided software including geneticalgorithm-based search and heuristics Analyze the effect of the schedule generation schemes – serial or parallel and priority rules. Also New heuristics –based on priority rules with a two-phase approach

Mohammad Abdolshah

Note first outlines the suitability of ranked positional weight method (RPWM), a heuristic resource scheduling method, to construction project scheduling. It then focuses on a new heuristic technique, the enhanced positional weight (EPWM), which is an improved version of the RPWM. Some interesting comparisons between the results given by Primavera, Microsoft Project, RPWM, and EPWM are also presented An Present an admissible heuristic search algorithm SLA, and an intelligent implementation method for solving the RCPSP, this search algorithm is characterized by the complete heuristic learning method process: state selection, heuristic learning, and search path review Tabu Deal with a number of TABU search heuristic concepts in Search order to construct a method for this combinatorial problem, Approach( namely the PSTSM heuristic PSTSM) Alternativ Introduces a multi-period stochasticing programming based e model of the project scheduling problem simulation

161. Aristide, Mingozzi et al.

1998

162. Dan Zhu 1997 &RemaPadma n 163. Rainer 1997 Kolisch& Andreas Drexl 164. Arno Sprecher 1997 et al.

165. Kedar S. 1997 Naphade et al.

Branch Relaxation and heuristic bounds, A method new 0-1 linear programm ing formulatio n, a tree search algorithm

a new branch and bound algorithm

Two distinctly different problem space search procedures TS

167. Erik 1997 A new Demeulemeest branch and bound er, Willy S L Herroelen algorithm 168. Kum-Khiong, 1996 MINSLA yang K, CPR, FCFS 169. OyaIcmeli, S 1996 A branch SelcukErengu and bound c procedure 170. F.Brian Talbot 1982 Integer A program- heuristic ings solution 171. Jan Weglarz

173. Arne Thesen

1976

174. E. W. Davis& 1971 G. E. Heidorn

PSGS, Tow classes of heuristic procedure A new heuristic method

A dynamic programm

Based on a new mathematical formulation which is used to derive 5 new lower bounds and also described a new tree search algorithm based on this exact formulation that uses the new bounds

Artificial Apply neural networks to induce the relationship between neural project parameters and heuristic performance to guide the networks selection under different project environments Propose a new local search method that first tries to and a feasible solution and secondly performs a singleneighborhood search on the set of feasible mode assignments.

Present a new procedure which is a considerable generalization of the branch and-bound algorithm proposed

by Demeulemeester and Herroelen

a new local search

166. Moizuddin, 1997 Mohammed& Selim, S. Z.

1981 A priority analyses 172. Dale F Cooper 1976

algorithm

SA

Embed a fast base heuristic (for instance, a dispatching rule) within a search procedure, then showing comparable performance to the branch-and-bound algorithm.

The algorithm uses the priority space for generating neighbors. it also employed uses a short-term memory component. to optimize the TS parameters that developed are 3k factorial design.

Describe a new depth-first branch-and-bound algorithm(GDH-PROCEDURE) A total of one scheduling and three heuristic dispatching rules that these planning rules are used to specify the priority of each activity in a project b ranking the precedence-feasible activities on an activity priority list.

The bounds in the branch and bound procedure are computed by solving payment-scheduling problem that can be formulated as linear programs and by that are well solvable.

A two stages solution methodology is developed which builds upon idea presented earlier. Stage one defines the problem as a compact integer-programming problem, stage two searches for the optimal solution using an implicit enumeration scheme that systemically improves upon generated heuristic solutions.

The properties of optimal schedules are given for strictly, concave and convex activity models.

Assess the effects of the heuristic method, the project characteristics and the priority rules

Extend the fields of heuristic algorithms for RCPSP. a sub optimizing resource allocation algorithm is employed, A new hybrid heuristic urgency factor is introduced and finally a systematic approach to the evaluation of the such algorithm is presented A dynamic programming approach that is a form of bounded enumeration. is presented to perform the shortest-path determination during construction of the a-network

*Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

271

ing approach 175. A.Thomas 1971 A branch A Mason, Colin and bound heuristic procedure, method L Moodie 176. Jerome D.Wiest

1967

Cost bounding procedures are augmented by dominance relationships presented as theorems. Initial feasible schedules are generated using a heuristic scheduling rule. Both heuristics rule and the branch and bound algorithm have been programmed for the computer Describe a computer model capable of scheduling single or multiple projects within theirs constraints

A heuristic method

In table 2 the approaches on RCPSP subject are categorized in different level. as seen most of the approaches are definite and discrete which is a big question that why researchers did not intend to work on other field.

Table 2: Approaches Categories Authors

Year

Approaches Stochastic

Det. Reactive

Proactive

Number of approaches Discrete

Stochastic

Fuzzy

1.

2.

3.

D.C. Paraskevopoulos et al.

2012





Chen Fang, Ling Wang

2012





Mohamed Haouari et al

2012





4.

Ling Wangn, ChenFang

2012

5.

Thomas S. Kyriakidis et al.

2012

6.

Koorush Ziarati et al.

2011

7.

2011

8.

Shu-Shun Liu, Chang-Jung Wang FilipDeblaere et al.

9.

SiamakBaradaran et al.

2011





10.

Mohammad Ranjbar et al.

2011





11.

R. Čapek et al.

2011





12.

MariemTrojet et al.

2011





13.

Ling Wang, Chen Fang

2011





14.

2011





15.

José Coelho, Mario Vanhoucke Ruey-Maw Chen

2011





16.

Shanshan Wu et al.

2011





17.

Mahdi Mobini et al.

2011





18.

OumarKone et al.

2011





19.

LucioBianco, MassimilianoCaramia

2011





20.

Agustín Barrios et al.

2011





21.

AnuragAgarwal et al.

2011





22.

Francisco Ballestıín, Rosa Blanco FilipDeblaere et al.

2011





23.

272

 

Mohammad Abdolshah

 









2011

2011

Integrated









24.

TarunBhaskar et al.

2011

25.

GrzegorzWaligóra

2011





26.

2011





2011





28.

José Fernando Gonçalves et al.

Vincent Van Peteghem,Mario Vanhoucke Reza Zamani

2011





29.

Olivier Lambrechts et al.

2011

30.

BehzadAshtiani et al.

2011

31.

Francisco Ballestín et al.

2011





32.

Jie Zhu et al.

2011





33.

Mohammad Jaberi

2011





34.

Hong Zhang,Feng Xing

2010

35.

E. Klerides, E. Hadjiconstantinou Qi Hao et al.

2010 2010





Svio B. Rodrigues, Denise S. Yamashita SondaElloumi , Philippe Fortemps AnisKooli et al.

2010





2010





2010





2010





41.

Jairo R. Montoya-Torres et al.

SiamakBaradaran et al.

42.

Moslem Shahsavar et al.

2010





43.

2010





44.

C.U. Fündeling, N. Trautmann Ruey-Maw Chen et al.

2010





45.

Wang Chen et al.

2010





46.

Vincent Van Peteghem , Mario Vanhoucke E. Klerides, E. Hadjiconstantinou Andrei Horbach

2010





2010





49.

Angela H. L. Chen & Chiuh-Cheng Chyu

2010





50.

Wang Hong et al.

2010





51.

Reza Zamani

2010





52.

JiupingXu&Zhe Zhang

2010

53.

Isabel Correia et al.

2010

54.

Wang Xianggang& Huang Wei H. R. Yoosefzadeh et al.

2010

27.

36.

37.

38.

39.

40.

47.

48.

55.







 







 

 

 

 



2010

2010

 

2010



 

*Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

273

56.

Angelo Oddi et al.

2010





57.

2010





60.

Doreen Krüger&Armin Scholl YuryNikulin&Andreas Drexl Tyson R. Browning &Ali A. Yassine Fawaz S. Al-Anzi et al.

61.

M. Ranjbar& F. Kianfar

2010

62.

N. Damak et al.

2009





63.

PengWuliang, Wang Chengen Liang Yan et al.

2009





2009





66.

Po-Han Chen,Seyed Mohsen Shahandashti Po-Han Chen,HaijieWeng

2009





67.

VikramTiwari et al.

2009





68.

2009





69.

Jiaqiong Chen, Ronald G. Askin Mohammad Ranjbar et al.

2009





70.

Antonio Lova et al.

2009





71.

J.J.M. Mendes et al.

2009





72.

Kuo-Ching Ying et al.

2009





73.

Wu Yu et al.

2009





74.

JörgHomberger

2011





75.

C.C. Chyu&Z.J. Chen

2009





76.

M. D. Mahdi Mobini et al.

2009





77.

2009





2008





79.

Christian Artigues& Cyril Briand Shu-Shun Liu,Chang-Jung Wang Nai-Hsin Pan et al.

2008





80.

Stijn Van de Vonder et al.

2008

81.

Francisco Ballestín et al.

2008





82.

R. Alvarez-Valdes et al.

2008





83.

J.F. Gonçalves et al.

2008





84.

Hédi Chtourou & Mohamed Haouari Haitao Li, Keith Womer

2008

2008

87.

LuongDuc Long, ArioOhsato Mohammad Ranjbar

2008





88.

Marek Mika et al.

2008





58.

59.

64.

65.

78.

85.

86.

274

2010



2010 2010



Mohammad Abdolshah





 

2009

2008























Supply chain configuration problem (SCCP) under resource constraints

89.

Mario Vanhoucke

2008





90.

Shih-Tang Lo et al.

2008





91.

Vicente Valls et al.

2008





92.

L.-E. Drezet, J.-C. Billaut

2008





93.

Mario Vanhoucke, Dieter Debels

2008





94.

B. Jarboui et al.

2008





95.

Sanjay Kumar Shukla et al.

2008

96.

Olivier Lambrechts et al.

2008

97.

Olivier Liess&Philippe Michelon A. A. Lazarev& E. R. Gafarov MajidSabzehparvar& S. Mohammad SeyedHosseini Jean Damay et al.

2008





2008





2008





2007





ShahramShadrokh, FereydoonKianfar Mohammad R. Ranjbar, FereydoonKianfar JirachaiBuddhakulsomsi, David S. Kim Stijn Van de Vonder et al.

2007





2007





Jacques Carlier, Emmanuel Ne´ron M. Rabbani et al.

2007

VéroniqueBouffard&Jacqu es A. Ferland RinaAgarwal et al.

2007

2006

112.

Lin-Yu Tseng, Shih-Chieh Chen Amir Azaron, Reza Tavakkoli-Moghaddam Luciano LessaLorenzoni et al.

Dieter Debels et al.

113.

114.

98.

99.

100.

101.

102.

103.

104.

105.

106.

107.

108.

109.

110.

111.

 

2007







2007













2007

RCPSP-TOC

 



2007









2006





2006



2006





Hong Zhang et al.

2006





John-Paris Pantouvakis, Odysseus G. Manoliadis Guidong Zhu et al.

2006





2006





I-Tung Yang, Chi-Yi Chang Marek Mika et al.

2005

2005

119.

M.A. Al-Fawzan, Mohamed Haouari KwanWoo Kim

120.

Tamás Kis

2005

115.

116.

117.

118.

2005









 

2005

 



 

*Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

275

121.

2005





2004





123.

Sophie Demassey&Chiristian Artigues Krzysztof Fleszar, Khalil S. Hindi Juite Wang

124.

I.E. Diakoulakis et al.

2004





125.

Reza Zamani

2004





126.

ChristophMellentien

2004





127.

2004





2004





129.

Vicente Valls & Francisco Ballestín Philippe Baptiste& Sophie Demassey Mireille Palpant et al.

2004





130.

A. Lim et al.

2004

131.

Christian Artigues et al.

2003

132.

Vicente Valls et al.

2003





133.

J. Carlier, E. N_eron

2003





134.

2003

135.

DimitriGolenko-Ginzburg et al.

Roland Heilmann

2003





136.

Kwan Woo Kim et al.

2003





137.

2003





138.

M Kamrul Ahsan & De-Bi Tsao J Alcaraz et al.

2003





139.

Chiu-Chi Wei et al.

2002

140.

2002

141.

Amedeo Cesta& Angelo Oddi A Sprecher

142.

Mario Vanhoucke et al.

2001





143.

Birger Franck et al.

2001





144.

Gunduz Ulusoy et al.

2001





145.

Sönke Hartmann

2001





146.

A. Kimms

2001





147.

J. Prashant Reddy et al.

2001





148.

Antonio Lova & Pilar Tormos Joanna Jozefowska et al.

2001





2001





2001





151.

Pilar Tormos & Antonio Lova Roland Heilmann

2001





152.

Gary Knotts et al.

2000





153.

Christoph Schwindt & Norbert Trautmann Erik Demeulemeester et al.

2000

122.

128.

149.

150.

154.

276

2004





Mohammad Abdolshah













RCPSP-TOC

 



2002

2000





 



 

155.

Ulrich Dorndrof et al.

2000





156.

Arno Sprecher

2000





157.

Tam P. W. M. & E. Palaneeswaran Shue Li-Yen,RezaZamani

1999





1999





1998





160.

Paul R. Thomas & Said Salhi Abel A.Fernandez

161.

Aristide, Mingozzi et al.

1998





162.

Dan Zhu&RemaPadman

1997





163.

1997





164.

Rainer Kolisch & Andreas Drexl Arno Sprecher et al.

1997





165.

Kedar S. Naphade et al.

1997





166.

Moizuddin, Mohammed;Selim, Shokri Z Erik Demeulemeester, Willy S L Herroelen Kum-Khiong, yang

1997





1997





1996





170.

Oya Icmeli & S. Selcuk Erenguc F.Brian Talbot

1982





171.

Jan Weglarz

1981





172.

Dale F Cooper

1976





173.

Arne Thesen

1976





174.

Davis, Edward W;Heidorn, George E A.Thomas Mason, Colin L Moodie Jerome D.Wiest

1971





1971





1967





158.

159.

167.

168.

169.

175.

176.

1998



1996







6. Conclusion Every day, better usage of the organizational resources such as machinery, human resource and materials are given more attentions. With existence of resource constraints, planning for achieving the goals of the contracts in projects, and at the top of them, time obligations, become more important. This paper described models and approaches in literature of project scheduling by considering resource constraints and the described models in literature that consist of more than 200 published articles in well known journals, are collected and provided in forms of a codified table. We tries to categorize models appropriately in this paper and surveys the proposed solutions for them by researches. By considering the increasing deployment of using planning and controlling project methods in organizations, factories and workshops such as powerhouse equipment construction projects and any kind of executive projects in various *Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

277

industries and totally, where ever there is usage of planning and controlling project, and by considering the diversity of organizations and factories, can identify the required model by considering the proposed criterions at beginning of this paper and researchers find the gaps in literature and try to fill them. We hope that the proposed solutions are reliable resources and references for gathering more information about different existence solutions in RCPSP literature.

7. References Agarwal A, Colak S, Erenguc S. A Neurogenetic approach for the resource-constrained project scheduling problem. Computers & Operations Research 2011; 38: 44–50.

Ahsan M.K, Tsao D.-B. Solving Resource-Constrained Project Scheduling Problems with bi-criteria heuristics search techniques. Journal of Systems Science and Systems Engineering 2003; 12(2): 190-203.

Al-Anzi F.S, Al-Zamel K, Allahverdi A. Weighted Multi-Skill Resources Project Scheduling. Journal of Software Engineering & Applications 2010; 3: 1125-1130.

Alcaraz J, Maroto C, Ruiz R. Solving the Multi-Mode Resource-Constrained Project Scheduling Problem with genetic algorithms. Journal of the Operational Research Society 2003; 54: 614–626.

Al-Fawzan M.A, Haouari M.A bi-objective model for robust resource-constrained project scheduling.

International Journal Production Economics 2005; 96: 175–187.

Alvarez-Valdes R, Crespo E, Tamarit J.M, Villa F. GRASP and path re-linking for project scheduling under partially renewable resources.European Journal of Operational Research 2008; 189: 1153– 1170.

Artigues C, Briand C. The resource-constrained activity insertion problem with minimum time lags.

Journal of Scheduling 2009; 12: 447-460.

Artigues C, Michelon P, Reusser S. Insertion techniques for static and dynamic resource-constrained project scheduling. European Journal of Operational Research 2003; 49: 249–267.

Ashtiani,Leus R, Aryanezhad M.B. New competitive results for the stochastic resource-constrained project scheduling problem: exploring the benefits of pre-processing. Journal of Scheduling 2011; 14: 157–171.

Azaron A, Tavakkoli-Moghaddam R. A multi-objective resource allocation problem in dynamic PERT networks. Applied Mathematics and Computation 2006; 181: 163–174.

Ballestín F, Blanco R. Theoretical and practical fundamentals for multi-objective optimization in resource-constrained project scheduling problems. Computers & Operations Research 2011; 38 : 51–62.

Ballestín F, Valls V, Barrios A. An evolutionary algorithm for the resource-constrained project scheduling problem with minimum and maximum time lags. Journal of Scheduling 2011; 14:391– 406.

Ballestín F, Valls V, Quintanilla S. Pre-emption in resource-constrained project scheduling. European Journal of Operational Research 2008; 189: 1136–1152 .

Baptiste P, Demassey S. Tight LP bounds for resource constrained project scheduling. OR Spectrum 2004: 26: 251–262.

Baradaran S, FatemiGhomi S.M.T, Ranjbar M, Hashemin S.S. Multi-mode renewable resourceconstrained allocation in PERT networks. Applied Soft Computing 2011.

278

Mohammad Abdolshah

Baradaran S, FatemiGhomi S.M.T, Ranjbar M, Hashemin S.S. A hybrid scatter search approach for scheduling problem in PERT-type networks. Advances in resource-constrained project Engineering Software 2010; 41: 966–975.

Barrios A, Ballestín F, Valls V.A double genetic algorithm for the MRCPSP/max. Computers& Operations Research 2011; 38: 33–43.

Browning T.R, Yassine A.A. A random generator of resource-constrained multi-project network problems. Journal of scheduling 2010; 13: 143–161.

Brucker, P., Schoo, A. and Thiele, O. A branch and bound algorithm for the resource constrained project scheduling problem.

European Journal of Operation Research 1998,17: 143-158. Čapek R, Šůcha P, Hanzálek Z. Production scheduling with alternative process plans. European Journal of Operational Research 2011.

Cesta A,Oddi A.A Constraint-Based Method for Project Scheduling with Time Windows. Journal of Heuristics 2002; 8: 109–136.

Chen J, Askin R. G. Project selection, scheduling and resource allocation with time dependent returns.

European Journal of Operational Research 2009; 193: 23–34.

Chen P.-H, Shahandashti S.M. Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Automation in Construction 2009; 18: 434–443.

Chen P.-H, Weng H. A two-phase GA model for resource-constrained project scheduling. Automation in Construction 2009; 18: 485–498.

Chen R.-M. Particle swarm optimization with justification and designed mechanisms for resourceconstrained project scheduling problem. Expert Systems with Applications 2011; 38: 7102– 7111.

Chen R.-M, Wu C.-L, Wang C.-M, Lo S.-T. Using novel particle swarm optimization scheme to solve scheduling problem in PSPLIB. Expert Systems with Applications 2010; resource-constrained 37: 1899–1910.

Chtourou H, Haouari M. A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling.

Computers & Industrial Engineering 2008; 55: 183–194.

Chyu C.-C,Chen Z.-J. Scheduling jobs under constant period-by-period resource availability to maximize date. The International Journal of Advanced Manufacturing Technology project profit at a due 2009; 42:569–580.

Coelho J, Vanhoucke M. Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers. European Journal of Operational Research 2011; 213: 73–82.

Coelho, J. and Tavares, L. Comparative analysis of meta–heuristics for the resource constrained project scheduling problem.

Technical report, Department of Civil Engineering, Institute Superior Tecnico, Portugal. 2003.

Damak N, Jaboui B, Siarry P, Loukil T. Differential evolution for solving multi-mode resourceconstrained project scheduling problems. Computers & Operations Research 2009; 36: 2653 – 2659 Damay J, Quilliot A, Sanlaville E. Linear programming based algorithms for preemptive and nonpreemptive RCPSP. European Journal of Operational Research 2007; 182: 1012–1022.

Davis E.W, Heidorn G.E. An Algorithm for optimal project scheduling under Multiple resource constraints. Management science 1971; 17(12): B803-B816.

Debels D, De Reyck B, Leus R, Vanhoucke M. A hybrid scatter search/electromagnetism meta-heuristic for project scheduling. European Journal of Operational Research 2006; 169: 638–653. *Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

279

Deblaere F, Demeulemeester E, HerroelenW.Proactive policies for the stochastic resource-constrained project scheduling problem. European Journal of Operational Research 2011; 214: 308–316 Deblaere F, Demeulemeester E, Herroelen W. Reactive scheduling in the multi-mode RCPSP. Computers & Operations Research 2011; 38: 63–74.

Deckro, R. F., Winkofsky, E. P., Hebert, J. E. and Gagnon, R., A decomposition approach to multiproject scheduling. European Journal of Operation Research 1991, 51: 110-118.

Demassey S, Artigues C. Michelon P. Constraint-Propagation-Based cutting planes: An application to the resource-constrained project scheduling. Journal on Computing 2005; 17(1): 52-65.

Demeulemeester E, De Reyck, Herroelen W. The discrete time/resource trade-off problem in project networks: a branch-and- bound approach. Institute of Industrial Engineers Transactions 2000; 32: 1059–1069.

Demeulemeester E.L, Herroelen W.S. A branch and bound procedure for the generalized resource scheduling problem. Operations Research 1997; 45(2): 201-212. constrained project Demeulemeester, E. and Herroelen, W., New benchmark results for the resource-constrained project scheduling problem.

Management Science 1997, 43: 1485-1492.

Diakoulakis I.E, Koulouriotis D.E, Emiris D.M. Resource Constrained Project Scheduling Using Evolution Strategies. Operational Research: An International Journal 2004; Drexl A, Grunewald J. Non-preemptive multi-mode resource-constrained project scheduling.IIE Transaction 1993; 25: 74-81.

Drezet L.-E, Billau J.-C. A project scheduling problem with labor constraints and time-dependent activities requirements. International Journal Production Economics 2008; 112 : 217–225.

Elloumi S, Fortemps P. A hybrid rank-based evolutionary algorithm applied to multi-mode resourceconstrained project scheduling problem. European Journal of Operational Research 2010; 205: 31– 41.

Franck B, Neumann K, Schwindt C. Truncated branch-and-bound, schedule-construction, and scheduleimprovement procedures for resource-constrained project scheduling. OR Spectrum 2001; 23: 297–324.

Fang C, Wang L. An effective shuffled frog-leaping algorithm for resource-constrained project Operations Research 2012; 39: 890-901. scheduling problem. Computers & Fleszar K, Hindi K.S. Solving the resource-constrained project scheduling problem by a variable neighborhood search. European Journal of Operational Research 2004; 155: 402–413.

Fernandez A.A, Armacost R.L, Pet-Edwards J.J. Understanding simulation solutions to resource constrained project scheduling problems with stochastic task durations. Engineering Management Journal 1998; 10(4): 5-13.

Fündeling C.-U, Trautmann N. A priority-rule method for project scheduling with work-content constraints. European Journal of Operational Research 2010; 203: 568–574.

Golenko-Ginzburg D, Gonik A, Laslo Z. Resource constrained scheduling simulation model for alternative stochastic network projects.Mathematics and Computers in Simulation 2003; 63: 105–117.

Gonçalves J.F, Resende M.G.C, Mendes J.J.M. A biased random-key genetic algorithm with forwardbackward improvement for the resource constrained project scheduling problem. Journal of Heuristics 2011; 17:467–486.

Gonçalves J.F, Mendes J.J.M, Resende M.G.C. A genetic algorithm for the resource constrained multiproject scheduling problem. European Journal of Operational Research 2008; 189: 1171–1190.

Hadju M. Network scheduling techniques for construction management. Kluwer Academic Publishers 1997.

280

Mohammad Abdolshah

Hartmann S. Project Scheduling with Multiple Modes: A Genetic Algorithm. Annals of Operations Research 2001; 102: 111–135.

Hao Q, Shen W, Xue Y, Wang S. Task network-based project dynamic scheduling and schedule coordination. Advanced Engineering Informatics 2010; 24: 417–427.

Heilmann R. A branch-and-bound procedure for the multi-mode resource-constrained project scheduling problem with minimum and maximum time lags. European Journal of Operational Research 2003; 144: 348–365.

Heilmann R. Resource–constrained project scheduling: a heuristic for the multi–mode case. OR Spectrum 2001; 23: 335–357.

Herroelen, W., De Reyck, B. and Demeulemeester, E., Resource-constrained project scheduling: A survey of recent developments. Computers Operations Research 1998, 25(4): 279-302.

Hong W, Tongling L, Dan L. Efficient Genetic Algorithm for Resource-Constrained Project Scheduling Problem. Transactions of Tianjin University 2010; 16: 376-382.

Homberger J. A (μ, λ)-coordination mechanism for agent-based multi-project scheduling. OR Spectrum 2012; 34: 107-132.

Horbach A. A Boolean satisfiability approach to the resource-constrained project scheduling problem.

Annals of operations Research 2010; 181: 89–107.

Icmeli O, Erenguc S.S. A branch and bound procedure for the resource constrained project scheduling problem with discounted cash flow. Management science 1996; 42(10): 1395-1408.

Icmeli, O. and Rom, W. O., Solving the resource-constrained project scheduling problem with optimization subroutine library. Computers and Operation Research 1996, 23: 801-817.

Jaberi M. Resource constrained project scheduling using mean field annealing neural networks.

International Journal of Multidisciplinary Science and Engineering 2011; 2(7): 6-12.

Jarboui B, Damak N, Siarry P, Rebai A. A combinatorial particle swarm optimization for solving multimode resource-constrained project scheduling problems. Applied Mathematics and Computation 2008; 195: 299–308.

Kim K.W, Yun Y.S, Yoon J.M, Gen M, Yamazaki G. Hybrid genetic algorithm with adaptive abilities for resource-constrained multiple project scheduling. Computers in Industry 2005; 56: 143–160.

Kim K.W, Gen M, Yamazaki G. Hybrid genetic algorithm with fuzzy logic for resource-constrained project scheduling. Applied Soft Computing 2003; 2(3): 174–188.

Kimms A. Maximizing the Net Present Value of a Project Under Resource Constraints Using a Lagrangian Relaxation Based Heuristic with Tight Upper Bounds. Annals of Operations Research 2001; 102: 221–236.

Kis T. Project scheduling: a review of recent books. Operations Research Letters 2005; 33: 105 –110.

Kis T. A branch-and-cut algorithm for scheduling of projects with variable-intensity activities.

Mathematical Programming: Series A and B 2005; 103(3): 515-539.

Klerides E, Hadjiconstantinou E. A decomposition-based stochastic programming approach for the project scheduling problem under time/cost trade-off settings and uncertain durations.

Computers & Operations Research 2010; 37: 2131–2140 .

Knotts G, Dror M, Hartman B.C. Agent-based project scheduling. Institute of Industrial Engineers Transactions2000; 32: 387401.

Kolisch R, Hartmann S. Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research 2006; 174: 23–37. *Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

281

Kolisch R, Drexl A. Local search for non-preemptive multi-mode resource-constrained project scheduling. IIIE Transactions 1997; 29: 987-999.

Kooli A, Haouari M, Hidri L, Néron E. IP-Based energetic reasoning for the resource constrained project scheduling problem.

Electronic Notes in Discrete Mathematics 2010; 36: 359–366.

Krüger D, Scholl A. Managing and modeling general resource transfers in (multi-)project scheduling. OR Spectrum 2010; 32: 369-394.

Lambrechts O, Demeulemeester E, Herroelen W. Time slack-based techniques for robust project uncertainty. Annals of operations Research 2011; 186:443–464. scheduling subject to resource Lambrechts O, Demeulemeester E, Herroelen W. Proactive and reactive strategies for resourceconstrained project scheduling with uncertain resource availabilities. Journal of Scheduling 2008; 11: 121–136.

Lazarev A. A, Gafarov E. R. On Project Scheduling Problem. Automation and Remote Control 2008; 69(12): 2070-2087.

Li H, Womer K. Modeling the supply chain configuration problem with resource constraints.

International Journal of Project Management 2008; 26: 646–654.

Liess O,Michelon P. A constraint programming approach for the resource-constrained project scheduling problem. Annals of Operations Research 2008; 157: 25–36.

Lim A, Rodrigues B, Thangarajoo R, Ziao F. A Hybrid Framework for Over-Constrained Generalized Resource-Constrained Project Scheduling Problems. Artificial Intelligence Review 2004; 22: 211–243.

Liu S, Wang C. Optimizing project selection and scheduling problems with time-dependent resource constraints. Automation in Construction 2011; 20: 1110–1119 Liu S.-S, Wang C.-J. Resource-constrained construction project scheduling model for profit maximization considering cash flow. Automation in Construction 2008; 17: 966–974.

Li-Yen S, Zamani, R.An intelligent search method for project scheduling problems. Journal of Intelligent Manufacturing 1999; 10 (3-4): 279-288.

Lo S.-T, Chen R.-M, Huang Y.-M, Wu C.-L. Multiprocessor system scheduling with precedence and resource constraints using an enhanced ant colony system. Expert Systems with Applications 2008; 34: 2071–2081.

Lova A, Tormos P, Cervantes M, Barber F. An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes. International Journal Production Economics 2009; 117: 302–316.

Lova A, Tormos P. Analysis of Scheduling Schemes and Heuristic Rules Performance in ResourceConstrained Multi-project Scheduling. Annals of Operations Research 2001; 102: 263–286.

Mendes J.J.M, Gonçalves J.F, Resend M.G.C. A random key based genetic algorithm for the resource constrained project scheduling problem. Computers & Operations Research 2009; 36: 92 – 109.

Merkle, D., Middendorf, M. and Schmeck. H. Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation 2002, 6: 333–346.

Mika M, Waligóra G, Węglarz J. Simulated annealing and tabu search for multi-mode resourceconstrained project scheduling with positive discounted cash flows and different payment models. European Journal of Operational Research 2005; 164: 639–668.

Mingozzi A, Maniezzo V, Ricciardelli S, Bianco L. An exact algorithm for resource-constrained project scheduling problem based on a new mathematical formulation. Management science 1998; 44(5): 714-729.

Mobini M, Mobini Z, Rabbani M. An Artificial Immune Algorithm for the project scheduling problem under resource constraints. Applied Soft Computing 2011; 11: 1975–1982.

282

Mohammad Abdolshah

Mobini M. Rabbani M, Amalnik M.S, Razmi J. Rahimi-Vahed A.R. Using an enhanced scatter search algorithm for a resource-constrained project scheduling problem. Soft Computing 2009; 13: 597610.

Mohring R, Schulz A, Stork F, and Uetz M. Solving project scheduling problems by minimum cut computations. Management science 2003; 46(3): 330-350.

Moizuddin M, Selim S.Z. Project scheduling under limited resources. American Association of Cost Engineers international Transaction 1997; 3: 1-7.

Montoya-Torres J. R, Gutierrez-Franco E, Pirachicán-Mayorga C. Project scheduling with limited resources using a genetic algorithm. International Journal of Project Management 2010; 28: 619– 628.

Naphade K.S, Wu S.D, Storer R.H. Problem space search algorithms for resource-constrained project scheduling. Annals of Operations Research 1997; 70: 307 – 326.

Nonobe, K. and Ibaraki, T. Formulation and tabu search algorithm for the resource constrained project scheduling problem. In C. C. Ribeiro and P. Hansen, Essays and Surveys in Meta-heuristics,.

Kluwer Academic Publishers, 2002, 557– 588.

Oddi,Cesta,Policella N, Smith S.F. Iterative flattening search for resource constrained scheduling. Journal of Intelligent Manufacturing 2010; 21: 17-30.

Palpant M, Artigues C. Michelon P. LSSPER: Solving the Resource-Constrained Project Scheduling Problem with Large Neighborhood Search. Annals of Operations Research 2004; 131: 237–257.

Pan N.-H, Hsaio P.-W, Chen K.-Y. A study of project scheduling optimization using TABU Search algorithm. Engineering Applications of Artificial Intelligence 2008; 21: 1101–1112.

Pantouvakis J.-P, Manoliadis O.G. A practical approach to resource-constrained project scheduling.

Operational Research: An International Journal 2006; 6(3): 299-309.

Paraskevopoulos D.C, Tarantilis C.D, Ioannou G. Solving project scheduling problems with resource constraints via an event listbased evolutionary algorithm. Expert Systems with Applications 2012; 39: 3983–3994.

Peteghem V, Vanhoucke M. Using resource scarceness characteristics to solve the multi-mode resourceconstrained project scheduling problem Journal of Heuristics 2011; 17:705–728.

Rabbani M, FatemiGhomi S.M.T, Jolai F, Lahiji N.S. A new heuristic for resource-constrained project scheduling in stochastic networks using critical chain concept. European Journal of Operational Research 2007; 176: 794–808.

Ranjbar M, Khalilzadeh M, Kianfar F, Etminani K. An optimal procedure for minimizing total weighted resource tardiness penalty costs in the resource-constrained project scheduling problem. Computers & Industrial Engineering 2011.

Ranjbar M, Kianfar F. Resource-Constrained Project Scheduling Problem with Flexible Work Problems: A Genetic Algorithm Approach. Transaction E: Industrial Engineering 2010; 17(1): 25-35.

Ranjbar M, De Reyck B, Kianfar F. A hybrid scatter search for the discrete time/resource trade-off problem in project scheduling.

European Journal of Operational Research 2009; 193: 35–48.

Ranjbar M. Solving the resource-constrained project scheduling problem using filter-and-fan approach. and Computation 2008; 201: 313–318.

Applied Mathematics Ranjbar M. R, Kianfar F. Solving the discrete time/resource trade-off problem in project scheduling with genetic algorithms.

Applied athematics and Computation 2007; 191: 451–456.

Reddy J. P, Kumanan S, Chetty O.V.K. Application of Petri Nets and a Genetic Algorithm to Multi-Mode Multi-Resource Constrained Project Scheduling. The International Journal of Advanced Manufacturing Technology 2001; 17:305-314. *Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

283

Rodrigues S. B, Yamashita D. S. An exact algorithm for minimizing resource availability costs in project Journal of Operational Research 2010; 206: 562–568. scheduling. European Schwindt C, Trautmann N. Batch scheduling in process industries: an application of resource–constrained project scheduling. OR Spectrum 2000; 22: 501–524.

Shadrokh S, Kianfar F. A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty.European Journal of Operational Research 2007; 181: 86–101.

Shahsavar M, AkhavanNiaki S.T, Najafi A.A. An efficient genetic algorithm to maximize net present value of project payments under inflation and bonus–penalty policy in resource investment problem. Advances in Engineering Software 2010; 41: 1023–1030.

Sprecher A. Network decomposition techniques for resource-constrained project scheduling. Journal of the Operational Research Society 2002; 53: 405–414.

Sprecher A. Scheduling resource-constrained projects competitively at modest memory requirements.

Management science 2000; 46(5): 710-723.

Sprecher A, Hartmann S, Drexl A. An exact algorithm for Project scheduling with multiple mode. OR Spectrum 1997; 19: 195- 203.

Shukla S.K, Son Y.J, Tiwari M.K. Fuzzy-based adaptive sample-sort simulated annealing for resourceconstrained project scheduling. The International Journal of Advanced Manufacturing Technology 2008; 36:982–995 Tam P.W.M, Palaneeswaran E. The use of enhanced positional weight method for constrained resource project scheduling. Canadian Journal of Civil Engineering 1999; 26(2): 242-247.

Tareghian, H. R., Farahi, M. H. and Moarrab, M. Solving Resource Constrained Scheduling Problem by Genetic Algorithm. Shahid Chamran Univ. Journal of Science 2007, 16: 24-35 Thesen A. Heuristic scheduling of activities under resource and precedence restrictions. Management Science 1976; 23(4): 412- 422.

Tiwari V, Patterson J.H, Mabert V.AScheduling projects with heterogeneous resources to meet time and quality objectives. European Journal of Operational Research 2009; 193: 780–790.

Tormos P, Lova A. A Competitive Heuristic Solution Technique for Resource-Constrained Project Scheduling. Annals of Operations Research 2001; 102: 65–81.

Thomas P.R, Salhi S. A TABU search approach for the resource constrained project scheduling problem.

Journal of Heuristics 1998; 4: 123–139.

Thomas Mason, A.; Moodie, Colin L., A branch and bound algorithm for minimizing cost in project scheduling, 1971, Management Science;Dec1971 Part 1, Vol. 18 Issue 4, pB-158 Tseng L.-Y, Chen S.-C. A hybrid meta-heuristic for the resource-constrained project scheduling problem.

European Journal of Operational Research 2006; 175: 707–721.

Trojet M, H’Mida F, Lopez P. Project scheduling under resource constraints: Application of the cumulative global constraint in a decision support framework. Computers & Industrial Engineering 2011; 61: 357–363.

Ulusoy G. Four Payment Models for the Multi-Mode Resource Constrained Project Scheduling Problem with Discounted Cash Flows. Annals of Operations Research 2001; 102: 237–261.

Valls V, Ballestín F, Quintanilla S. A hybrid genetic algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research 2008; 185: 495–508.

Valls V, Ballestín F. A Population-Based Approach to the Resource-Constrained Project Scheduling Problem. Annals of Operations Research 2004; 131: 305–324.

Valls, V., Perez, M. A. and Quintanilla, M. S. Justification and RCPSP: A technique that pays. European Journal of Operations Research, 2004, 152: 134-149.

284

Mohammad Abdolshah

Valls V, Quintanilla S,Ballestín F. Resource-constrained project scheduling: A critical activity reordering Journal of Operational Research 2003; 149: 282–301. heuristic. European Van Peteghem V, Vanhoucke M. A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. European Journal of Operational Research 2010; 201: 409–418.

Van De Vonder S, Demeulemeester E, Herroelen W. Proactive heuristic procedures for robust project scheduling: An experimental analysis. European Journal of Operational Research 2008; 189: 723–733.

Van De Vonder S, Ballestín F, Demeulemeester E, Herroelen W. Heuristic procedures for reactive project scheduling. Computers & Industrial Engineering 2007; 52: 11–28.

Vanhoucke M, Debels D. The impact of various activity assumptions on the lead time and resource utilization of resource-constrained projects. Computers & Industrial Engineering 2008; 54: 140– 154.

Waligóra G. Heuristic approaches to discrete-continuous project scheduling problems to minimize the make span. Computational Optimization and Applications 2011; 48: 399–421.

Wang J. A fuzzy robust scheduling approach for product development projects. European Journal of Operational Research 2004; 152: 180–194.

Wang L, Fang C. An effective estimation of distribution algorithm for the multi-mode resourceproblem. Computers & Operations Research 2012; 39: 449-460. constrained project scheduling Wang L, Fang C. An effective shuffled frog-leaping algorithm for multi-mode resource-constrained project scheduling problem.

Information Sciences 2011; 181: 4804–4822.

Wang L, Fang C. A hybrid estimation of distribution algorithm for solving the resource-constrained project scheduling problem 2011.

Wei C.-C, Liu P.-H, Tsai Y.-C. Resource-constrained project management using enhanced theory of constraint. International Journal of Project Management 2002; 20: 561–567.

Yu Wu, Xin-cun Zhuang, Guo-hui Song , Xiao-dong Xu , Cong-xin Li, Solving resource-constrained multiple project scheduling problem using timed colored Petri nets, Journal of Shanghai Jiaotong University (Science), 2009, Volume 14, Issue 6, pp 713-719 Wu S, W H.D, Shukla S.K, Li B. Chaos-based improved immune algorithm (CBIIA) for resourceconstrained project scheduling problems. Expert Systems with Applications 2011; 38: 3387– 3395.

Wuliang P, Chengen W. A multi-mode resource-constrained discrete time–cost tradeoff problem and its genetic algorithm based solution. International Journal of Project Management 2009; 27: 600–609.

Xianggang W, Wei H. Fuzzy Resource-Constrained Project Scheduling Problem for Software Development. Journal of natural sciences 2010; 15(1): 25-30.

Yan L, Jinsong B, Xiaofeng H, Ye J. A heuristic project scheduling approach for quick response to maritime disaster rescue. International Journal of Project Management 2009; 27: 620–628.

Ying K.-C, Lin S.-W, Lee Z.-J. Hybrid-directional planning: improving improvement heuristics for scheduling resource-constrained projects. The International Journal of Advanced Manufacturing Technology 2009; 41:358–366.

Yoosefzadeh H. R, Tareghian H.R, Farahi M.H. Tri-directional Scheduling Scheme: Theory and Computation. Journal of Mathematical Modeling and Algorithms 2010; 9:357–373.

Yu W, Xin-cunZ,Guo-hui S, Xiao-dong X, Cong-xin L. Solving Resource-constrained Multiple Project Scheduling Problem Using Timed Colored Petri Nets. Journal of Shanghai Jiaotong University 2009; 14(6): 713-719. *Corresponding author (Mohammad Abdolshah). Tel: +98-231-4462198 E-mail address: abdolshah@gmail.com. 2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://tuengr.com/V05/0253.pdf.

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Zamani R. A hybrid decomposition procedure for scheduling projects under multiple resource constraints.

Operation research international journal 2011; 11:93–111.

Zamani R. A parallel complete anytime procedure for project scheduling under multiple resource constraints. The International Journal of Advanced Manufacturing Technology 2010; 50:353–362.

Zamani R. An efficient time-windowing procedure for scheduling projects under multiple resource constraints. OR Spectrum 2004; 26: 423–440.

Ziarati K, Akbari R, Zeighami V. On the performance of bee algorithms for resource-constrained project scheduling problem.Applied Soft Computing 2011; 11: 3720–3733.

Zhang H, Xing F. Fuzzy-multi-objective particle swarm optimization for time–cost–quality tradeoff in construction. Automation inConstruction 2010; 19: 1067–1075.

Zhang H, Li H, Tam C.M. Particle swarm optimization for resource-constrained project scheduling.

International Journal of Project Management 2006; 24: 83–92.

Zhu G, Bard J.F, Yu G. A branch-and-cut procedure for the multimode resource-constrained project scheduling problems. Journal on Computing 2006; 18(3): 377-390.

Zhu J, Li X, Shen W. Effective genetic algorithm for resource-constrained project scheduling with limited preemptions. International Journal of Machine Learning and Cybernetics 2011; 2:55–65.

Zhua D, Padman R. Connectionist approaches for solver selection in constrained project scheduling.

Annals of Operations Research 1997; 72: 265 – 298.

Dr.Mohammad Abdolshah received his B.Sc. and M.Sc in Industrial Engineering from Amirkabir University of Technology, Tehran, Iran. Later , he obtained his Ph.D. from University of Putra Malaysia, Malaysia in Industrial Engineering. Dr. Mohammad Abdolshah’s current research interests are Industrial Engineering, Quality Engineering, Control Project, and Project Management.

Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines in the journal’s website.

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2014 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies http://TuEngr.com

Comparison between Analytical Results and Response of the Laboratory-Scaled Truss Bridges under the Moving Car Load a

a

Surachet Charnrit , Weerayut Krahothong , and Chaisak Pisitpaibool a

a*

Department of Civil Engineering Faculty of Engineering, Thammasat University, THAILAND

ARTICLEINFO

Article history: Received 15 May 2014 Received in revised form 21 July 2014 Accepted 25 July 2014 Available online 29 July 2014

Keywords: Laboratory scaled model; Moving car load; Static loading; Unexpected truss behavior.

A B S T RA C T

Two sets of the two traditional types of the laboratory-scale parallel chorded truss bridges under the moving car loads are conducted. Both steel bars used to assemble the bridges and a miniradio-controlled kid-car used as the applied loads are selected from materials available in the market. The experimental programs address the unexpected rebound behavior of the vertical deflection of a truss model under the low speed of the moving car loads. This behavior, however, cannot be detected by the traditional and numerical analysis methods. The rebound behavior of the model may require further investigation.

2014 INT TRANS J ENG MANAG SCI TECH.

1. Introduction A two-parallel chorded truss is commonly used as a bridge structure, which is built up as the distance for joining two areas at the end supports.

Difference of the geometric

arrangement of its diagonal members converts the applied loading into different manners of the tensile or compressive internal forces causing a bend of the entire truss.A truss span ranging from 9 m to 122 m is economically possible to be selected for forming a bridge structure, although the greater span lengths have occasionally been used (Hibbeler, R. C., 2009). Based on the response of the interaction between the bridge structures and their loads applied, the truss behaviors have been experimentally and analytically investigated by different researchers. *Corresponding author (C.Pisitpaibool). Tel/Fax: +66-2-5643001 Ext.3102. E-mail address: pchaisak@engr.tu.ac.com.

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Bacinskas, et al. (2013)employed the aid of full-scale static and dynamic testing to explore the structural condition and the behaviour of the riveted of a historic narrow-gauge railway steel truss bridge built in 1936. For assessment of bridge capacity, an analytical model was developed from conducting the field load tests. Static and dynamic tests of the bridge using two original engines were performed.

Additionally dynamic tests using impulse

excitation were also investigated. Thestructural responses (stresses, static and dynamic displacements, accelerations, mode shapes, corresponding resonant frequencies and modal damping values) of the bridge superstructure were determined. Investigation has shown that the bridge revealed sufficient capacity for safe operation.

Brunell and Kim (2013) investigated the performance of a steel truss bridges subjected to local damage. The existence of local damage was detected by an indicator called the global safety index of the system based on deflection characteristic. It was emphasized that the development of a repair method capable to address the global redundancy of a damaged truss bridge was required.

Cheng, B., Qian, G., and Sun, H., (2013), used the finite element method to analyze the elastic and elasto-plastic behaviors of trusses consisting of the bowknot/conventional integral joints.

The results expressed that the secondary moments at the member ends and the

sectional maximum stresses of the un-shrunken segments of the truss were significantly reduced by the section-shrinking of the member ends. Conversely, the vertical stiffness and elastic stability of the bowknot truss were deteriorated comparing to the conventional one.

When the steel strength of the shrunken segments had been appropriately improved, the ultimate bearing capacities of axially compressed shrunken members and of Warren trusses with bowknot integral joints were as high as those of uniform members and of conventional trusses, respectively.

The initial main purpose of this study was to set up the basic experimental program of the prototype model to investigate the response of a bridge behavior under the moving load condition. During the verification stage, it was found that the normal speed of the mini radiocontrolled kid-car (applied load) was too fast to detect by the dial gauge. The car was then pulled forward with a very low speed. Due to the circumferential model condition, the unexpected rebound behavior of the vertical deflection of a truss model was found. This behavior is then become the point of interested in this study.

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Surachet Charnrit, Weerayut Krahothong, and Chaisak Pisitpaibool

2. Experimental Study Works on the study of the experimental response of the laboratory-scaled truss bridge under the moving car load were divided into three parts. The first part deals with how to design the truss bridge models, which includes the geometric arrangement of its members and organizing the material to fabricate the model by welding. The second and third parts concern with load device and test procedure, respectively, obtaining from the moving car load.

2.1 Truss Bridge Model Design and Manufacture Two series of the Pratt and Warren types of the Pony truss bridge models were experimental studied. A model was composed of two steel trusses laying in the vertical planes along the two sides of the bridge. Each truss had the longitudinal parallel top and bottom chords as shown in the Figure 1. The truss specimens were named as the Pt1, Wr1, Pt2 and Wr2. The first two letters indicated its type while the last letter indicated the series number. Each truss had a total span of 2.50 m and the horizontal dimension of each diagonal member was 25 cm. The depths of the first and second series were 25 cm and 15 cm, respectively.

10 @ 25 cm = 2.50 m Pt1

25 cm

Wr1

25

Pt2

15 cm

Wr2

15 cm

Figure 1: Truss Bridge Models.

The steel bars used to assemble the bridges were intended to select from the smallest size of the materials available in the market. The longitudinal parallel top and bottom chords were made from the 4/8 tube bars, which inner and outer diameters were 18.9 mm and 21.7 mm, respectively.

The vertical and diagonal members were the SD24-RB6 reinforcing steel,

*Corresponding author (C.Pisitpaibool). Tel/Fax: +66-2-5643001 Ext.3102. E-mail address: pchaisak@engr.tu.ac.com.

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whose diameter was 6 mm which was the smallest size of the commercial reinforcing steel.

In order to stabilize the whole truss system, 6 bars of the SD24-RB6 reinforcing steel were used as the bracings placed at the bottom chords. The main structural assembly of the truss bridge model is shown in the Figure 2. The deck was made from the viva-board of 10 mm thickness.

Figure 2: Truss bridge models in the initially set-up the program.

2.2 Loading Device A mini radio-controlled kid-car was selected as the applied moving loads due to its available in the market with the affordable budget. Various weights had been experimentally applied to the bridge truss models. However, Table 1 shows the car load distribution only for the two extreme cases, which are the minimum case when the car is empty and the maximum case when the car was loaded until the maximum capacity was reached. The maximum capacity used of the kid-car was 24.04 kg.

Table 1: Load distribution of the car model.

Empty car load (kg) Full load (kg) Front wheels Rear wheels Total car load

5.79 5.96 11.75

12.42 11.62 24.04

2.3 Instrumentation and Test Procedure The experiment started with the first series of the Pratt and Warren types (Pt1 and Wr1).

In fact, in the second series it was intended to investigate the behavior of the other types (e.g.,

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Surachet Charnrit, Weerayut Krahothong, and Chaisak Pisitpaibool

the Pratt or K-trusses) of the truss models. Basic types of instrumentation were used to monitor the behavior during the tests. A small load cell was placed under the supported bracing which located at one end of the longitudinal bottom chord. Strain gauges were placed at some middle bars to measure the internal forces. The bridge deflection was measured at mid-span underneath the longitudinal bottom chord using a displacement transducer. In the beginning of the test, a dial gauge was set aside the displacement transducer, as shown in the Figure 3, to verify whether the obtained deflection was reasonable.

Figure 3: Test Model and Experimental Setup.

During the verification stage, the mini radio-controlled kid-car was pulled forward with a very low speed (0.011 m/s) to allow the vertical deflection of the truss could be detected by the dial gage. Results from the first series revealed the unexpected rebound behavior of the vertical deflection of the Wr1 truss model (details are shown in the subsequence section). The vertical deflection behavior was then become the point of interested in this study.

The direction of the second series of the experiment had been changed aimed to validate the behavior of the same types of Pratt and Warren trusses with the different dimension. The specimens were then designed as the Pt2 and Wr2 by reducing only the height of the truss bridge. Results obtained from the second series of the Wr2 truss model also indicated the rebound behavior of the vertical deflection at the mid span point. *Corresponding author (C.Pisitpaibool). Tel/Fax: +66-2-5643001 Ext.3102. E-mail address: pchaisak@engr.tu.ac.com.

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3. Analytical Models Complementary to the experimental investigation, analytical models were conducted on the laboratory scaled truss bridge behavior. Two methodologies of the analytical models were selected. This included the traditional method of virtual work and numerical method using the available commercial finite element software.

3.1 Traditional Analysis (Method of Virtual Work) By applying the virtual work for the coplanar truss system, the weight of the car were considered as a row of two concentrated live loads and applied at the bottom chord of the truss. The row of loads was referred as the Car loads F1 and F2 as shown in the Figure 4. v

x

Car load

78 cm F2 P3 P2

x 78 cm F2

F1 P1

P3

Equivalent car load

(a) Pt1 and Pt2 Models

v Car load F1

P2

P1

Equivalent car load

(b) Wr1 and Wr2 Models

Figure 4: Car Load Distribution for 2-D Analysis.

To determine the internal forces developed in each member, the two important assumptions for analysis a truss needs to be included (Hibbeller., R. C., and Yap., K. B., 2012). The first assumption states that the truss members are joined together by smooth pins.

The second one requires that all loading are applied at the joint. This assumption could be satisfied by allowing the front wheel to be placed at any joint of the truss. The Car loads F1 could be directly transformed to the Equivalent car load P1 as shown in the Figure 4.

However, the Car loads F2 might need to apply the linear interpolation function to transform into the Equivalent car load P2 and P3.

Once the internal forces are obtained, many method of structural analysis can be performed to find the vertical deflection at the mid span of the truss bridge analytical models.

In the case of the virtual work method (Laible, J. P., 1985), the displacement of a truss joint can be determined from direct application of the following equation.

292

1â&#x2C6;&#x2122;â&#x2C6;&#x2020;=

â&#x2C6;&#x2018; pâ&#x2C6;&#x2122;P đ??´đ??¸

L

Surachet Charnrit, Weerayut Krahothong, and Chaisak Pisitpaibool

(1),

Where â&#x2C6;&#x2020; = external joint displacement, p = internal virtual normal force caused by the external virtual unit load, P = internal normal force member caused by the real load, L = length of a member, A= cross-sectional area of a member, and E = modulus of elasticity of a member.

Results obtained from the method of virtual work of the four models (e.g., Pt1, Wr1, Pt2 and Wr2) provide the similar manner. The rebound behaviour of the Wr1 and Wr2 models could not be detected by the traditional analysis.

Figure 5: Comparison of vertical deflection history at mid-span of Pt1 and Wr1

3.2 Numerical Analysis (Finite Element Method) Finite element method, the static analysis of the simple 2-D truss analysis, was performed aiming to verify whether the unexpected rebound of the Wr1and Wr2 models could be detected. A commercial software was selected for this purpose. Since the geometry is very simple (only truss members and joints), nodes and elements (two-dimensional truss element) were directly created by the software. The areas of the top and bottom chords were *Corresponding author (C.Pisitpaibool). Tel/Fax: +66-2-5643001 Ext.3102. E-mail address: pchaisak@engr.tu.ac.com.

2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online available at http://tuengr.com/V05/0287.pdf.

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0.89 cm2, while the areas of the diagonal and vertical members were 0.283 cm2. E = 2.04x106 ksc. The parameter of interested and presented in this paper is only the vertical deflection at the mid-span point. The car load distribution applied in this case followed the one presented in the Figure 3.

Figure 6: Comparison of vertical deflection history at mid-span of Pt2 and Wr2.

4. Results Figure5 shows the comparison of the typical mid-span deflection obtained from the First series, Pt1 and Wr1. Most lines behave in such a normal manner, except the blue lines of the Wr1, which represents results from the laboratory. The blue lines of Wr1 appears that the mid-span deflection slightly rebound when the front wheel is approximately located at the distances ranged from 1.25 m to 2.25.

Figure 6 shows the comparison of the typical mid-span deflection obtained from the Second series, Pt2 andWr2. Most lines behave in such a normal manner equivalent to the results from the First series. The blue lines of the of Wr2, which represents results from the laboratory model, also appears that the mid-span deflection slightly rebound when the front

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Surachet Charnrit, Weerayut Krahothong, and Chaisak Pisitpaibool

wheel is approximately located at the distances ranged from 1.25 m to 2.25 Although the rebound behavior of the Warren type truss bridge (Wr1 andWr2) is in a small magnitude, this response is unexpected to be found and cannot be detected by the elastic static analysis, such as the virtual work and finite element. Static analysis is chosen since the car is moved by pulling forward with a very low speed (0.50 m per 45 second or 0.011 m/s)

5. Discussion The investigation indicated that the rebound of the vertical deflection obtained from the experiment of the Warren truss models could not be taken into account by the traditional and numerical analysis procedures.

Several sources addressing below may be the source causing this behavior.

1.

The geometric arrangement of the diagonal members especially the two bars which form the liked A-shape at the mid-span of the bridge.

2. The spacing of the bracing under the Warren is quite large comparing with those of the Pratt truss. Some amount of applied load may be directly transferred to the joints that far from the mid span but close to the both ends.

3. The combination between the truss loads and the truss weight used in this investigation.

It should be noted that when the position of front wheel is approximately 1 m, the rear wheel just be located on the desk. In addition, when the distance is of approximately 2.75 m, the front wheel just get off from the bridge desk. This means the rebound deflection occurs when all wheels are on the bridge. Since the bridge span (2.50 m) is not significant greater than the span length (0.78 m) of the car wheels, when the car is at the mid-point the distance from each wheels to the nearer supports are very small (0.86 m-by symmetry). With the combination of the geometric arrangement of the members in the A-shape at mid span, most of loads may be directly distributed to both sides of the supports instead of the mid-span.

This reduces the load at the mid span resulting to the lesser deflection and consequently the rebound of the behavior.

In order to find the exactly parameters affecting this behavior, future study is required.

Moreover, other similar models, such as the Warren truss with the V-shape at mid-span, may need to be constructed and performed the comparison of their response. *Corresponding author (C.Pisitpaibool). Tel/Fax: +66-2-5643001 Ext.3102. E-mail address: pchaisak@engr.tu.ac.com.

2014. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 5 No.4 ISSN 2228-9860 eISSN 1906-9642. Online available at http://tuengr.com/V05/0287.pdf.

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6. Conclusion Two series of the laboratory-scaled parallel chorded truss bridges under the moving car loads were conducted. Each series of the experiments included two traditional types of the steel bridge trusses, which were in the form of the Pratt and Warren types. A mini radiocontrolled kid-car was used as the loads applied on the bottom parallel chords of the models with a low speed. The experimental programs addressed the unexpected rebound behavior of the vertical deflection of the Warren truss models under the low speed of the moving car loads. This behavior, however, cannot be detected by the traditional and numerical analysis methods. Further study is required to clarify the parameter influencing the rebound behavior of the Warren truss model.

7. Acknowledgements The authors gratefully acknowledge the contributions of Mr. Worapoj and Mr. Prothompong for their help in providing some instrumentation for this study.

8. References Bacinskas, D., Kamatis, Z., Jatulis, D., and Kilikevicius, A., (2013), Field Testing of Old Narrow-Gauge Railway Steel Truss Bridge,Procedia Engineering, 57 (2013) 136-43.

Brunell, G. and Kim, Y.J. (2013), Effect of Local Damage on the Behavior of a laboratoryscale steel truss bridge. Engineering Structures, 48(2013), 281-291.

Cheng, B., Qian, G., and Sun, H., (2013), Steel Truss Bridges with Welded Box-Section Members and Bowknot Integral Joints, Part I: Linear and Non-Linear Analysis, Journal of Constructional Steel Research, 80 (2013) 465-474.

Hibbeler, R. C., 2012, Mechanics for Engineers â&#x20AC;&#x201C; Statics, Prentice Hall, Pearson Education South Asia Pte Ltd, Singapore.

Laible, J. P., 1985, Structural Analysis, CBS College Publishing, New York.

Surachet Charnrit is a student of Department of Civil Engineering, Faculty of Engineering at Thammasat University. His interests are in the areas of construction technology and management.

Weerayut Krahothong is a student of Department of Civil Engineering, Faculty of Engineering at Thammasat University. He is interested in applications of technology in construction and management.

Dr. Chaisak Pisitpaibool is an Assistant Professor of Department of Civil Engineering at Thammasat University. He received his B.Eng. and M.Eng. from Khon Khaen University in 1991. He had gained the experience as a lecturer in Chiang Mai University before moving to the Thammasat University. He then continued his PhD study at Nottingham University, UK, where he obtained his PhD in 2003. His research interest encompasses laboratory modeling of structures.

Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines in the journalâ&#x20AC;&#x2122;s website.

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