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International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies IN THIS ISSUE Using DEMATEL Method to Analyze the Causal Relations on Technological Innovation Capability Evaluation Factors in Thai Technology-Based Firms The Effect of the 2,4-Dichlorophenoxy Acetic Acid, Benzyl Adenine and Paclobutrazol, on Vegetative TissueDerived Somatic Embryogenesis in Turmeric (Curcuma var. Chattip) Application of Bender’s Decomposition Solving a Feed–mix Problem among Supply and Demand Uncertainties Securing Bank Loans and Mortgages Using Real Estate Information Aided by Geospatial Technologies

Volume 4 Issue 2 (April 2013) ISSN 2228-9860 eISSN 1906-9642

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Theoretical Investigation of Hetero– Diels–Alder Functionalizations on SWCNT and Their Reaction Properties The Phenomenology of Lamban Tuha: The Local Wisdom of South Sumatra Traditional Architecture

Cover Photo: Marina Bay Sands Building, in Singapore. Photo is taken by Mr. Saranyu SAVASRAT. Photo is used with permission.


2013 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

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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.Phadungsak Ratanadecho (Thammasat University, THAILAND) Professor Dr.Dewan M. Nuruzzaman (Dhaka University of Engineering & Technology, BANGLADESH) Professor Dr. Lutero Carmo de Lima (State University of Ceará, BRAZIL ) Associate Prof.Dr. Prapat Wangskarn (Dean of Faculty of Engineering, Thammasat University, THAILAND) Associate Prof.Dr.Uruya Weesakul (Past-Dean of Faculty of Engineering, Thammasat University, THAILAND )

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.Narin Watanakul (Thammasat University, THAILAND) Associate Prof. Commander Dr.Komsun Suwannarurk (Thammasat University, THAILAND ) Associate Prof.Dr.Peter Kuntu-Mensah (Texas A&M University-Corpus Christi, USA) Associate Prof.Dr. Anchalee Jala (Thammasat University, THAILAND ) Associate Prof. Dr. Masato SAITOH (Saitama University, JAPAN ) Assistant Prof.Dr. Zoe D. Ziaka (International Hellenic University, GREECE ) Associate Prof.Dr. Supornchai Utainarumol (King Mongkut's University of Technology North-Bangkok, THAILAND) Associate Prof.Dr.Chavalit Chaleeraktrakul (Thammasat University, THAILAND ) Associate Prof.Dr.Krittiya Lertpocasombut (Thammasat University, THAILAND ) Associate Prof.Dr. Bovornchok Poopat (King Mongkut's University of Technology Thonburi, THAILAND ) Associate Prof.Dr.Pudit Laksanacharoen (King Monkut's University of Technology North Bangkok, THAILAND) Associate Prof.Dr.K Pianthong (Ubon Ratchathani University, THAILAND) Associate Prof.Dr.Thanaporn Supriyasilp (Chiang Mai University, THAILAND) Associate Prof.Dr. Junji SHIKATA (Yokohama National University, JAPAN) Associate Prof. Dr.Aree Taylor (Thammasat University, THAILAND) Assistant Prof.Dr. Akeel Noori Abdul Hameed (University of Sharjah, UAE) Assistant Prof.Dr. Atch Sreshthaputra (Chulalongkorn University, THAILAND) Assistant Prof.Dr. Rohit Srivastava (Indian Institute of Technology Bombay, INDIA) Assistant Prof.Dr. Watanachai Smittakorn (Chulalongkorn University, THAILAND ) Assistant Prof.Dr. Kitjapat Phuvoravan (Kasetsart University, THAILAND) Assistant Prof.Dr. Khiensak Seangklieng (Thammasat University, THAILAND ) Assistant Prof.Dr. Chainarong Chaktranond (Thammasat University, THAILAND ) Assistant Prof.Dr.Kridayut Chompoming (Thammasat University, THAILAND ) Assistant Prof.Dr. Nopporn Leeprechanon (Thammasat University, THAILAND ) Assistant Prof.Dr. Suphattra KETSARAPONG (Sripatum University, THAILAND ) Assistant Prof.Dr. Sawat Pararach (Thammasat University, THAILAND ) Assistant Prof.Dr. Winai Raksuntorn (Thammasat University, THAILAND ) Assistant Prof.Dr. Watit Pakdee (Thammasat University, THAILAND ) Assistant Prof.Dr. Cattaleeya Pattamaprom (Thammasat University, THAILAND ) Assistant Prof.Dr.Puttipol Dumrongchai (Chiangmai University, THAILAND ) 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.Isares Duchallaya (Thammasat University, THAILAND ) Dr.Bandit Suksawat (King Mongkut's University of Technology North-Bangkok, THAILAND ) Dr. Salahaddin Yasin Baper (Salahaddin University - Hawler, IRAQ ) Dr. Foong Swee Yeok (Universiti Sains Malaysia, MALAYSIA) Dr.Orawan Chunhachart (Kasetsart University Kamphaengsaen Campus, THAILAND ) Dr. Manop Kaewmoracharoen (Chiang Mai University, THAILAND)


2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies Volume 4 Issue 2 (April, 2013) http://TuEngr.com

ISSN 2228-9860 eISSN 1906-9642

FEATURE PEER-REVIEWED ARTICLES

9 Using DEMATEL Method to Analyze the Causal Relations on Technological Innovation Capability Evaluation Factors in Thai Technology-Based Firms 81 9 The Effect of the 2,4-Dichlorophenoxy Acetic Acid, Benzyl Adenine and Paclobutrazol, on Vegetative Tissue-Derived Somatic Embryogenesis in Turmeric (Curcuma var. Chattip) 105 9 Application of Bender’s Decomposition Solving a Feed–mix Problem among Supply and Demand Uncertainties 111 9 Securing Bank Loans and Mortgages Using Real Estate Information Aided by Geospatial Technologies 129 9 Theoretical Investigation of Hetero–Diels–Alder Functionalizations on SWCNT and Their Reaction Properties 145 9 The Phenomenology of Lamban Tuha: The Local Wisdom of South Sumatra Traditional Architecture 157 Contact & Office: 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 THAILAND.

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2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

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

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Using DEMATEL Method to Analyze the Causal Relations on Technological Innovation Capability Evaluation Factors in Thai Technology-Based Firms a

Detcharat Sumrit , and Pongpun Anuntavoranich

a*

a

Technopreneurship and Innovation Management Program, Graduate School, Chulalongkorn University, Bangkok, THAILAND. ARTICLEINFO

A B S T RA C T

Article history: Received 25 September 2012 Received in revised form 28 December 2012 Accepted 14 January 2013 Available online 18 January 2013

This study analyzes the technology innovation capabilities (TICs) evaluation factors of enterprises by applying the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. Based on the literature reviews, six main perspectives and sixteen criteria were extracted and then validated by six experts. A questionnaire was constructed and answered by eleven experts. Then the DEMATEL method was applied to analyze the importance of criteria and the casual relations among the criteria were constructed. The result showed that the innovation management capability perspective was the most important perspective and influenced the remaining perspectives. This work also presents the significant criteria for each perspective.

Keywords: Technology Innovation Capability; TIC evaluation factors; DEMATEL method; Cause and effect relationship.

2013 INT TRANS J ENG MANAG SCI TECH.

1. Introduction Innovation’s importance has continuously increased and aligns with global business growth. Bessant et al., (2005), and Huang (2011) clearly stated that Technological Innovation Capabilities (TICs) play a crucial part in the initiation of firms’ competency and as the source of sustainable competitive advantage. The enterprises, thus, are strongly required the periodical monitoring their TICs and have to continuously strengthen their weak capabilities in order to *Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

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facilitate the competitive advantage. This study mainly focuses on the technological-based firms since they rely significantly on innovation development to pursue their business growth. Although TICs were accepted as a main part of enhancing competitive advantage, TICs assessment is rather complicated due to multi-dimensionality. The measuring indicators of TICs are also diverse and difficult to assess by any single-dimension scale as they involve the interaction among various resources (Chiesa et al., 1998, Hansen, 2001, Guan and Ma, 2003, Burgelman et al., 2004). Guan et al., (2006) defined TICs measurement framework as benchmark audition on the quantitative evaluation based on traditional DEA approach, which relies on both technological capability and critical capabilities in the area of manufacturing, marketing, organization, strategy planning, learning and resources allocation. However, Wang et al., (2008) proposed the evaluation of high-tech firms’ TICs under both quantitative assessment (by applying new fuzzy multi-criteria analytical approach) and qualitative assessment (using five main aspects of capabilities i.e. R&D, innovation decision, marketing, manufacturing and capital).

Wang et al., (2008) viewed that the traditional

multi-criteria were not wholly suitable for TICs assessment. They also stated that the TICs assessment was considered as subjective and ambiguous. To clarify and reduce the subjective and ambiguous information, this study uses both qualitative and quantitative methods. In this study, TICs’ critical evaluation perspectives and criteria as well as the causal relations among them are presented. The result will aid the managements in the determination of the degree of importance of critical factors/ criteria and their influences on these factors. Following this introduction, literature reviews of TICs and DEMATEL method were illustrated in Section 2. Research methodology (including research framework, and the procedure and results) was proposed in Section 3. Discussion and results were conducted in Section 4. Finally, Section 5 drew the conclusion.

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2. Literature Review  2.1 Technological innovation capability (TICs)  TICs was defined as an enterprises’ ability to improve their technological innovativeness in order to create new customer value through the introduction of new products and services, the exploitation of new technologies and the exploration of new skill and competencies (Perdomo-Ortiz et al., 2009, Wang et al., 2008, Huang, 2011). TICs assessments were also included the aspects of multi-dimensionality, complexity, interactive innovation activities with resource allocation to enhance competitive advantage (Wang et al., 2008, Chiesa et al., 1996). Various researchers have developed the technological innovation framework, approaches and components to evaluate a firm’s technological or innovation capabilities. For instance, Baark et al., (2011) classified the assessment of a firm’s TICs into four approaches: (i) the asset approach (Christensen, 1995), (ii) process approach (Chiesa et al., 1996; Burgelman et al., 2004), (iii) output-based (Romijin and Albaladejo, 2002), and (iv) functional approach (Guan and Ma, 2003; Yam. et al., 2004). Yam et al., 2004 developed an audit innovation capability model by using functional approach, which consisted of seven components: learning capability, R&D capability, resource allocation capability, manufacturing capability, marketing capability, organizing capability and strategic planning capability. These studies of technological innovation capability development are basically related to our research in view of providing an overall framework for understanding the importance of such capability. Based on the extensive literature review, overall TICs evaluation factors were concluded in Table 1.

*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

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Table 1: Summary of the perspectives and criteria of TICs’ evaluations Perspectives/ Criteria

Author

Innovation Management Capability (P1) Strategic Management Capability (C1)

Burgelman et al., (2004), O’Regan et al., (2006), Ceylan and Koc (2007), Dobni (2008), Yam et al., (2004), Yam et al., (2011), Türker (2012).

Organization Capability (C2)

Guan et al., (2006), O’Regan et al., (2006), Burgelman et al., (2004), Yam et al., (2004), Yam et al., (2011), Ceylan and Koc (2007), Dobni (2008), Spyropoulou and Kyrgidou (2012), Türker (2012).

Resource Allocation Capability (C3)

Chiesa et al., (1996), Barney and Clark (2007), Burgelman et al., (2004), Guan et al., (2006), Dobni (2008), Wang et al., (2008), Ceylan and Koc (2007), Yam et al., (2011), Spyropoulou and Kyrgidou (2012), Voudouris et al., (2012).

Risk Management Capability (C4)

Amabile et al., (1996), Isaksen et al., (1999), Forsman (2011), Yang (2012).

Collective Learning Capability (P2) Learning Capability (C5)

Guan et al., (2006), Chiva and Alegre (2007), Teece (2007), Alegre and Chiva (2008), Yam et al., (2004), Yam et al., (2011), Camisón and Villar-López (2012).

Absorptive Capacity (C6)

Ceylan and Koc (2007), Zahra and George (2002), Lane and Koka (2006), Camisón and Forés (2010), Forsman (2011), Wonglimpiyarat (2010), Kim et al., (2011), Gebauer et al., (2012), Lin et al., (2012).

Knowledge Management Capability (C7)

Forsman (2011), Yang (2012).

Innovation Sourcing Capability(P3) Network Linkage Capability (C8)

Lin (2004), Chesbrough (2004), Tidd (2006), Kim and Song (2007), Spithoven et al., (2010), Shan and Jolly (2010), Zeng et al., (2010), Huang (2011), Forsman (2011), Mu and Benedetto (2011), Kim et al., (2011), Voudouris et al., (2012).

Technology Acquisition Capability (C9)

Chiesa et al., (1996), Ceylan and Koc (2007), Lee et al., (2009).

Technology Development Capability(P4) R&D Capability (C10)

Guan et al., (2006), Wang et al., (2008), Yam et al., (2004), Yam et al., (2011), Zahra and George (2002), Levitas and Mc Fadyen (2009), Kim et al., (2011), Forsman (2011), Lin et al., (2012).

Project Cross Functional Team Integration

Martins and Terblanche (2003), Lin (2004), Camisón and Forés (2010), Kim et al., (2011), Yam

Capability (C11)

et al., (2011).

Technology Change Management Capability

Jansen et al., (2005), Garrison (2009), Forsman (2011).

(C12) Robustness Product & Process Design Capability (P5) Product Structural Design and Engineering

Chiesa et al., (1996), Christensen (1995), Zhang et al., (2000), De Toni and Nassimbeni (2001),

Capability (C13)

Antony et al., (2002), Nassimbeni and Battain (2003), Lin (2004), Ho et al., (2011).

Process Design and Engineering Capability

Chiesa et al., (1996), Zhang et al., (2000), De Toni and Nassimbeni (2001), Antony et al.,

(C14)

(2002), Nassimbeni and Battain, (2003).

Technology Commercialization Capability (P6) Manufacturing Capability (C15)

Lin (2004), Yam et al.,(2004), Guan et al. (2006), Wang et al.,(2008), Yam et al., (2011), Kim et al., (2011), Yang (2013).

Market Capability (C16)

Lin (2004), Yam et al., (2004), Guan et al., (2006), Dobni (2008), Wang et al., (2008), Yam et al., (2011), Forsman (2011), Mu and Benedetto (2011), Kim et al., (2011).

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Detcharat Sumrit and Pongpun Anuntavoranich


2.2 DEMATEL Method  DEMATEL method was originally developed between 1972 to 1979 by the Science and Human Affairs Program of the Battelle Memorial Institute of Geneva, with the purpose of studying the complex and intertwined problematic group. It has been widely accepted as one of the best tools to solve the cause and effect relationship among the evaluation criteria (Chiu et al., 2006, Liou et al., 2007, Tzeng et al., 2007, Wu and Lee, 2007, Lin and Tzeng, 2009). This method is applied to analyze and form the relationship of cause and effect among evaluation criteria (Yang et al., 2008) or to derive interrelationship among factors (Lin and Tzeng, 2009). Based on Yu and Tseng (2006), Liou, et al., (2007), Tzeng, et al., (2007), Yang, et al., (2008), Wu and Lee (2007), Shieh et al., (2010), the procedure of DEMATEL method is presented below: Step 1:

Step 2:

Step 3:

Gather experts’ opinion and

Calculate the normalized

Derive

calculate the average matrix Z

initial direct-relation

relation matrix T

Step 4: the

total

Calculate the sums of rows and

matrix D

columns of matrix T

Step 5:

Step 6:

Set the

Build a cause and

Is a cause and effect

threshold value

effect relationship

relationship diagram

(α)

diagram

Yes

acceptable?

The final cause and effect relationship

No

Figure 1: The process of the DEMATEL method.

Step 1: Gather experts’ opinion and calculate the average matrix Z A group of m experts and n factors are used in this step. Each expert is asked to view the degree of direct influence between two factors based on pair-wise comparison. The degree to which the expert perceived factor i affects on factor j is denoted as xij. The integer score is ranged from 0 (no influence), 1 (low influence), 2 (medium influence), 3 (high influence), and 4 (very high influence), respectively. For each expert, an n x n non-negative matrix is constructed as Xk =

, where k is the expert number of participating in evaluation process with 1≤ k ≤ m.

Thus, X1, X2, X3,.., Xm are the matrices from m experts. *Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

85


To aggregate all judgments from m experts, the average matrix Z= [zij] is shown below. ∑

zij =

(1)

Step 2: Calculate the normalized initial direct- relation matrix D The normalized initial direct-relation matrix D = [dij], where value of each element in matrix D is ranged between [0, 1]. The calculation is shown below. D = λ * Z,

(2)

[dij]nxn = λ [zij]nxn

(3)

or

where λ = Min

,

(4)

is the powers of matrix D, e.g. D2, D3,…, D∞

Based on Markov chain theory,

guarantees the convergent solutions to the matrix inversion as shown below. lim

= [0]nxn,

(5)

Step 3: Derive the total relation matrix T The total-influence matrix T is obtained by utilizing Eq. (7), in which, I is an n x n identity matrix. The element of tij represents the indirect effects that factor i had on factor j, then the matrix T reflects the total relationship between each pair of system factors. T

= lim (D +

+ …+

= ∑ where ∑

86

= D1 +

+ …+

Detcharat Sumrit and Pongpun Anuntavoranich

(6)


= D (I + D1 +

+ …+

= D (I - D)-1(I - D)(I + D1 +

) + …+

)

= D (I - D)-1(I - Dm) = D (I - D)-1

T

(7)

Step 4: Calculate the sums of rows and columns of matrix T In the total-influence matrix T, the sum of rows and the sum of columns are represented by vectors r and c, respectively. r = [ri]nx1 = ∑

́1xn = ∑

c= where

nx1,

(8)

́1xn ,

(9)

́ is denoted as transposition matrix.

Let ri be the sum of ith row in matrix T. The value of ri indicates the total given both directly and indirectly effects, that factor i has on the other factors. Let cj be the sum of the jth column in matrix T. The value of cj shows the total received both directly and indirectly effects, that all other factors have on factor j. If j = i, the value of (ri + ci) represents the total effects both given and received by factor i. In contrast, the value of (ri-ci) shows the net contribution by factor i on the system. Moreover, when (ri -ci) was positive, factor i was a net cause. When (ri -ci) was negative, factor i was a net receiver (Tzeng et al., 2007; Liou et al., 2007; Yang et al., 2008; Lee et al., 2009). Step 5: Set a threshold value (α) The threshold value

), was computed by the average of the elements in matrix T, as

computed by Eq. (11). This calculation aimed to eliminate some minor effects elements in matrix T. (Yang et al., 2008).

=

∑ N

(10)

where N is the total number of elements in the matrix T. *Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

87


Step 6: Build a cause and effect relationship diagram The cause and effect diagram is constructed by mapping all coordinate sets of (ri +ci, ri -ci) to visualize the complex interrelationship and provide information to judge which are the most important factors and how influence affected factors (Shieh et al., 2010). The factors that tij is greater than , are selected shown in cause and effect diagram (Yang et al., 2008).

3. Research Methodology  3.1 Research Framework of TICs  This section established a structure for identifying the evaluation perspective and criteria as well as their relationships of TICs factors. An overview of the proposed TICs evaluation framework was illustrated in Figure 2. The details of each procedure and the results were explained in next section. Stage 1: Define the problem statement Stage 2: Explore TICs measurement perspectives and

Extensive literature reviews

criteria Stage 3: Develop interview questionnaire and validity testing Content validation by experts Stage 4: Interview session with 11 industrial experts

Stage 5: Analyze cause and effect relationship among and identify evaluation factors

Applying DEMATEL method

Figure 2: The proposed procedure of TICs criteria assessment.

3.2 Procedure and the result  This section is to describe the process of TICs evaluation perspectives and criteria, according to Figure 2. Not only the determination of TICs evaluation perspectives and criteria but also the measurement of the relationship among them was also performed. The process and the result of each stage were presented in the following stages:

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Detcharat Sumrit and Pongpun Anuntavoranich


3.2.1 Stage of defining the problem statements  To encounter the fierce competition of the dynamic global environment and the upcoming, TICs are considered as one of the significant factors of Thai technology-based firms to sustain competitiveness. Hence, an evaluation of TICs turns to be a tool to aid managements to define strengths and weaknesses in term of TICs. Appropriate factors of TICs then should be identified. This study presents not only the appropriate factors but also the cause and effect relationship among the perspectives and criteria. 3.2.2 Stage  of  exploring  the  TICs  measurement  perspectives  and  criteria  from  literature reviews  The extensive literature review was conducted to identify multi-attributions and multi-dimensionalities of the TICs evaluation factors. Based on the reviews, six perspectives and sixteen evaluation criteria were derived as shown in Table 1. 3.2.3 Stage of developing a questionnaire    After obtaining the sixteen criteria and six perspectives of TICs evaluation factors from literatures, a questionnaire was designed. A group of qualified experts reviewed and tested the designed questionnaire to assure the content validity of questionnaire. The group of qualified experts was consisted of three professionals from academic institutions, two from industrial sector and one from Thai Automotive Institution. After interviewing, the questionnaire was revised based on the experts’ aspects. 3.2.4 Stage of interviewing session  Eleven experts were asked to complete the questionnaire. The experts have at least 5 years experiences and worked in management positions in well-known Thai technology-based firms and some of the firms were awarded as Thailand’s Most Innovative Company in 2010. After obtaining the completed questionnaires from the experts, DEMATEL analytical technique was used to determine the causal relations and to identify the significant perspectives and criteria. The results of analyses were shown in the next section.

*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

89


3.2.5 Stage of  analyzing  the  causal  relation  and  identifying  the  evaluation  perspectives and criteria    Based on the six perspectives and sixteen criteria of TICs evaluation as stated above, this study further employed the DEMATEL method to indicate the complex relationship and identify the significant TICs evaluation perspectives and criteria. In this section, the computation was divided into two parts for calculating on perspectives and criteria, respectively. The procedure of the DEMATEL method and the results of each stage were also presented as follows. 3.2.5.1 Applying DEMATEL method on the six perspectives  Xk showed the data gathered in terms of the six perspectives of expert k, where Xk =

.

Step procedures of applying DEMATEL method were shown next.

X1=

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2

0

2

3

3

0

1

2

0

2

2

2

2

2

3

2

0

2

3

2

3

1

0

2

2

0

3

2

2

1

1

0

3

2

1

2

2

0

2

2

0

2

1

1

2

2

0

1

1

2

2

3

0

X10=

X11=

Detcharat Sumrit and Pongpun Anuntavoranich


(1) The computation of the average matrix Z was constructed by using Eq. (1).

Z=

0

2

2.2727

2.2727

2.2727

3.4545

1.90909

0

2.54545

3

2.5454

1.7272

1.18181

2.1818

0

1.7272

2

1.3636

1.72727

3.3636

1.72727

0

1.8181

1.9090

1.72727

1.6363

1.81818

1.8181

0

2.9090

1.72727

1.2727

1.63636

1.6363

1.9090

0

(2) The normalized initial direct-relation matrix D was calculated by using Eq. (2) to Eq.(5).

D=

0.0000

0.1760

0.2000

0.2000

0.2000

0.3040

0.1680

0.0000

0.2240

0.2640

0.2240

0.1520

0.1040

0.1920

0.0000

0.1520

0.1760

0.1200

0.1520

0.2960

0.1520

0.0000

0.1600

0.1680

0.1520

0.1440

0.1600

0.1600

0.0000

0.2560

0.1520

0.1120

0.1440

0.1440

0.1680

0.0000

(3) The total relation matrix T was calculated by using Eq. (6) to Eq. (7) as shown below.

T =

1.1983

1.6022

1.5638

1.6194

1.6335

1.7897

1.3147

1.4312

1.5522

1.6366

1.6189

1.6449

0.9837

1.2421

1.0379

1.2130

1.2383

1.2532

1.2195

1.5575

1.4040

1.3262

1.4714

1.5434

1.1290

1.3342

1.3001

1.3464

1.2203

1.4966

0.9883

1.1431

1.1256

1.1653

1.1928

1.1104

(4) The sums of rows and columns of matrix T were calculated by using Eq. (8) to Eq. (9) as shown in Table 2. Table 2: The sums of given and received among six perspectives. P1

P2

P3 *

P4 *

P5 *

P6 *

*

ri

cj

(ri+ cj)

(ri- cj)

9.4068

6.8335

16.2403

2.5733

P1

1.1983

1.6022

P2

1.3147

1.4312*

1.5522*

1.6366*

1.6189*

1.6449*

9.1985

8.3103

17.5087

0.8882

P3

0.9837

1.2421

1.0379

1.2130

1.2383

1.2532

P4

1.2195

1.5575

*

1.5638

1.4040

*

1.6194

1.3262

1.6335

1.4714

*

1.7897

6.9683

7.9836

14.9519

-1.0153

*

8.5219

8.3069

16.8288

0.2151

*

7.8266

8.3752

16.2018

-0.5486

6.7255

8.8382

15.5637

-2.1127

1.5434

P5

1.1290

1.3342

1.3001

1.3464

1.2203

1.4966

P6

0.9883

1.1431

1.1256

1.1653

1.1928

1.1104

*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

91


(5) The set up of the threshold value (α) The threshold value was derived from the average of elements in matrix T, which was calculated by using Eq. (10). =

.

= 1.351

(6) The construction of the cause and effect relationship diagram The values of tij in Table 2, which were greater than α (1.351), were shown as tij*, which presented the interaction between perspectives, e.g. the value of t12 (1.6022) > α (1.351), the arrow in the cause and effect diagram was drawn from P1 to P2. The cause and effect diagram of six perspectives was constructed as Figure 3. Threshold Value (α) = 1.351

r-c P1

3 2 1 0

r+c 15.0

15.5

-1 -2 -3

P2

P4 16.0

16.5

17.0

17.5

P5 P3 P6

Figure 3: The visualization of the causal relationship among perspectives of TICs. 3.2.5.2 Applying DEMATEL method on the sixteen criteria  Under each perspective, the significant criteria were determined by using the same procedures as described in (1) to (6) above. Both direct and indirect effects of the criteria under six perspectives were summarized in Table 3 and the cause and effect diagrams among criteria under each perspective were shown in Figure 4 to Figure 9.

92

Detcharat Sumrit and Pongpun Anuntavoranich


4. Discussion and Results  4.1 Results on the Perspectives    The important of evaluation perspectives was determined by (r+c) values. Based on Table 3, Collective Learning Capability (P2) was the most important evaluation perspective with the largest (r+c) value = 17.5087, whereas Innovation Sourcing Capability (P3) was the least important perspective with the smallest (r+c) value = 14.9519. Regarding to (r+c) values, the prioritization of the importance of six evaluation perspective was P2 > P4> P1> P5> P6> P3. Table 3: The direct and indirect effects of the criteria under each perspective. Criteria

(r+c)

(r-c)

The overall effects of the four criteria of Innovation Management Capability perspective Strategic Management Capability (C1)

17.8235

2.1543

Organization Capability (C2)

19.7564

0.1836

Resource Allocation Capability (C3)

17.0986

-1.3492

Risk Management Capability (C4)

17.7238

-0.9887

The overall effects of the three criteria of Collective Learning Capability perspective Learning Capability (C5)

9.3937

0.3263

Absorptive Capability (C6)

9.3174

1.3097

Knowledge Management Capability(C7)

9.0972

-1.6360

The overall effects of the two criteria of Innovation Sourcing capability perspective Network Linkage Capability (C8)

6.3333

1.0000

Technology Acquisition Capability (C9)

6.3333

-1.0000

The overall effects of the three criteria of Technology Development Capability perspective R&D Capability (C10)

71.7604

2.8446

Project Cross Functional Team Integration Capability (C11)

68.7422

1.7228

Technology Change Management Capability(C12)

65.4969

-4.5675

The overall effects of the two criteria of Robustness Product & Process Design Capability perspective Product Structural Design and Engineering Capability (C13)

7.8000

1.0000

Process Design and Engineering Capability (C14)

7.8000

-1.0000

The overall effects of the two criteria of Technology Commercialization Capability perspective Manufacturing Capability (C15)

21.000

1.0000

Market Capability (C16)

21.000

-1.0000

*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

93


r- c

C1

r- c

2

2 C6

1

1 C2 18

17

19

C5

r+c

0 20

-1

C4

-1 C3

-2

r+c

0

-2

Threshold Value (α) = 2.262

9.2

9.0

9.4

9.6

C7 Threshold Value (α) = 1.50

Figure 4: The cause and effect diagram of the four

Figure 5: The cause and effect diagram of the three

criteria of Innovation Management Capability

criteria of Collective Learning Capability

r- c

r- c 6

2

4

C8 1

C10 C11

2

r+c

0

-1

2.0

4.0

66

64

8.0

68

70

-2

-2

6.0

r+c

0

C9

-4

Threshold Value (α) = 1.583

C12

-6

Threshold Value (α) = 11.444

The cause and effect diagram of the two Figure 6:

Figure 7: The cause and effect diagram of the three

criteria of Innovation Sourcing Capability.

criteria of Technology Development Capability

r- c

r- c 2 1

2

C13

C15 1

 

0 4.0 -1

6.0

8.0

r+c

10

0 -1 -2

Threshold Value (α) = 1.950

Figure 8: The cause and effect diagram of the two criteria of Robustness Product & Process Design Capability

94

20

22

24

C16

C14

-2

r+c 18

Threshold Value (α) = 5.250

Figure 9: The cause and effect diagram of the two criteria of Technology Commercialization Capability

Detcharat Sumrit and Pongpun Anuntavoranich


Based on (r-c) values, the six perspectives were divided into (i) cause group and (ii) effect group. (i) If the value of (r-c) was positive or net cause, such perspective was classified in the cause group, and directly affected the others. The highest (r-c) factors also had the greatest direct impact on the others. In this study, Innovation Management Capability (P1), Collective Learning Capability (P2), and Technology Development Capability (P4) were classified in the cause group, having the (r-c) values of 2.5733, 0.8882, and 0.2151, respectively. It also indicated that P1 (Innovation Management Capability) was the most critical impact factor on the others. Based on the matrix T in Table 2, it was found that P2 (Collective Learning Capability) and P4 (Technology Development Capability) had a mutual interaction as both the value of t24 (1.6366) and t42 (1.5575) were greater than α (1.351). (ii) If the value of (r-c) was negative or net receive, such perspective was classified in the effect group, and largely influenced by the others. For this study, Technology Commercialization Capability (P6), Innovation Sourcing Capability (P3) and Robustness Product and Process Design Capability (P5) were categorized in the effect group, with the (r-c) values of -2.1127, -1.0153 and -0.5484, respectively. And P6 (Technology Commercialization Capability) was the most affected by the other factors (P1), (P2), (P4), and (P5).

4.2 Results on the Criteria    According to Table 3, under Innovation Management Capability perspective (P1), this study found that Organization Capability (C2) and Strategic Management Capability (C1) were the two most important criteria based on first and second highest (r+c) values of 19.7564 and 17.8235, respectively. Whereas both Strategic Management Capability (C1) and Organization Capability (C2) were in the cause group based on their positive (r-c) values of 2.1543 and 0.1836, respectively. For Resource Allocation Capability (C3) and Risk Management Capability (C4) were in the effect group, given negative (r-c) values of -1.3492 and -0.9887, respectively. From Figure 4, Strategic Management Capability (C1) was the most critical criteria because it directly influenced on the other three criteria. Organization Capability (C2) had a direct impact on Resource Allocation Capability (C3) and a mutual interaction on Risk Management Capability (C4). *Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

95


For the perspective of Collective Learning Capability (P2), Learning Capability (C5) and Absorptive Capability (C6) were the two most important criteria based on higher (r+c) values of 9.3937 and 9.3174, respectively. They were also the net cause group with higher positive (r-c) values of 0.3263 and 1.3097, respectively. For Knowledge Management Capability (C7) was net receive with the (r-c) value of -1.6360. From Figure 5, Absorptive Capability (C6) presented as the most significant criteria given impact to the other two criteria. For the perspective of Innovation Sourcing capability (P3) in Table 3, Network Linkage Capability (C8) and Technology Acquisition Capability (C9) showed the same importance level of the (r+c) values 6.3333. However, based on the (r-c) value of 1.0 (Figure6), Network Linkage Capability (C8), was a net cause and largely impacted Technology Acquisition Capability (C9). According to Technology Development Capability perspective (P4), R&D Capability (C10) and Project Cross Functional Team Integration Capability (C11) were the two most important criteria with highest (r+c) values of 71.7604, and 68.7422, respectively. Both of them were net cause. As shown in Figure7, R&D Capability (C10) had the greatest (r-c) value of 2.8446, which directly affected Technology Change Management Capability (C12) and had a mutual interaction on Project Cross Functional Team Integration Capability (C11). For the perspective of Robustness Product & Process Design capability (P5), both criteria Product Structural Design and Engineering Capability (C13) and Process Design and Engineering Capability (C14) had the same importance level of the (r+c) values equaling to 7.80. However, as Figure 8, Product Structural Design and Engineering Capability (C13) was net cause with the (r-c) value of 1.0, and affected Process Design and Engineering Capability (C14). For the perspective of Technology Commercialization Capability (P6), there were the same importance level of the two criteria i.e. Manufacturing Capability (C15) and Market Capability (C16), based on their equal (r+c) values of 21.0. However, as Figure 9, Manufacturing Capability (C15) was a net cause having the (r-c) value of 1.0 and affected Market Capability (C16).

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Detcharat Sumrit and Pongpun Anuntavoranich


5. Conclusion This study applied DEMATEL method not only to analyze the TIC evaluation perspectives and criteria, consisting of six perspectives and sixteen criteria for Thai technology-based firms’ but also to describe the cause and effect relationship among them. The result implied that the management should concentrate on improving the three core perspectives in the cause group i.e. Innovation Management Capability, Collective Learning Capability, and Technology Development Capability. The three remaining perspectives were found in the effect group i.e. Technology Commercialization Capability, Innovation Sourcing Capability and Robustness Product and Process Design Capability, which they were also affected by the ones in the cause group. By the aspect of prioritizing the importance of criteria and the cause and effect relationship among criteria under the three core perspectives, this study found that the Strategic Management Capability, Absorptive Capability and R&D Capability were the most critical criteria. Therefore, in order to enhance the overall competitive advantage in term of TICs, Thai technology-based firms should allocate more resources in these core perspectives. In the case of having limited resources, firms should emphasize on their Strategic Management Capability since it is the main critical criteria in the adjustment of corporate planning and would yield highest positive results on TICs.

6. Appendix The definitions of criteria are identified in Table A: Table A: terms and definitions of criteria used in this study Terms

Definitions

Strategic Management

The firm’s ability to identify internal strengths and weaknesses and external opportunities and

Capability

threats, to formulate plans in accordance with the corporate vision and missions, and to adjust the plans for implementation (Yam et al., 2004).

Organization Capability

The firm’s ability to secure the organizational mechanism and harmony, to cultivate the organization culture, and to adopt the better management practices (Yam et al., 2004).

Resource Allocation

The firm’s ability to acquire and to allocate appropriately capital, exercise and technology in the

Capability

innovation process (Yam et al., 2004).

Risk Management Capability

The firm’s ability to assess the risk of technological innovation and to take the risk of technological innovation adoption (Forsman, 2011).

*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

97


Table A: terms and definitions of criteria used in this study (continue) Terms

Definitions

Learning Capability

The firm’s ability to identify, to assimilate, and to exploit the knowledge from internal organization (Yam et al., 2004).

Absorptive Capacity

The firm’s ability to recognize, to assimilate, and to apply the value of new external information to commercial ends (Cohen and Lavinthal, 1990).

Knowledge Management Capability Network Linkage Capability

The firm’s ability to accumulate critical knowledge resources and to manage its assimilation and exploitation (Miranda et al., 2011). The firm’s ability to transmit information, skills and technology, and to receive them from other departments of the firm, including third parties such as the clients, the suppliers, the consultants, the technological institutions (Shan and Jolly, 2010).

Technology Acquisition

The firm’s ability to acquire and to adopt external technology from other parties (Hemmert, 2004).

Capability R&D Capability

The firm’s ability to integrate R&D strategy, project implementation, project portfolio management, and R&D expenditure (Yam et al., 2004).

Project Cross functional

The firm’s ability to coordinate and to integrate all phases of the R&D process and the inter-relations

team integration capability

with the functional tasks of engineering, production and marketing (Camisón and Forés, 2010).

Technology Change

The firm’s ability to accurately predict future technological trends and to response the technology

Management Capability

changes (Jansen et al., 2005).

Product Structural Design

The firm’s ability to design product structure, to build product modularization and to make product

and Engineering Capability

and process compatible (De Toni and Nassimbeni, 2001).

Process Design and

The firm’s ability to design process for supporting the manufacturing design and to design the

Engineering Capability

assembly activities (De Toni and Nassimbeni, 2001).

Manufacturing Capability

The firm’s ability to transform R&D result into products, which meet the market’s need as required design, and able to produce (Yam et al., 2004).

Market Capability

The firm’s ability to sell products on the basis of the understanding of customers’ need, the competitive environment, costs and benefits, and the acceptance of the innovation (Yam et al., 2004).

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China. Journal of Engineering and Technology Management, 29(1), 34–46. Yang, L-R. (2013). Key practices, manufacturing capability and attainment of manufacturing goals: The perspective of project/engineer-to-order manufacturing. International Journal of Project Management, 31(1), 109-125. Yang, Y.P., Shieh, H. M., Leu, J.D. and Tzeng, G.H. (2008). A novel hybrid MCDM model combined with DEMATEL and ANP with applications. International Journal Operational Research, 5(3), 160-168. Yu, R. and Tseng, G.H. (2006). A soft computing method for multi-criteria decision making with dependence and feedback. Applied Mathematics Computation, 180(1), 63-75. Zahra, S.A. and George, G. (2002). Absorptive capacity: a review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203. Zeng, S.X., Xie, X.M. and Tam, C.M. (2010). Relationship between cooperation networks and innovation performance of SMEs. Technovation, 30(3), 181-194. Zhang, Z., Waszink, A. and Wijngaard, J. (2000). An instrument for measuring TQM implementation for Chinese manufacturing companies. International Journal of Quality & Reliability Management, 17 (7), 730–755.

D. Sumrit is a Ph.D. Candidate of Technopreneurship and Innovation Management Program, Graduate School, Chulalongkorn University, Bangkok, Thailand. He received his B.Eng in Industrial Engineering from Kasetsart University, an M.Eng from Chulalongkorn University and MBA from Thammasat University.

Dr. P. Anuntavoranich is an Assistant Professor of Department of Industrial Design at Faculty of Architecture, Chulalongkorn University, and he is Director of Technopreneurship and Innovation Management, Chulalongkorn University. He received his Ph.D. (Art Education) from the Ohio State University Columbus, OH., USA. His specialist is creative design and innovation management.

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

*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: dettoy999@gmail.com, p.idchula@gmail.com. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf

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The Effect of the 2,4-Dichlorophenoxy Acetic Acid, Benzyl Adenine and Paclobutrazol, on Vegetative Tissue-Derived Somatic Embryogenesis in Turmeric (Curcuma var. Chattip) Anchalee Jala a* a

Department of Biotechnology, Faculty of Science and Technology, Thammasat University, THAILAND ARTICLEINFO

A B S T RA C T

Article history: Received 09 October 2012 Received in revised form 06 November 2012 Accepted 16 January 2013 Available online 22 January 2013

The nodal explants of Curcuma var. Chattip could develop callus after in vitro culturing and transplanting within 4 weeks in MS medium supplemented with 1.0 mg l-1 2,4-D, although this optimal if the media was further supplemented with 5.0 mg l-1 BA, obtaining the highest number of new shoots in 6 weeks. MS medium supplemented with 0.01 mg l-1 paclobutrazol and 15% (v/v) coconut water was found suitable for regenerating the highest number of new shoots (7.25 shoots). The number of leaves per plantlet, length of leaves, and length of petrioles were significantly reduced (p≤ 0.05) when increased concentrations of paclobutrazol and especially paclobutrazol with 15 % (v/v) coconut water. However further experimentation is required to evaluate the dose-response and the interaction between coconut water and paclobutrazol. In contrast, there were no significant difference in leaf width in all treatments.

Keywords: Paclobutrazol; 2,4-dichlorophenoxy acetic acid (2,4-D); Benzyl adenine (BA), Somatic embryogenesis.

2013 INT TRANS J ENG MANAG SCI TECH.

1. Introduction Turmeric (Curcuma var. chattip), a herbaceous plant of the Zingiberaceae (ginger) family (Purseglove,1972), is an economically important cultivated species in Thailand being grown for cut flowers, decoration and landscaping. Although propagated by underground rhizomes, *Corresponding author (A. Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450. E-mail address: 2013 International Transaction Journal of Engineering, anchaleejala@yahoo.com. Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/105-110.pdf

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the rate of rhizome multiplication and growth is very low, making this non-viable for large scale economic production. Moreover, many diseases and pests, particularly soft rot caused by Pythium spp. (Jantan et al., 2003) as well as bacterial wilts are consistently threatening Curcuma var. chattip. This problem is compounded by its slow propagation rate which seriously impedes on the ability to replace diseased plants quicker than infection rates. To aid the rapid and large scale propagation of this plant, in vitro formation of storage organs such as rhizomes that can be directly transferred to the field without any acclimatization has been reported (Balachandran et al., 1990). However, further improvement of the protocol to obtain larger and more vigorous plantlets is required in order to approach an economically or logistically viable method. The presented investigation was carried out to examine the effects of the plant growth regulators palcolbutrazol and benzyl adenine (BA) in the presence of napthaleneacetic acid (NAA) and coconut water, upon the formation of callus and multiply new plantlets.

2. Materials and Methods  Spouted immature shoots (ca. 1 cm. long) were collected and used as explants. They were washed thoroughly in running tap water and soaked with liquid detergent (teepol solution) followed by rinsing in tap water for 2 min. For surface sterilization, explants are rinsed in 10 % (v/v) Clorox solution for 10 min and 5 % (v/v) Clorox solution for 10 min and finally soaked with sterile distilled water three times to remove traces of Clorox. Immature shoots are trimmed to remove excess tissue. Murashige and Skoog medium (MS) fortified with 0.25 % (w/v) and 4% (w/v) final concentration of gelrite and sucrose, respectively. MS medium supplemented with 2,4-dichlorophenoxy acetic acid (2,4-D), BA, and coconut water is used as basal medium for callus. The pH of medium was adjusted to 5.6 by using 0.1 N NaOH or 0.1 HCl and autoclaving at 121 ํC for 20 min to sterilize. All cultures were incubated under 16hours photoperiod (irradiance of 36 µmole m-2 S-1) with temperature 25 ± 1°C. We observed samples at regular intervals and scored for callus and shoot growth, with 10 independent replicates being used for each experimental treatment.

3. Statistical Analysis  This experiment used CRD (Completely Randomized Design). The analysis of variance between the means was conducted by using Duncan’s multiple range test (Duncan, 1955).

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4. Result and Discussion  After 4 weeks of in vitro culture, MS medium with 1.0 mg l-1 2,4-D and no BA showed a remarkable degree of callus formation. Indeed, the callus obtained from in vitro culture was faster growing, delicate, mostly spongy, and white creamy in color (Table 1). In addition, with 0.5 mg l-1 2,4-D concentration, slightly more explants grew callus, and grew more callus per explants, when using MS medium supplemented with 0.5 mg l-1 and 1.0 mg l-1 BA. This studies is in broad agreement with Nayak (2000) and Hosoki andSagawa(1977) who reported the callus induction from leaf base as explants of curcuma aromatica using 1.5 mg l-1 2,4-D and 1.0 mg l-1BA. However, in this system it is yet to be evaluated if BA will enhance the highest level of callus production seen with 1.0 mg l-1 2,4-D. Table 1: The average number of explants induced to form callus (as 5%) and degree of callus per explants after culturing for 4 weeks. MS medium plus % of explants Degree of Callus response induced callus 2,4-D 0.5 mg l-1 53.4 a 2,4-D 1.0 mg l-1 78.2 c -1 2,4-D 2.0 mg l 56.7 a 2,4-D 0.5mg l-1+BA 0.1mg l-1 49.86 a -1 -1 2,4-D 0.5 mg l +BA 0.5mg l 66.67 b -1 -1 2,4-D 0.5 mg l +BA 1.0mg l 61.34 b a – Slight callusing, b – more callusing, and c – Profuse callusing Callus obtained from in vitro cultivation (Table 1) were used to investigate the influence of growth regulators on the induction of somatic embryogenesis. Four-week old callus were used for multiplying new shoot by culturing them on MS basal medium supplemented with 0.1 mg l-1NAA, 15% (v/v) coconut water and varying levels (viz.1.0, 2.0,3.0, 4.0, and 5.0 mg l-1) of BA. All independent in vitro cultures suggested that the observed effect of BA was likely to be mediated via inducing new shoots, as summarized in Table 2. After six weeks, new shoots regenerated from callus. MS medium supplemented with 0.1 mg l-1,15% CW and 5.0 mg l-1 BA gave the highest average levels of new shoots attained per callus (5.1 ± 0.54) as worked of Dekker et al. (1991) did with ginger but Jala (2011) cultured Curcuma longa in MS medium supplemented with 2 mg l-1 BA. Callus obtained from above mentioned medium are used to investigate the influence of the growth retardant (paclobutrazol) on the growth of somatic embryogenesis. *Corresponding author (A. Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450. E-mail address: 2013 International Transaction Journal of Engineering, anchaleejala@yahoo.com. Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/105-110.pdf

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After six weeks, callus was regenerated to new shoots on MS medium supplemented with 0.1 mg l-1 NAA and 5.0 mg l-1 BA. On this condition, callus could form somatic embryos and germinated (Table 2) within 6 weeks, allowing this system to investigate the effect of paclobutrazol. Table 2: Number of new shoots regenerated from callus that are cultured on MS medium supplemented with 0.1 mg l-1 NAA 15% (v/v) coconut water with varied BA concentrations. NAA (mg l-1) BA(mg l-1) Number of new shoots* CW(%) 0.1

15

1.0 2.0 3.0 4.0 5.0

1.2 e 1.8 d 2.5 c 3.2 b 5.1 a

* Means within columns with different letters are significantly different by using DMRT at the ( p ≤ 0.05) level. Table 3: The effect of paclobutrazol and coconut water on somatic embryo regeneration from callus within 8 weeks of in vitro culturing. MS+NAA0.1mgl-1, BA5.0mgl-1, 4 %sucrose (control) plus

No. of multiple shoots*

No. of The leaf The leaf Length leaves length width of leaf per (cm)* (cm)ns petriole plantlet * (cm)* Control ( no Paclobutrazol ) 4.00 c 7.82 a 3.80ab 0.73 5.53 a Paclobutrazol 0.01 mg l-1 3.00 d 7.67 a 3.65 b 0.84 4.11 b -1 Paclobutrazol 0. 1 mg l 5.00 b 6.92 ab 3.37 bc 0.78 3.36 cd Paclobutrazol 1.0 mg l-1 4.00 bc 7.08 ab 3.53 b 0.86 3.97 bc Paclobutrazol 5.0 mg l-1 3.75 c 5.86 c 3.97 a 0.75 4.40 b -1 Paclobutrazol 10.0 mg l 4.50bc 7.54 a 3.25 c 0.85 3.61 c Paclobutrazol 0.01 mg l-1+ cw15% 7.25 a 4.57 c 4.33 a 0.87 3.60 c Paclobutrazol 0.1 mg l-1 + cw15% 3.75 c 5.48 c 3.66 b 0.82 3.70 c Paclobutrazol 1.0 mg l -1 +cw15% 6.25 a 5.33 c 4.08 a 0.82 3.41 cd Paclobutrazol 5.0 mg l -1 +cw15% 3.00 de 6.60 bc 3.93 a 0.79 2.51 de Paclobutrazol 10.0 mgl-1 +cw15% 2.5 e 2.65d 2.15 d 0.91 e *Means within columns with different letters are significantly different from each other (p ≤ 0.05), ns - no significant difference for that trait across all culture conditions.

4.1 Effect of plant growth (retardant – paclobutrazol) on somatic embryo  The callus were used to investigate the influence of the growth retardant (paclobutrazol) on growth of somatic embryogenesis by cultured them in MS basal medium supplemented with 0.1 mg l-1 NAA, 5 mg l-1 BA, with and without 15% (v/v) coconut water (CW) and varied concentrations of paclobutrazol (viz. 0.01, 0.1, 1.0, 5.0 and 10 mg l-1) for 8 weeks. During this time the number of multiple shoots, number of leaves, leaf length, leaf width, and length of each leaf petriole were measured as shown in Table 3.

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Results indicated that paclobutrazol and 15% (v/v) CW both induced a significant difference (p≤ 0.05) on plantlet regeneration as measured by four out of five traits (Table 3). Only the average leaf width showed no statistically significant differences between the different treatments. However, length of leaf petriole and length of leaf were not significantly different. The numbers of leaves per plantlet were all broadly decreased by increasing paclobutrazol concentrations, especially in the presence of coconut water where the broadly similar effects at lower concentrations of paclobutrazol in the presence of 15% (v/v) coconut water. However, with respect to the number of adventitious shoots the situation is less clear with no clear trend.

In general, Paclobutrazol alone showed no clear if any concentration

dependent effect upon the number of multiple shoots per explants but, the data for Paclobutrazol in the presence of 15% (v/v) coconut water is more erratic with the two highest average number of shoots per explants (7.25 shoots and 6.25 shoots for 0.01 and 1.0 mg/l Paclobutrazol, respectively) intermixed amongst lower values with the lowest attained with 10 mg/l Paclobutrazol. Direct somatic embryogenesis has been reported in many other plants (e.g. Nadguada et al.1978, Salvi et al. 2000, Shiqurkar et al. 2001, Yasuda et al. 1988).

5. Conclusion  Immature nodal explants of Curcuma var. Chattip could develop callus after in vitro culturing in MS medium supplemented with 1.0 mg l-1 2,4-D within 4 weeks. Subculturing callus in MS basal medium supplemented with 0.1 mg l-1 NAA, 15% coconut water and 5.0 mg l-1 BA yielded the highest number of new shoots. These could be further induced somatic embryo regeneration and leaf formation by in vitro culturing in MS medium supplemented with 0.1 mgl-1 NAA, 5.0 mgl-1 BA, 15% (v/v) coconut water and (0.01 to10.0 mg l-1) paclobutrazol.

Although equivocal as complex interactions may be occurring, the data

suggests that 0.01 and 1.0 mg l-1 paclobutrazol and 15% (v/v) CW were the most suitable conditions leaded to formation of the highest number of new shoots (7.25 and 6.25 shoots per explants, respectively) within 6 weeks.

6. References Balachandran S.M., Bhat S. and Chandel K. 1990. In vitro clonal multiplication of turmeric (Curcuma spp.) and ginger (Zingiber officinale Rosc.). Plant Cell Rep, 8: 521-524. *Corresponding author (A. Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450. E-mail address: 2013 International Transaction Journal of Engineering, anchaleejala@yahoo.com. Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/105-110.pdf

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Dekkers, A.J.; Rao A.N. and Goh C.J. 1991 In vitro strorage of multiple shoot cultures of gingers of ambient temperatures of 24-29 C, Sci Hort, 47:157–168. Hosoki, T. and Y. Sagawa. 1977. Clonal propagation of ginger (Zingiber Rosc.) Through tissue culture. Sci Hort., 12: 451–452. Jala, A. 2011. Effects of NAA BA and Sucrose on Shoot Induction and Rapid Micropropagation by Trimming Shoot of Curcuma Longa L. INT TRANS J ENG MANAG SCI TECH, 3(2):101-109. Jantan, I.B., Yassin M.S.M., Chin C.B., Chen L.L., Sim N.L. 2003. Antifungal activity of the essential oils of nine Zingiberaceae species. Pharmaceutical Biology, 41: 392-397. Jen-kun Lin, Shoei-Yn and Lin-Shiau. 2000. Mechanisms of Chemoprevention by Curcumin. Proc. Natl. Sci. Counc. ROC (B), 25(2): 59 – 66. Jitoe, A., Toshiya. Masuda, I. G. P. Tengah, Dewa N. Suprapta, I. W. Gara, Nobuji. Nakatani. 1992. Antioxidant activity of tropical ginger extracts and analysis of the contained curcuminoids. J. Agri. Food Chem., 40:1337- 1340. Kikuzaki, H. and Nakatani, N. 1993. Antioxidant effects of some ginger constituent. J. Food Sci, 58:1407-1410. Murashige, T. and Skoog, F. 1962. A revised medium for rapid growth and bioassays with Tobacco tissue cultures. Physiol. Plant, Compenhagem, 15:473–479. Nadguada, R.S.Mascarenhas,A.F., Hendre, R.R. and Jagannathan, V. 1978. Rapid multiplication of turmeric (Curcuma longa L). Ind. J. Exp. Biol., 16:120-122. Nayak, S. 2000. In vitro multiplication and microrhizome induction in Curcuma aromatica Salisb. Plant Growth Regul. 32:41-47. Salvi, N.D., George, L. and Eapen, S. 2000. Direct regeneration of shoot from immature inflorescence culture of turmeric. Plant Cell Tiss. Org. Cult., 62:235-238. Shigurkar, M.V., John, C.K. and Nadgauda, R.S. 2001. Factors affecting in vitro microrhizome production in turmeric. Plant Cell Tiss. Cult. 64:5-11. Yasuda K., Tsuda T., Shimizu H., and Sugaya A. 1988. Multiplication of Curcuma species by tissue culture, Planta Medica, 54: 75-79.

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 given at the journal’s website.

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2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

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Application of Bender’s Decomposition Solving a Feed–mix Problem among Supply and Demand Uncertainties Somsakaya Thammaniwit a

a*

and Peerayuth Charnsethikul

a

Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, THAILAND. ARTICLEINFO

A B S T RA C T

Article history: Received 23 August 2012 Received in revised form 19 December 2012 Accepted 16 January 2013 Available online 24 January 2013

The feed-mix problem is primarily transformed into a mixing situation applying a mathematic formulation with uncertainties. These uncertainties generate the numerous expansions of alternative constraint equations. The given problem has been formulated as mathematic models which correspond to a large-scale Stochastic Programming that cannot be solved by the most popular ordinary calculation method, Simplex Method: LINPROG. This research aims to investigate effective methodology to reveal the optimal solution. The authors have examined the method of Bender’s decomposition: ® BENDER and developed both methods into MATLAB program and calculated comparatively. The results revealed that the nearest optimal solutions can be determined by means of a Two-stage Stochastic Programing incorporated with Bender’s decomposition at the most intensive number of uncertainties and take less calculation time than by LINPROG.

Keywords: Large-scale Feed-mix Problem; Bender’s Decomposition; Two-stage Stochastic Programming.

2013 INT TRANS J ENG MANAG SCI TECH.

1 Introduction Many animal food mixing industries are confronted with a decision making problem on an appropriate recipe. That is to say that the determination of raw materials which contain various kinds of feed ingredients added to the process are influenced by expectations of *Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: csomsak@engr.tu.ac.th. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf

111


obtaining a product with lower costs, standardization, or surpass nutritive requirement. Such a feed mix problem is complex and cannot be solved by traditional calculation methods. There are many approaches which have been applied, such as Pearson’s square method which is very suit for only two-feed ingredients to be mixed [9] and Trial and Error which is one of the most popular means for feed formulation but it consumes a lot of time for calculation [4],[9]. Classical Linear Programming (LP) is widely used for modeling the animal feed problem. The normal objective in formulating the feed mix is to minimize cost subject to adequate nutrient ingredients (input raw materials) and the required nutrient constraints (output nutrient values) [1].

However, due to the various constraints that need to be

conserved, the problem has been extended to become a large-scale problem with uncertainties. Therefore, when using LP method, it is difficult to determine a good balance of nutrients in the final solution. The constraints in LP are also rigid leading to an infeasible solution [1], [2]. However, LP has a positive highlight as a deterministic approach, because it can provide the best solution of hundreds of equations simultaneously [14].

By Stochastic

approaches, there are also various methods that have been applied for such a complex problem e. g: Chance Constraint (CCP) and Quadratic Programming (QP), Risk Formulation, and Genetic Algorithm (GA). In addition to these mentioned methods, there are also some methods with other possible algorithms such as Integrated LP and Dynamic Programming (DP), Integrated LP and Fuzzy, Integrated GA and Fuzzy, Integrated GA and Monte Carlo Simulation. All of these methods are arranged as Integrated approaches [13]. This research does not take into consideration all the above mentioned issues, but aims to investigate another new effective calculation method for the feed mix problem and proposes the application of Two-stage Stochastic Linear Programming (TSL) incorporated with the method of Bender’s Decomposition (BENDER). Hence, this paper describes the preliminary stage of mathematic formulation, the setup of matrix systems and program development, MATLAB@ program, and represents the optimization results of a case study.

2 Problem Analysis and Methodology  The classical diet problem is considered as a Linear Programming problem with general LP matrix: Min Z = CT X, Subject to AX = b, and X ≥ 0 for all. Because of the limitation of the calculation devices, the prior results were revealed without regard for some variables with

112

Somsakaya Thammaniwit, and Peerayuth Charnsethikul


high variance constraint coefficients. Nowadays, because of the higher performance of computational calculation, the development of the mentioned LP model when the system uncertainties are taken into account can be written as Min

Z

subject to

gT U

+

AX + U - V

=

b

x , u, v

0,

=

CT X

+

where CT X represents the main cost and g T U

hT V

(1) h T V the additional corrective costs

+

of materials supplied subject to AX + U - V = b , where A is the coefficient of the decision variable X (material quantity), U and V stand for the least and the excess mixed output quantities respectively. Awareness of nutrition values contained in U and V have an effect on the RHS as well. This issue will be discussed in the subtopic 2.3 later. Meanwhile the Two-stages Linear Programming Model [6], [7], [10], [11], [16] can be written as:

Maximize

Q k n z = ∑ E [c ] x + ∑ Pq [ ∑ c x ] j j q=1 j=1 j=k+1 qj qj

(2)

Subject to 1st.Stage

k ∑ a ij x j = bi j=1

2nd.Stage

k k ∑ a qij x j + ∑ a qij x qj = b qi j=1 j=1 i = r +1,...m

x

j

≥ 0;

for

(3)

i = 1, 2,..., r

(4)

for q = 1,2,...,Q

x

qj

≥ 0,

where Pq

x

qj

probability of occurrences of scenerio q

(q=1,2,…,Q)

nd

extent variable in the 2 .stage at constraint j by event q

Notation 1. The value of each random element is independent of the levels of all x j 2. The levels of x j for j = 1, 2 ,…, k ≤ n must be fixed at the 1st. stage 3. The constraint i = 1, 2… r contains only the 1st.stage variables, and the associated aij *Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: csomsak@engr.tu.ac.th. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf

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and bi are known with certainty. 4. The variables of xqj in the 2nd. Stage, where i = r +1, 2, …, m and q = 1, 2, …, Q 5. The values of cqj , a qij and f bqi , for i = r+1, 2, …, m and j = 1, 2 ,…, n are represented by the set of ( cqj , a qij , bqi ) with probability Pq , for q = 1, 2, …, Q

2.1 The problem analysis  The animal food mixing as shown below in Table 1 was discussed by a production team. The problem is to determine the optimal quantities of the three main input raw materials to be added to the mixing process. Table 1: The input ingredient amounts represented as x1, x2, and x3 were unknown. Protein (%) Calcium (%) cost / unit Crushed dried fish 51-53 10-11 70Baht x1 Tapioca 2-3 5-7 40Baht x2 Sorghum 8-9 23Baht x3 Market required between19-20 Not less than 8 m Note: Baht is the currency used in Thailand (As of January 2013, 30Baht = US$1). The market demand d = 1.000 ± 0.001 unit weigh

t

Let x1, x2 and x3 be the non-negative quantities of the crushed dried fish, tapioca and sorghum respectively. They are mixed to yield 1 unit of the minimum cost diet that satisfies all the specified nutritional requirements m =2 prescribed as following: protein not in excess of not less than

20 % (Upper Boundary) 19 % (Lower Boundary)

calcium not less than

8%

Incremental corrective action cost of nutrient value, respectively FEXD

=

fexd

=

7 Baht/ Unit of protein

FLES

=

fles

=

5 Baht/ Unit of protein

FEXD

=

fexd

=

7 Baht/ Unit of calcium

FLES

=

fles

=

4 Baht/ Unit of calcium

Incremental corrective action cost of ingredient (raw material), respectively FEXDD FLESD

114

=

fexdd = =

30

flesd =

Baht/ kg 5000 Baht/ kg

Somsakaya Thammaniwit, and Peerayuth Charnsethikul


2.2 Methodology Such a problem is a typical large-scale stochastic linear programming with full system uncertainties (tolerances). The decision values of variables xj are decided by the coefficient of a1 ± tol, a 2 ± tol, ... , a n ± tol , right-hand-side ( RHS) parameter vector, the nutrition values bi of b1 ±tol, b 2 ±tol, ... , b m ±tol and moreover, the size of the market demand d ±tol as Figure1 below:

Figure 1: The problem type: A-B-D Uncertainty [15].

2.3 Model Formulation  To formulate the given problem in the form of TSL_ model and to allocate the calculation matrix system, some occurrence possibilities were assumed as follows: Assumptions A): PN

the occurrence possibility for each nutrient constraint

PD

the occurrence possibility for each demand constraint

PN = PD = P (Point) for this case study, the distributions of the probability of PN and PD are assumed to be uniform distributions. Thus, the possibilities PN and PD will be equal and also equal to P (Point) where the P (Point) is the initial input number for allocating the division number of all system uncertainty intervals. E

incremental event step, for this study, E = PN x PD

Constr

Constraint, C = m x Event + PD

Var

Variable, Var = n + 2Constr

*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: csomsak@engr.tu.ac.th. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf

115


Assumptions B): The lower and upper boundaries of all constraint variables are allocated from the middle point of their tolerances:

a ij ∈ ⎡⎣a ij(mid) - a ij(tol) ; a ij(mid) + a ij(tol) ⎤⎦ bi ∈ ⎡⎣ bi(mid) - bi(tol) ; bi(mid) + bi(tol) ⎤⎦ d k ∈ ⎡⎣d (mid) - d (tol) ; d (mid) + d (tol) ⎤⎦

(5)

a ij(mid) ; a ij(tol) ; bi(mid) ; bi(tol) ; d (mid) and d (tol) ∈ R Referring to the previous notations (1), (2), (3), (4) and (5) including analyzing the above given diet problem in Table 1, the calculation models were developed to be (6), (7) and (8). The objective function of the given problem was to minimize the total cost z min. which is the sum of the raw material unit cost cj multiplied by the amount of xj, plus the sum of all necessary incremental corrective action costs for both qualitative and quantitative values not meeting the specification, Equation (6). The sum of the product of the quantity xj and its uncertain coefficients aijl including the sum of nutritive slack u ilk and surplus vilk of each calculation scenario have to be balanced to the right-hand-side RHS , i.e. the vectors of required nutritive value of bil multiplied by the demand d , in Equation (7). Simultaneously, the LHS, the sum of xj and addition of the slacks uk and surplus vk has to be associated with the quantity requirement of d. In practice, the demand d cannot be exactly equal to 1 unit, but rather it comprises an allowance of ± 0.001 in unit weight (0.1% error). Minimize z n ∑ c jx j j=1

+

m P P P ∑ ∑ ∑ (gilk u ilk + h ilk vilk ) + ∑ (g k u ′k + h k v′k ) i=1k=1 l=1 k=1

(6)

Subject to

x ; j

n ∑ a ijl × x j + u ilk - vilk = bil × d k j=1

; ∀ilk

(7)

n ∑ x + u ′k − v′k j j=1

; ∀k

(8)

u

ilk

;

v

il k

;

= dk u′ k

; v′ k

≥ 0

; ∀i, j, k

Where

116

Somsakaya Thammaniwit, and Peerayuth Charnsethikul


k

denotes the constraint alternatives

j

denotes the type of ingredient to be input to mixing process ( j=1,2,…,n ).

i

denotes the type of nutrient composition which the market needs ( i=1,2,…,m)

cj

denotes the cost factor of raw material j- type (cost/ unit weight = fx )

xj

denotes the quantity of raw material j- type (weight unit = kg ).

aij

denotes the nutrient value type i in material type j

bi

denotes the nutrient value (of product) type i /unit weight.

u

denotes the nutrient value (of product) i , at the event l which misses (slack )

i lk

the target in the alternative k v ilk

denotes the nutrient value (of product) i , at the event l which exceeds (surplus ) the target in the alternative k

g il k

denotes the expected cost of nutrient value i / unit which misses the target alternative k . FLES = fles

hilk

denotes the expected cost of nutrient value i / unit which exceeds the target alternative k . FEXD

= fexd

gk

denotes the expected cost of u′k (by lack of demand) = FLESD

hk

denotes the expected cost of v′k (by exceeding demand) = FEXDD

u′k

denotes the lower quantity of raw material in the alternative k

v′k

denotes the excess quantity of raw material in the alternative k

dk

denotes the market demand 1.000 weight unit with the standardized allowance of ± 0.001 in unit weight (0.1 % error) for animal food production.

2.4 Minimum Cost Calculation Model  Minimize z = m P P P (fx1 ⋅ x1 + fx 2 ⋅ x 2 + fx 3 ⋅ x 3 ) + ∑ ∑ ∑ (FLES ⋅ u ilk + FEXD ⋅ vilk ) + ∑ (FLESD ⋅ u ′k + FEXDD ⋅ v′k ) (9) i=1 k=1 l =1 k=1

Subject to n ∑ a ijl × x j + u ilk - vilk = bil × d k j=1

; ∀ilk

*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: csomsak@engr.tu.ac.th. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf

117


n ∑ x + u ′k - v′k = d k ; ∀i, k j j=1 x ; j

u

il k

;

v

ilk

;

u′ k

; v′ k

≥ 0

; ∀i, j, k

Substitute the given data from Table 1 into the minimal cost calculation model above 2

P

P

Minimize Z = (70x1 + 40x 2 + 23x 3 ) + ∑ ∑ ∑ ((5,4) ⋅ u ilk + (7,7) ⋅ vilk ) + i=1 k=1 l =1 P ∑ 5000 ⋅ u ′k + 30 ⋅ v′k k=1

(

)

Subject to [51--53]x1 + [2--3]x 2 + [0]x 3 +u - v 1lk 1lk [10--11]x1 + [5--7]x 2 + [8--9]x 3 + u - v 2lk 2lk x1 + x 2 + x 3 + u ′ - v ′ k k ' ≥ x1 , x 2 , x 3 0, u , v' il k ilk

= [19--20]

∀l ,k

= 8

∀l ,k

= 1±0.001

∀k

∀i,l ,k

0

1st.Event; for i = 1, l =1, k =1 (at lower boundary) 51x1 + 2x 2 + 0x3 +u111 − v111 10x1 + 5x 2 + 8x3 + u 211 - v211 x1 + x 2 + x3 + u1′ - v1′

= 19 ⋅ (0.999) = 8 ⋅ (0.999) = 0.999

2nd.Event; for i = 2, l = 2, k = 2 (at middle value) 52x1 + 2.5x 2 + 0x3 +u122 - v122 10.5x1 + 6x 2 + 8.5x 3 + u 222 - v 222 x1 + x 2 + x3 + u ′2 - v′2

= 19.5 ⋅ (1.000) =

8

⋅ (1.000)

= 1.000

3rd.Event; for i = 3, l = 3, k = 3 (at upper boundary) 53x1 + 3x 2 + 0x3 + u133 - v133

= 20 × (1.001)

11x1 + 7x 2 + 9x3 + u 233 - v233 x1 + x 2 + x3 + u3′ - v3′

= 8 × (1.001) = 1.001

The above formulations just demonstrate how to set up only three events (l): lower boundary (LB), middle value, and upper boundary (UB). But, in this research, the calculation models were planned to be set up throughout the tolerance intervals of all relevant variables

118

Somsakaya Thammaniwit, and Peerayuth Charnsethikul


and divide those with the same number of P, by which the resolution (res), through all uncertainty intervals can be definable. One must beware of the LB and UB of each event which correspond with the given tolerances.

2.5 The TSL incorporated with Bender’s Decomposition  The concept of the Bender’s decomposition is to predict the second-stage costs by a scalar θ and replace the second-stage constraints by cuts, which are necessary conditions expressed in terms of the first-stage variables x and θ [6], [10], [12]. The initial model can be modified, and written as shown below: The 1st.stage: Primal problem Min

Z

T T T C X + g U + h V

=

subject to

AX + U - V

x , u, v

=

b

0,

(10)

The 2nd.stage: The Dual Bender’s Decomposition U - V

=

b - AX

b - AX

0

Max

T

ω=

( b - AX )

θ=

∑ ( b - AX )

cond =

Min θ+X; Subject to

θ +

T Y + C X

(11)

T

(

)

⎡ cond; θ+ Y T A-C T X ≥ ⎣

( YT A-CT ) X

T b Y⎤

(12)

T ≥ b Y

Stop condition Check

θ = ω ?

No → Get Xnew → 1X, 2X,..... Yes → Get θ and X

⇒ Min Z = Max ω ⇒ Stop

The selected algorithm to attain a satisfactory solution was the integration between TSL and Bender’s decomposition. As shown in Figure 2, the main concept of Bender’s decomposition is to split the original problem into a master problem and a sub-problem, which in turn decomposes into a series of independent sub problems, one for each. The latter are used to generate cuts. The X initial is to be randomly selected to substitute in terms of *Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: csomsak@engr.tu.ac.th. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf

119


the constraints inequality equation. If the result, by substitution of X greater than 0, then

initial,

is equal to or

yi = gi. If the result (substitution of X initial) is less than 0, then yi = - h.

The xinitial and the selected yi are substituted in the Dual equation and in the inequality (to generate the optimal cut) to attain the maximum value of

ω and θ respectively. By

minimization of the θ + 0Xnew ; subject to θ + (y T A-c T ) ⋅ x new ≥ b T y , Equation (12) renders

θ and x new . If the obtained ω and θ are equal at the step of convergence test, ω and θ are to be approximately equivalent to zDual which can be obtained as an optimal solution.

Figure 2: TSL incorporated with Bender’s decomposition [6], [10], [11], [15].

2.6 Calculation Tools  ®

The mathematical calculation tool, MATLAB

Program was selected to solve this

®

problem. The MATLAB _Software / Verion.2006a and a HP_Pavillion_IntelCore_2Qurd Inside, No.: 016-120610000, personal computer, at the Department of Industrial Engineering,

120

Somsakaya Thammaniwit, and Peerayuth Charnsethikul


Thammasat University, Pathumtani were used. The calculation steps were as follows Step1. In accordance with the mathematic formulation, previous topic 2.4, the matrices systems were established according to the ordinary simplex method. The initial input variable matrix was expressed in two possible substitutions: randomization and dualsimplex algorithm. Step2. Model building according to the method of Bender’s decomposition followed the programming flowchart as shown in Figure 2. ®

Step3. Preparation of MATLAB programming named: lstart, start, setupmodel, solvemodel; linMAT, bender, dataABD_Uncertainty.mat, result, pline, selfcat, speye, and vspace Step4. Creation of data file: a, a_tol, b, b_tol, d, d_tol, fx, fles, fexd, flesd and fexdd Step5. Program execution by the program named: lstart (input parameter is P-Number).

2.7 Computational Calculation   To solve the above formulated problem, the primal-dual Simplex method /LINPROG, Equations (1) was applied to compare with two-stage stochastic linear programming incorporated with the method of Bender’s Decomposition, Equations (9), (10), (11), (12) referred to Equations (2), (3), (4), (6), (7), and (8). Assumptions: 1. The quantity of each nutrient value in each type of raw material is a continuous event. The values, which are independent of each other, are uncertain. However, the values intervals are recognized to be uniform distributions. 2. All scenarios are also uniformly distributed and independent on each other. As the solving tools for comparative calculation, the results of both selected method can be collected after the calculation iteration has terminated. The programs for primal-dual simplex and Bender’s decomposition were developed. The starting program referred to as ‘Start.m’ constitutes the main application to perform the execution of all relevant functions. The matrix systems corresponding with Equations (9), (10), (11), (12) are established. The program referred to as ‘linMAT.m’ is a matrix to receive all loaded input data whereas the *Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: csomsak@engr.tu.ac.th. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf

121


program name: ‘Speye.m’ serves to construct a large identity matrix system with the ‘Selfcat.m’ application to await the parameter patterns modification prior to the continuation of the Bender’s decomposition program ‘Bender.m’ [15]. Table 2: P Event Constr

Var

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50

23 75 159 275 423 603 815 1059 1335 1643 1983 2355 2759 3195 3663 4163 4695 5259 5855 6483 7143 7835 8559 9315 10103

4 16 36 64 100 144 196 256 324 400 484 576 676 784 900 1024 1156 1296 1444 1600 1764 1936 2116 2304 2500

10 36 78 136 210 300 406 528 666 820 990 1176 1378 1596 1830 2080 2346 2628 2926 3240 3570 3916 4278 4656 5050

Figure 3:

Lower section cut off at the uncertainty number of P=2:2:50.

x1_BEN x2_BEN x3_BEN 0.3631 0.3633 0.3632 0.3617 0.3617 0.3617 0.3618 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617

0.2420 0.2422 0.2422 0.2761 0.2768 0.2769 0.2739 0.2762 0.2761 0.2766 0.2764 0.2768 0.2763 0.2763 0.2763 0.2769 0.2767 0.2769 0.2765 0.2770 0.2764 0.2767 0.2768 0.2768 0.2767

0.3949 0.3946 0.3947 0.3622 0.3615 0.3614 0.3643 0.3621 0.3621 0.3617 0.3619 0.3615 0.362 0.362 0.362 0.3614 0.3616 0.3614 0.3618 0.3614 0.3619 0.3616 0.3615 0.3615 0.3616

Sx_BEN 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Z_BEN Ti_BEN x1_LIN x2_LIN x3_LIN Sx_LIN Z_LIN Ti_LIN 48.61 48.60 48.59 48.57 48.54 48.52 48.52 48.51 48.51 48.51 48.50 48.50 48.50 48.50 48.50 48.49 48.49 48.49 48.49 48.49 48.49 48.49 48.49 48.49 48.49

0.4924 0.1767 0.1995 0.1284 0.1818 0.2010 0.1472 0.1660 0.1456 0.1393 0.1514 0.1820 0.1695 0.1490 0.1589 0.1537 0.2377 0.1867 0.1819 0.1650 0.1649 0.1892 0.1923 0.1462 0.1504

0.3631 0.3633 0.3632 0.3617 0.3617 0.3617 0.3618 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617

0.2420 0.2422 0.2422 0.2762 0.2766 0.2769 0.2739 0.2762 0.2765 0.2766 0.2768 0.2769 0.2763 0.2765 0.2766 0.2767 0.2768 0.2769 0.2765 0.2766 0.2767 0.2768 0.2768 0.2769 0.2766

0.3949 0.3946 0.3947 0.3621 0.3617 0.3615 0.3643 0.3621 0.3618 0.3617 0.3616 0.3614 0.362 0.3618 0.3617 0.3616 0.3615 0.3614 0.3618 0.3617 0.3616 0.3615 0.3615 0.3614 0.3617

48.61 48.60 48.59 48.57 48.54 48.52 48.52 48.51 48.51 48.51 48.50 48.50 48.50 48.50 48.50 48.49 48.49 48.49 48.49 48.49 48.49 48.49 48.49 48.49 48.49

2.3399 0.0558 0.0868 0.2207 0.3620 0.8774 1.4670 1.1920 0.9501 0.2607 0.3611 0.7045 1.5480 0.6279 0.7172 0.5023 0.6613 2.2707 1.2727 0.8648 1.1724 0.8411 3.1807 3.4919 2.8000

The congruence of x1_BEN vs. x1_LIN; x2_BEN vs. x2_LIN and x3_BEN vs. x3_LIN, x3_LI

122

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Somsakaya Thammaniwit, and Peerayuth Charnsethikul


3 Results and Discussion  The calculation results were enumerated to check the calculation efficiencies of both the applied methodologies and the programming development. To be discussed in this research paper are the expected values of all response factors and their calculation times on a limited set of personal computers. Hence, there are three sections discussed as follow:

3.1 The result at the lower section (P = 2:2:50)  P = 2:2:50 denotes for the value of P starting at 2: increasing step 2: and ending at 50. According to assumption a) on page 6, the incremental event step E = P2 for uniform distribution and constraint number C = m × Event + PD. The values of x1_BEN, x1_LIN; x2_BEN, x2_LIN; x3_BEN, x3_LIN are congruent and consistent variants as represented in Table 2 and in Figure 3. The results of Zmin_BEN and Zmin_LIN represent their respective congruencies and at P= 32 (in Figure 4, at 16 on the axis) . However, consideration of the series plot of Ti_BEN and Ti_LIN in Figure 5 shows the calculation time fluctuations of Ti_LIN, but not for Ti_BEN.

Time Series Plot of Ti_BEN, TI_LIN 4

Variable Ti_BEN TI_LIN

Calculation time_ Ti

3

2

1

0 2 4 6 8 10 12 14 16 18 20 22 24 Number of point(P=2:2:50) divided in all uncertainties intervals

Figure 4: Zmin_BEN and Zmin_LIN

Figure 5: Ti_BEN and Ti_LIN

3.2 The results at the middle section (P = 50:2:106)  At the intensive computational events, the values of x1_BEN, x3 _BEN, z_BEN and x1_LIN, x3 _LIN, z_LIN are stable congruent with a very small consistent variance as reported in Table 3 below:

*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: csomsak@engr.tu.ac.th. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf

123


Table 3: Upper section cut off at the uncertainty number P = 50:2:106. P 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100 102 104 106

Event Constr 2500 2704 2916 3136 3364 3600 3844 4096 4356 4624 4900 5184 5476 5776 6084 6400 6724 7056 7396 7744 8100 8464 8836 9216 9604 10000 10404 10816 11236

5050 5460 5886 6328 6786 7260 7750 8256 8778 9316 9870 10440 11026 11628 12246 12880 13530 14196 14878 15576 16290 17020 17766 18528 19306 20100 20910 21736 22578

Var x1_BEN x2_BEN x3_BEN Sx_BEN Zmin_BEN Ti_BEN 10103 10923 11775 12659 13575 14523 15503 16515 17559 18635 19743 20883 22055 23259 24495 25763 27063 28395 29759 31155 32583 34043 35535 37059 38615 40203 41823 43475 45159

0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742

0.2767 0.2766 0.2772 0.2771 0.2768 0.2771 0.2768 0.2767 0.2766 0.2768 0.2764 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.3616 0.3617 0.3611 0.3612 0.3615 0.3612 0.3615 0.3616 0.3617 0.3604 0.3567 0.5166 0.5141 0.5116 0.5094 0.5085 0.5062 0.5045 0.5026 0.5009 0.4991 0.4987 0.4971 0.4956 0.4942 0.4929 0.4925 0.4913 0.4901

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9989 0.9947 0.8908 0.8882 0.8858 0.8835 0.8827 0.8804 0.8787 0.8768 0.8750 0.8733 0.8729 0.8713 0.8698 0.8684 0.8671 0.8668 0.8655 0.8643

48.49 48.49 48.49 48.49 48.48 48.48 48.48 48.48 48.48 48.48 48.47 48.39 48.18 47.98 47.78 47.60 47.42 47.24 47.07 46.91 46.75 46.59 46.45 46.30 46.16 46.03 45.90 45.77 45.65

0.2884 0.1726 0.2258 0.1420 0.1641 0.2000 0.2491 0.2022 0.2676 0.1525 0.2050 0.1865 0.1798 0.1804 0.2126 0.1892 0.1766 0.1828 0.1794 0.1692 0.1697 0.1991 0.1917 0.1981 0.2047 0.2016 0.2233 0.1791 0.1778

x1_LIN 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3617 0.3741 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742 0.3742

x2_LIN x3_LIN Sx_LIN Zmin_LIN Ti_LIN 0.2766 0.2767 0.2767 0.2768 0.2768 0.2769 0.2767 0.2767 0.2768 0.2766 0.2762 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.3617 0.3616 0.3616 0.3615 0.3615 0.3614 0.3616 0.3616 0.3615 0.3607 0.3568 0.5166 0.5140 0.5116 0.5094 0.5085 0.5064 0.5045 0.5026 0.5008 0.4991 0.4987 0.4971 0.4956 0.4942 0.4929 0.4925 0.4913 0.4901

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9990 0.9947 0.8908 0.8882 0.8858 0.8835 0.8827 0.8806 0.8787 0.8768 0.8750 0.8733 0.8729 0.8713 0.8698 0.8684 0.8670 0.8667 0.8655 0.8643

48.49 48.49 48.49 48.49 48.48 48.48 48.48 48.48 48.48 48.48 48.47 48.39 48.18 47.98 47.78 47.60 47.42 47.24 47.07 46.91 46.75 46.59 46.45 46.30 46.16 46.03 45.90 45.77 45.65

3.3737 1.2569 1.7699 1.3229 2.8755 2.3254 5.5466 1.5803 2.5353 3.0060 6.6884 3.0824 2.0440 1.4806 2.0009 1.5094 1.4550 3.0142 6.0361 5.9829 4.6769 1.9615 4.8717 10.8197 4.3436 3.0834 3.4999 7.3899 3.6450

According to the numerical consideration shown in Table 3, the values of x1_BEN , x1_LIN; x2_BEN, x2_LIN ; x3_BEN, x3_LIN are congruent and consistent variants. The optimal values of Zmin_BEN and Zmin_LIN are also congruent and have tendencies to converge to the most optimal solution. By contrast with this, the calculation times Ti_LIN and Ti_BEN are very different. The ratio of them is 2.5353:0.2676 = 9.474:1 at P = 66. The sums of Sx_BEN and Sx_LIN start to decrease after P = 66 as do the Zmin values also. Figure 6, 7 and 8 show that the components of x3_BEN and x3_LIN were selected , but not x2_LIN and x2_BEN at P=72. In an actual factory, the producers can make a decision at this stage with Sx = 0.8908 unit weight and Zmin = 48.39. If they want to obtain the sum Sx = 1.000 unit weight to meet demand, they can select the status of P = 66 with the optimal Zmin = 48.48 (higher cost). A further perspective, Figure 9 represents the series plot of Ti_BEN and Ti_LIN with major fluctuations. the calculation times of Ti_LIN are extremely variable, whereas the calculation time Ti_BEN tends to be constant.

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Time Series Plot of Sx_BEN, Sx_LIN

0.975 0.950 0.925 0.900 0.875

48.0 47.5 47.0 46.5 46.0 45.5

0.850 3 6 9 12 15 18 21 24 27 Numer of Point [50:2:106] divided in uncertainty intervals

3 6 9 12 15 18 21 24 27 Numer of Point [50:2:106] divided in uncertainty intervals

Figure 7: Min.cost Zmin_BEN and Zmin_LIN

Figure 6: Sx_BEN and Sx_LIN Time Series Plot of x1_BEN, x3_BEN, x1_LIN, x3_LIN 0.525

TimeSeries Plot of Ti_BEN, TI_LIN 12

Variable x1_BEN x3_BEN x1_LIN x3_LIN

0.500 0.475 0.450 0.425 0.400

Variable Ti_BEN TI_LIN

10 Calculation time_Ti

Selected x1_LIN, x1_BEN, x3_LIN, x3_BEN [Unit weight]

Variable Zmin_BEN Zmin_LIN

48.5 Minimum cost Z [Unit Currency]

Market demand [Unit Weight]

Time Series Plot of Zmin_BEN, Zmin_LIN Variable Sx_BEN Sx_LIN

1.000

8 6 4 2

0.375

0

0.350 3 6 9 12 15 18 21 24 27 Numer of Point [50:2:106] divided in uncertainty intervals

Figure 8: The congruence of x1_BEN, x3_BEN,x1_LIN, x1_BEN

3 6 9 12 15 18 21 24 27 Number of point(P=50:2:106) dividedinall uncertainties intervals

Figure 9: Ti_LIN and Ti_BEN.

3.3 The results at the upper section  Continuing the computational calculation by the Bender’s decomposition method with P = (106:2:1584) until the calculation terminated (out of memory on the HP_Pavillion_ IntelCore_2Quard Inside, No.: 016-120610000, personal computer), the nearest optimal solution can be accepted at P=1584 corresponding to Zmin of 38.63Baht and Sx_BEN of 0.8185 unit weight. Sx_BEN is not equal to 1 as the market demand. It depends upon the cost factors of flesd and fexdd . If flesd is less than fexdd, it will be reasonable to produce the mixed product with lower amount from the demand. But, the optimization can reveal the lowest value of the Zmin as shown in Table 4 below. *Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: csomsak@engr.tu.ac.th. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf

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Table 4: The upper section cut off at the uncertainty number P = (1560:2:1584) P

Eve nts

Constr

Var

x1_BEN x2_BEN x3_BEN Sx_BEN Z_BEN

Ti_BEN

1560

2433600

4868760 9737523

0.3743

0 0.4445

0.8187

38.64

10.9534

1562

2439844

4881250 9762503

0.3743

0.0002 0.4442

0.8186

38.64

10.2998

1564

2446096

4893756 9787515

0.3743

0 0.4442

0.8185

38.64

10.3893

1566

2452356

4906278 9812559

0.3743

0 0.4442

0.8185

38.64

10.5075

1568

2458624

4918816 9837635

0.3743

0 0.4453

0.8197

38.63

10.4842

1570

2464900

4931370 9862743

0.3743

0.0001 0.4444

0.8187

38.63

11.056

1572

2471184

4943940 9887883

0.3743

0 0.4442

0.8185

38.63

10.0722

1574

2477476

4956526 9913055

0.3743

0 0.4445

0.8188

38.63

11.0531

1576

2483776

4969128 9938259

0.3743

0.0002 0.4445

0.8188

38.63

11.0438

1578

2490084

4981746 9963495

0.3743

0.0011 0.4446

0.8189

38.63

11.373

1580

2496400

4994380 9988763

0.3743

0.0055 0.4441

0.8185

38.63

10.543

1582

2502724

5007030

1E+07

0.3743

0 0.4445

0.8187

38.63

11.7056

1584

2509056

5019696

1E+07

0.3743

0 0.4442

0.8185

38.63

10.5942

OUT OF MEMORY

4 Conclusions At the first start with a lower division number of points P, the results obtained from the simplex method-LINPROG and Bender’s decomposition were consistently equivalent. The calculation results were almost identical. These two algorithms are very suitable for smallscale problems

but when increasing the division numbers (point P) there is, a rise of

uncertainties numbers, the consequence of the enlargement of the constraint numbers. Some of response factors were found to deviate from the target and thus failed in condition. Nevertheless, the calculation by both methods can be performed. The LINPROG method is extensive in calculation time and thus requires a large memory storage. On the personal computer, the calculation failed to determine the results at the earlier stage. Conversely, the Bender’s decomposition method can quickly and consistently obtain the nearest optimal solution up to the calculation termination due to being out of memory. The ®

same problem and the same calculation tool, MATLAB program, were also performed on a high performance computer.

The results showed the enormous effect of the system

uncertainties mainly influencing the calculation times. It is noteworthy that the ratio of the mean time consumption of the LINPROG : BENDER is approximately equal to 232.77 :1 at P=2:2:500

on a general PC, whereas the results of the other response factors can be

congruent.

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The authors hope that this calculation method, the integration of Two-stage stochastic linear programming incorporated with the Bender’s decomposition method, compacted in ®

general form of a MATLAB

programming, can contribute to supporting decision making

in other operations research areas as a low cost effective calculation tool. For future research, this program is to be developed in form of a Graphic User Interface for convenience of use.

5 Acknowledgements Many thanks go to National Electronics and Computer Technology Center (NECTECH), THAILAND for kindly supporting access into their HPC-calculating system and the TechSourch Co.Ltd. (Thailand) for their research cooperation. Furthermore, the authors thank Mr. Rattaprom Promkham from Mathematics Department, Rachamonkala University of Technology Thanyaburi for programming suggestions.

6 References [1] A. E. Chappell, (1974): “Linear programming cuts costs in production of animal feeds”, Operation Research quarterly, 25(1): 19-26. [2] A. G. Munford, (1996): “The use of iterative linear programming in practical applications of animal diet formulation”, Mathematics and computers in Simulation, 42: 255-261. [3] E. Engelbrecht, (2008): “Optimising animal diets at the Johannesburg zoo”. University of Pretoria, Pretoria. [4] D. M. Forsyth, (1995): “Chapter 5: Computer programming of beef cattle diet”, in Beef cattle feeding and Nutrition, 2nd ed., T. W. Perry and M. J. Cecava, Academic Press, Inc, pp. 68. [5] I. Katzman, (1956): Solving Feed Problems through Linear Programming, Journal of Farm Economics, 38(2) (May, 1956): 420-429. [6] G.Infanger, George B. Danzig, (1993): Planning under uncertainty-Solving Large-Scale Stochastic Linear Programs, Stanford University. [7] G. Ausiello, P. Crescenzi, G.Gambosi, V.Kann, A. M. Spacecamda, (1998): Complexity and Approximation, Springer, Chapter 2. [8] Michel X. Goemans, David, P. William son, (1997): The primal-dual method for approximations algorithms and its application to network design problems, PWS Publishing Co., Chapter 4. [9] M. O. Afolayon and M. Afolayon, (2008) “Nigeria oriented poultry feed formulation software requirements”, Journal of Applied Sciences Research, 4(11): 1596-1602. *Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: csomsak@engr.tu.ac.th. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf

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[10] P. Charnsethikul, (2009): Theory of Primal/Dual and Benders’ Decomposition, Lecture notes Department of Industrial Engineering, Kasetsart University, Thailand. [11] P. Charnsethikul, (1996): Linear Programming with Constraint Coefficient Tolerances, IE Network Conference, Industrial Engineering in AD. 2000, 24-25 October 1996, Pattaya, Thailand: 287-298. [12] Robert M. Freund, (2004): Benders’ Decomposition Methods for Structured Optimization, including Stochastic Optimization, Massachusetts Institute of Technology. [13] Rosshairy Abd Rahman, Chooi-Leng Ang, and Razamin Ramli, (2010): Investigating Feed Mix Problem Approaches: An Overview and Potential Solution, World Academy of Science, Engineering and Technology. [14] S. Babu and P. Sanyal, (2009): Food Security, Poverty and Nutrition Policy Analysis: Statistical Methods and Applications. Washington, DC, USA: Academic Press. p.304. [15] S. Thammaniwit, (2013): A Stochastic Linear Programming Method for the Diet Problem under Uncertainties, Doctoral Thesis, Submitted to the Senate of Kasetsat University, Bangkhen, Bangkok, Thailand, and December 2013. [16] Wagner, H. M., 1977. Principles of operations research, with applications to managerial decisions, 2nd Ed., Introduction to Stochastic Programming Models, pp.651-699.

S.Thammaniwit is an Assistant Professor of Industrial Engineering Department at Thammasat University, Thailand. He received his Dipl.Ing. (Konstruktionstechnik: Werkzeugsmaschinen) from The University of Applied Science of Cologne, Germany in 1982. He worked and received on-the-job training with at least 14 German companies before working in the government sector in his country. He earned his Master’s degree in Manufacturing System Engineering under Chula/Warwick corporation program at Chulalongkorn University, Bangkok in 1992. Most of his research is involved with tools, machine tools design and construction as well as Engineering Management. Currently, he is pursuing a doctoral degree at Kasetsart University, Bangkhen, Bangkok, Thailand. Dr.P. Charnsethikul is an Associate Professor of Industrial Engineering Department, Kasetsart University, Bangkok, Thailand. He received his M.S, PhD. (Industrial Engineering) from Texas Technical University, USA. His research interests are in the area of Optimization, Operations Research, Numerical Mathematics & Statistics, Management Science, Production & Operations, Numerical Methods and Analysis with Applications in Safety Engineering. Since 2006 he has been acting as Deputy Dean of the Faculty of Engineering at Kasetsart University, Bangkhen, Bangkok, Thailand.

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

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2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

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

Securing Bank Loans and Mortgages Using Real Estate Information Aided by Geospatial Technologies a*

a

b

David Kuria , Moses Gachari , Patroba Odera and Rogers Mvuria

c

a

Department of Geomatic Engineering and Geospatial Information Science, Dedan Kimathi University of Technology, KENYA b Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, KENYA c Lutheran World Federation, Department for World Service, KENYA ARTICLEINFO

ABSTRACT

Article history: Received 10 November 2012 Received in revised form 25 January 2013 Accepted 28 January 2013 Available online 29 January 2013

Due to liberalization within the financial market, there has been increased cash flow in banks. This has resulted in increased competition among banks to secure and increase their customer base, in an effort to remain profitable. Banks are foregoing the multitude of checks that used to be conducted before granting any mortgage facility to customers, in an effort to remain competitive. This has led to a drastic increase in the number of credit card and loan defaulters, leading to increased operation costs and reduction in profit margins. This research proposes an integrated GIS approach enabling banks locate defaulting real estate properties used as collateral. Using data provided by Kenya Commercial Bank (KCB) for a locality in Kenya, a geodatabase was developed and a custom application developed for the bank loan appraiser to use. This application retrieves property information about a client based on his/her bank account information. Based on a series of spatially driven queries embedded in the solution, the appraiser can prepare a detailed appraisal for the client in a very short time, thereby satisfying the client, while not prejudicing the banks position.

Keywords: Banking; GIS; Loan appraisal; Information technology; Mortgage.

2013 INT TRANS J ENG MANAG SCI TECH.

*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: dn.kuria@gmail.com. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf

129


1 Introduction Geographic Information Systems (GIS) have been defined and conceptualized in a number of different but related ways. de Man (1988), Goodchild (1992), and Burrough (1986) argue that GIS is a special type of information system that handles spatial data. Dickinson and Calkins (1988) take a component view of GIS, arguing that a GIS has three elements: technology (hardware and software), a database, and infrastructure (staff, facilities, etc.). GIS technology has a great deal to offer the mortgage finance industry because geographic location and spatial relationships have a central role in housing and mortgage market outcomes. Housing is fixed in its location and is durable. As a result, a home’s location relative to employment opportunities, mortgage finance and housing market intermediaries, public services, and amenities exerts a strong influence on its price. The location of a home influences the choices and opportunities of its residents and of those seeking to own it and location is thus a strong influence on mortgage markets. The location of mortgage suppliers defines the availability of mortgage credit. The segregation of residential space into discrete geographic submarkets influences the pattern and nature of mortgage product demand. The highly segregated nature of residential space demands that mortgage lenders target their products, services, and marketing efforts to the specific character of different demographic groups that occupy different market areas Because the nature of the competition faced by participants in the mortgage finance process varies across different areas, they must develop different competitive strategies to suit the character of their competition in different areas. GIS technology has the potential to support a wide range of business applications in mortgage finance (Alberts and Douglas, 1992). At the most elemental level, it can provide mapping capabilities to help decision makers visualize the spatial distribution of variables that affect their business. At a higher level, it can be used to combine multiple variables such as the racial and income composition of neighborhoods with the location of recent mortgage originations. It can be used, for example, to select the optimal number, location, and size of branch offices to service a market area given some decision rules about how far any share of potential borrowers can be from a branch office (Birkin and Clarke 1998). In Kenya, the use of GIS is rather limited but growing steadily with the mainstreaming of GIS in many curricula in the Universities. Acquisition of georeferenced data is also an expansive undertaking including the data management and dissemination. Another limitation is

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poor consumer awareness which means less demand for the products and services of GIS. Regardless of these limitations, GIS has been used in Kenya for several projects with good result, for instance, in compiling the National Water master plan (Republic of Kenya, 1992). The Kenya Wildlife Services (Kariuki, 1992) used GIS for managing the large volumes of data they acquire relating to wildlife census, vegetation and land use dynamics, infrastructure, security and planning of operations. The department of resource surveys and remote sensing makes use of GIS and Remote Sensing in the mapping of natural resources (Ottichilo, 1986). An integrated GIS and remote sensing system has been developed for water resources management in Kitui county (Kuria, et al, 2012), while Mulaku and Nyadimo (2010) used GIS in the mapping of schools. Kuria, et al (2011) developed a GIS tool for enhancing efficiency in distribution of national examinations. GIS has also been used to prepare the National Environment Action Plan (Ministry of Environment and Natural Resources, 1994) and to monitor a development programmed in Laikpia District (Hoesli, 1995). In recent years, the banking industry in Kenya has been undergoing drastic changes reflecting a number of underlying developments. Significant advancement in surveying, architectural and information technology (IT) has accelerated and broadened the dissemination of real property information and financial services and also increased the complexity. Another key impetus for change has been the increasing competition among a broad range of domestic and foreign institutions in providing bank loans, mortgages and other related services. Regulations and computer technology advancement are forcing mortgage institutions to adopt better operational strategies and upgrade their skills, throwing more challenges to banking sector. One of the most tedious tasks in banking is providing mortgage services for their clients. Banks offer this service to enhance their accessibility to the customers. To cushion themselves, banks need collateral such as tangible (fixed) property or business assets etc. In order for a bank to sanction a mortgage, the property is analyzed. Some of these analyses include distance from the main road or size of the plot, verification of the actual owner of the plot, current land valuation etc. Currently in Kenya, banks accomplish these analyses manually by going to the sites or contacting third party to do the tasks. This process at the least takes 30 days to complete and involves a substantial expenditure in funds while following this due diligence. In case the same plot is being used by a client to request for other loans to the same bank or different banks *Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: dn.kuria@gmail.com. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf

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the analyses have to be done for each application cascading the problem. A GIS simplifies the process by easily accomplishing the following: (i) does the spatial analysis easily on the computer from the office, (ii) calculates the area or distance from main road or other features of interest without having to visit the site/plot, (iii) calculates the land value of the area by analyzing the surrounding area, (iv) creating a centralized or distributed database system that can be used to detect and prevent multiple loan applications utilizing the same plots, and (v) perform auction procedures and instructions without much difficulty. The objective of this study is to develop a pilot database for real property information within parts of Kilimani, Lavington and Kileleshwa estates that can be used to secure bank loans and mortgages. To accomplish this objective a geodatabase is developed with capabilities of analyses, and modeling of (i) present land use (ii) present land value (iii) information of the plot e.g. past owner, case, previous loan taking condition etc (iv) distance from the main road and (v) adjacent road width

2

The study area  The area chosen for study (Figure 1) covers extensive parts of Kileleshwa, Kilimani and

Lavington estates. It lies between (9857500m N, 242000m E) and (9859000m N, 254000m E). It covers an area approximately 3,000,000 m2. The area has approximately 1500 plots and over 1800 permanent buildings. Semi-permanent buildings were however not considered. Lavington is a suburb green haven lying halfway between the busier areas of Hurlingham and Westlands. Here the few modern apartment blocks are unobtrusive and the lanes linking busier roads are lined with large houses and bungalows set in an acre or less of well-tended land. Many have swimming pools. Some roads are gated with security staff screening all visitors. Lavington is popular with expatriate families. Kilimani is bounded to the north by Dennis Pritt Road and to the south by Ngong Road, bisected by Argwings Kodhek Road. T hese busy areas host many of Nairobi’s newly built apartment blocks. These are replacing the expansive 1950s and 1960s bungalows which once sat back from the wide streets bordered by high green hedges. Few of the new blocks are taller than five story and many enjoy balconies, communal swimming pools and 24-hour security staff. Kileleshwa is quieter and greener than Kilimani, Hurlingham or Ngong Road and more of the 1950s and 1960s bungalows, set in large mature gardens, have survived the property developers. However, there are some apartments

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David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria


blocks here too.

Figure 1: The study area.

2.1 Mortgage financing  The term mortgage refers to the process by which an individual or a business can purchase either a residential or a commercial property without having to pay the total value upfront. Mortgage is defined as a loan to an individual or a business for purchasing a real estate. In this case the real estate also acts as collateral for the loan. The mortgage contains two parts: (i) mortgage that is also the pledge and (ii) the promissory note which is the promise for repayment. Virtually all mortgage financing arrangements require one to put in some equity, with the financing institution funding a portion of the value of one’s new home. Most will require that one saves up for the required down payment with them, which is usually 15% to 30% of the purchase price. Paying more up-front works better in the long run, reducing the costs of the mortgage. One may also pay term deposits and various statutory fees for any transfers, charges and/or registrations to take place. As a rule of thumb, the combined legal and stamp duties work out to about 10% of the purchase price of the new home. Monthly repayments will also typically include the cost of insurance policies on the property offered as security for the loan and on mortgagor’s life.

*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: dn.kuria@gmail.com. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf

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3 Methodology The study area was chosen on the basis that it has undergone drastic development changes for the past few years due to changing zoning ordinances and building regulations. Emergence of apartments clearly indicates that many people are buying residential houses in this area. The prices of such apartments range from Ksh. 6,000,000 and some even over Ksh. 10,000,000. It is thus obvious that mortgage market will continue to be of great concern in this region. The banking sector thus needs to collect and maintain a spatial database of all spatially referenced information about the buildings that are on mortgage and all land parcels that acting as collaterals.

3.1 Data collection  In order to have a clear view of the study area some aerial photographs were used. These had the advantage of depicting the land uses as they appear and brought about clarity.

Figure 2: Digitized property map. A topo-cadastral map (map showing land subdivisions and the topography) of the area was scanned at a resolution of 600 dpi (dots per inch) and saved. The target areas of Kilimani, Lavington and Kileleshwa estates acted as the sample size of the project. The features considered are (i) transport network, (ii) cadastral subdivision, (iii) existing buildings, and (iv)

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David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria


land use. Figure 2 shows the land parcels in the study area, which can serve as collateral for mortgages.

Data capture was carried out in the following ways: (i) digitizing by scanning hardcopy maps(ii) onscreen digitizing and (iii) attribute entries. The topographic maps, survey plans and building plans were scanned using a flatbed scanner. The resulting images are a digital copy of the original paper based map or plan. Onscreen digitizing was done with great care since a deviation in parcel areas would mean a lot in the final analysis. This involved digitization of line, point or area features. Once all the required features had been digitized and stored in their corresponding layers, attribute entry was done. Buildings’ photographs and where possible floor layout plans were incorporated into the database in order to provide as much information as possible. In the following diagram, one can that the selected building is cut through by the boundary on the right. From visualization alone several conclusions can be drawn; number of floors, boundary case, building type, value, floor area, nearest road etc.

3.2 The conceptual system  A customer-centric business model can help address these challenges. A mortgage institution’s primary function is to deliver financial services and products to their customers. In the modern world they need to be market driven and market responsive. The success of such an institution depends on its approach to data management, customer relation management. Such institutions manage a bulk of information about customers, customer profiles and much more.

By incorporating ‘geographical location information of real property’ into their

database, long range planning mixed with geographical modeling will yield tangible benefits to the mortgage finance institutions community. Figure 3 shows the mortgage decision making process as conceptualized in this work. A GIS plays a central role tying the bank’s non-spatial data to spatial data. Utilizing the bank’s non-spatial data, the financial health of a client’s account can be easily obtained. Using the spatial data, property information can be retrieved and analyzed based on various attributes and spatial relationships with other features. From these analyses, the mortgage or loan value can be determined. Information about encumbrances and other restrictions on a property can also be retrieved. Since all these bits of information have been stored in a single centralized or distributed database, a quick decision (in a matter of minutes) can be arrived at on whether to *Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: dn.kuria@gmail.com. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf

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grant or deny the application. To aid in this decision making process, the logic captured in Figure 3 has been coded into an extension that calls up the geodabase and based on the parameters of interest (such as particular parcel, client, mortgage remaining, transaction histories) a set of decision pathways are proposed and presented to the appraiser using the system.

Figure 3: Conceptual model of the mortgage decision making process.

4 Results and Discussion  A pilot GIS database for real property information in Kileleshwa, Kilimani and Lavington has been developed. The database contains information on plots, plot numbers, plot sizes, plot values. Layers of related geographic features e.g. buildings, access roads and rivers are also incorporated in this database. Figure 4 shows an example of information retrieved for one land parcel and the building it contains. Managing such a bulk of geographical data including

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customer’s bank records is hectic and cumbersome. Prior to the incorporation of the GIS, decision making required some time and even a third party consultation. Now with the GIS in place, some of the checks and decisions can be made quite easily and in a cost effective fashion.

Figure 4: Retrieved geodatabase results for parcel and contained building.

Figure 5: Accessibility levels. In Figure 4, parcels with boundary disputes can be easily identified from the database, encroaching parcels, existing encumbrances, e.g. court orders, caveat, pending cases and other restrictions can also be effortlessly retrieved. It is evident that the distance from the main road *Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: dn.kuria@gmail.com. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf

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affects the value of any property; the further the property is from a main road, the lesser the value.

Figure 5 shows the accessibility levels. These levels were determined through a 15m buffering on either side of the road. Parcels in light green color had a high proximity to tarmac road, other parcels in light blue had a lower proximity to the road. They however had minor access roads linking to the tarmac roads. For land value computations purposes, the study area was assumed to have a high accessibility level in the interest of simplicity. Eighteen properties were sold out recently in the area; these were used to make buffers at distances of 5 km.

Figure 6: Property value distribution. Buffers are rings drawn around features at specified distance from the features. These buffers were meant to determine the value of all land parcels in the study area. It was concluded that the land values were homogeneous throughout the study area. The statistics of these plot values in shillings and areas in square meters were generated. From frequency distribution analysis of these recently sold out properties, a mean value of approximately KShs. 14,790,000.00 per acre was obtained. A similarly obtained mean area of the properties was found as 4990 m2. The following formulation (eq. 1) was used to obtain the prevailing value for a property.

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David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria


C=

C × 4047 × Ac A

(1)

Where Ac = property area (in acres) A = property area (m2) and C = value of property with the over-bar referring to mean value, with the value 4047 being the conversion factor (from acres to m2). It is worth mentioning that in the study area, errors in area computation can have serious value implications since 1 m2 costs KShs. 3,000.00. Based on these computations, a property value distribution map was prepared (Figure 6). It gives a clear view of prevailing land values. It is therefore easy to predict the range of any plot value at a glance. The most expensive parcels belong to Lavington Primary school and Kenton College – dark brown. The value depends on the size of the property, the larger the size the more expensive. Figure 7 provides insight to the bank on the distribution of property that the bank needs to subject to auction – in red. The plots in purple show mortgaged property. Tracking such property with the use of Global Position System (GPS) is easy and cost effective since their positional information can be obtained from the map and imported into the GPS devices for field crews.

Figure 7: Mortgages approved and defaulted.

*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: dn.kuria@gmail.com. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf

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An ArcGIS extension was developed that can be used to bring out a user interface to non-GIS appraisers. This was programmed in Visual basic and linked to the GIS database. The extension generated the interface shown in Figure 8. An account number input generated all other information related to that parcel. The database end user (bank employee) is only expected to input the account number for any customer and the program automatically retrieves all other parcel related information. All this is achieved within a GIS environment with the map of attributes in the background.

Figure 8: User interface for querying database.

Based on the amount of mortgage applied for and the credit history of a client, the application is able to flag the credit worthiness of the applicant. A healthy client is flagged with a green status, while one who either has encumbrances on the property or whose value of collateral is insufficient to guarantee the mortgage is flagged with a red status. In the case of one whose case needs further clarification an orange status is presented.

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5 Conclusion A GIS database for real property information within the study area has been developed. Attributes related to real estate were input from existing maps, with some being generated by the software such as areas and prices of land parcels. The geodatabase retrieves geospatial information from the mortgage appraiser. Performance of analysis, statistics and updating yielded the desired results. Storage and retrieval of spatial data was convenient, without a large storage capacity demands. Using the proposed system, a bank can effortlessly and efficiently manage the administration its financial products touching on spatial elements. This system has demonstrated capability of identification and tracking of defaulters, multiple loan or mortgage applications tied to the same property. It is able to process the credit worthiness of any client applying for a mortgage.

While this study has demonstrated the potential of GIS in the banking industry, banks still need to decide on the utility of GIS with respect to simplifying their loan and mortgage processing. The rapid emergence of apartments in high residential areas shows that mortgage market will continue to thrive and hence handle more geographic data. Such data needs a central storage with fast digital map retrieval capability; which is accompanied by any other related non-spatial data. This work considered data from one bank which was not linked to any other bank’s data and it is imperative that a centralized system for the banking sector can help deal with rogue defaulters from other banks. Sharing of these or any spatial data among bank branches can improve efficiency and thus reduce costs.

6 Acknowledgement The authors would like to acknowledge the Kenya Commercial Bank for providing valuable information on the loan procurement procedures and data used in this work.

7 References Alberts, R. J., and Douglas S. B. (1992). “Geographic Information Systems: Applications for the Study of Real Estate”, Appraisal Journal, 60(4), pp. 483–492. *Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: dn.kuria@gmail.com. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf

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Birkin, M. and Clarke, G. (1998). “GIS, geodemographics and spatial modelling: an example within the UK financial services industry”, Journal of Housing Research, 9(1), pp87-112. Burrough, P., (1986). Principles of Geographic Information Systems for Land Resources Assessment. Monograph on Soil and Resources Survey, (12), Clarendon Press, Oxford. 194 pp. Dickinson, H. J. and H. W. Calkins (1988). "The economic evaluation of implementing a GIS", International Journal of Geographical Information Systems, 2(4), pp. 307-327. Goodchild, M., (1992). “Geographical Information Science”, International Journal of Geographic Information Systems, 6(1), pp. 31-46. Hoesli, T. (1995). “GIS based impact monitoring of a development programme” Laikipia-Mount Kenya Papers. No.18. Maguire, D. J., (1991). “An overview and definition of GIS”, In Geographical Information Systems Principles and Applications, edited by D. J. Maguire, M. F. Goodchild, and D. W. Rhind. (New York: Longman Scientific and Technical; John Wiley and Sons, Inc.), pp. 9-20. de Man, E., (1988). “Establishing a geographic Information System in Relation to its Use”, International Journal of Geographic Information Systems, 2(2), pp. 245-261. Ministry of Environment and Natural Resources (1994). The Kenya National Environment Action Plan. Summary. Nairobi, Kenya. Mulaku, G.C. and Nyadimo, E. (2011). “GIS in Education Planning: The Kenyan School Mapping Project” Survey Review, 43(323): 567-578. Kariuki, A. (1992). “Applications of geographic information systems in the management of the wildlife resource.” In Applications of geographical information systems for efficient data storage and handling in Kenya. Okoth, P.F. (ed.). Proceedings of a symposium. Kenya Soil Survey, Nairobi. Pp.10-19. Kuria, D. N., Gachari, M. K., Macharia, M. W. and Mungai, E., (2012). “Mapping groundwater potential in Kitui District using geospatial technologies”. International Journal of Water Resources and Environmental Engineering, 4(1), pp. 15 – 22. Kuria, D. N., Ngigi, M. M., Wanjiku, J. W. and Kasumuni, R. K., (2011). “Managing distribution of national examinations using geospatial technologies: A case study of Pumwani and Central divisions” International Journal of Computer Engineering Research. 2(5), pp. 82 – 92.

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Ottichilo, W.K. (1986). “Food production, famine and early warning system: The Kenyan experience.” In: Proceedings of the 20th International Symposium on Remote Sensing of the Environment. Nairobi, Kenya. Environmental Research Institute of Michigan, Ann Arbor, Michigan, U.S.A. pp. 177-180. Republic of Kenya. (1992). “The study on the National Water Master Plan” Sectoral Report (S). GIS based analysis. Ministry of Water Development. Nairobi.

Dr. David Kuria is a Senior Lecturer in the department of Geomatic Engineering and Geospatial Information Science of the Dedan Kimathi University of Technology. He holds a B. Sc (Surveying) with Honors from the University of Nairobi (Kenya), an M. Sc (Photogrammetry and Geoinformatics) from the Stuttgart University of Applied Sciences (Germany) and a PhD from the University of Tokyo (Japan). Dr. Kuria’s current interests are in web mapping, climate research and geospatial application development.

Dr. Moses Gachari is an Associate Professor in the Department of Geomatic Engineering and Geospatial Information Science of the Dedan Kimathi University of Technology. He holds a B.Sc (Surveying and Photogrametry) with Honors from the University of Nairobi (Kenya), an M.Sc., and a PhD degrees from the University of Oxford (UK). Prof. Gachari has research interests in geospatial applications in development and environment, geodesy and surveying in general.

Dr. Patroba Odera is a lecturer in the Department of Geomatic Engineering and Geospatial Information System of Jomo Kenyatta University. He holds a B. Sc. in Surveying with Honors and an M. Sc. in Surveying from the University of Nairobi (Kenya) and a PhD from the Kyoto University (Japan). Dr. Odera’s research interests are in gravity determination, geodesy and geospatial technologies.

Rogers Mvuria is a Geomatics Engineer and GIS analyst with the Lutheran World Federation. He holds a B.Sc. in Geomatic Engineering and Geospatial Information Systems with Honors from the Jomo Kenyatta University of Agriculture and Technology. Mr. Mvuria’s research interests are in development of geospatial applications and surveying.

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

*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: dn.kuria@gmail.com. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf

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2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

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Theoretical Investigation of Hetero–Diels–Alder Functionalizations on SWCNT and Their Reaction Properties Danai Pankhao a, Nongnit Morakot a, Somchai Keawwangchai a, and Banchob Wanno a,b* a

Supramolecular Chemistry Research Unit, Department of Chemistry, Faculty of Science, Mahasarakham University, THAILAND b The Center of Excellence for Innovation in Chemistry (PERCH-CIC), THAILAND ARTICLEINFO

A B S T RA C T

Article history: Received December 2012 Received in revised form 28 January 2013 Accepted 04 February 2013 Available online 07 February 2013

Two–layered ONIOM method at the ONIOM(B3LYP/6–31G(d,p):AM1) theoretical level was applied to investigate the hetero Diels-Alder reaction functionalization of various nitrosoalkenes (NAs) and thionitrosoalkenes (TNAs) onto side–wall (5,5) armchair SWCNT. The results indicated that SWCNT can be functionalized with NAs and TNAs. The energy barriers of TNAs funtionalized SWCNT were lower than those of NAs. This implied that TNAs are easier to react with SWCNT than those of NAs. In addition, electronic properties and density of states of SWCNT were modified by the Diels-Alder functionalizations of NAs and TNAs.

Keywords: DFT; Hetero–Diels–Alder reaction; Nitrosoalkene; ONIOM; SWCNT; Thionitrosoalkene.

2013 INT TRANS J ENG MANAG SCI TECH.

1 Introduction During the last decade, many researches have been focused on functionalizations of single–walled carbon nanotube (SWCNT) to make fascinating new physical and chemical properties

for

practical

applications

(Meyyappan,

2005).

Generally,

chemical

functionalizations to SWCNT were achieved by covalent functionlizations onto the sidewall of tube at the sp2 carbon system. The experimental functionlizations using fluorine (Chamssedine *Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: banchobw@gmail.com 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf

145


et al., 2011), diazonium salt (Bahr et al., 2001), and fuming nitric acid (Kitamura et al., 2011), and theoretical functionalizations using azomethine ylides (Cho et al., 2008), nitrene (Zhang et al., 2006), ozone (Yim & Johnson, 2009), ethene (Lawson & Walker, 2012), alanine and alanine radical (Rajarajeswari et al., 2012), and diazomethyl aromatic compound (Raksaparm et al., 2012), pyrazinamide (Saikia & Deka, 2010) on SWCNTs were successful studied and reported. Chemical cycloaddition on buckminsterfullerene (C60) was reviewed by Yurovskaya and Trushhov (Yurovskaya, & Trushkov, 2002). Diels–Alder reaction is well known to occur between a conjugated diene and a dieneophile and is particularly useful chemical modification to construct cyclic compounds. This reaction has also been explored on the functionalizations of the C60 (Ohno et al., 1993; Ohno et al., 1995; Yang et al., 2006; Nakahodo et al., 2008; Yang et al., 2009). Interestingly, the Diels–Alder cycloadditions on the sidewall SWNT were successful studied by experimental (Delgado et al., 2004; Ménard-Moyon et al., 2006) and theoretical (Lu et al., 2002) methods. Nitrosoalkenes (Tahdi et al., 2002; Gallos et al., 2003) and thionitrosoalkenes (Bryce et al., 1994; Reed & Zhang, 2001) are a class of hetero dienes. In principle, the SWCNTs should be traceable to these reactions with hetero compounds such as nitrosoalkenes (NAs) and thionitrosoalkenes (TNAs). However, experimental and theoretical studies of the side–wall addition of nitrosoalkene and thionitrosoalkene to SWCNT have not yet appeared to the best of our knowledge. In the present work, the hetero-Diels–Alder reactions of nitrosoalkene and thionitrosoalkene compounds on armchair (5,5) SWCNT have been investigated by using the quantum calculation.

2 Computational Details  Two-layered ONIOM method at the ONIOM(B3LYP/6–31G(d,p):AM1) theoretical level was applied to geometry optimizations of all species of cycloaddition functionalization onto side-wall (5,5) armchair SWCNT. The model of SWCNT (C70H20 model) was chosen with open ends and the hydrogen atoms were used to saturate the carbon atoms at the two terminated ends of the tube (Figure 1). The ball atoms of a pyrene (C16) model cluster and those belonging to nitrosoalkene and thionitrosoalkene molecules were treated at the higher B3LYP/6–31G(d,p) level, and the remaining SWCNT atoms were treated with the AM1 method. Based on the two-layered ONIOM approach, a pyrene molecule shown as ball atoms of SWCNT was selected to be the high level layer. The hetero-Diels–Alder functionalizations were assigned to

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occur at the C1–C2 bond of SWCNT as shown in Figure 1. All of the structures of reactants, transition states and products were located by the ONIOM(B3LYP/6–31G(d,p):AM1) model achieved without any symmetry constraints. All transition states were characterized by single imaginary frequency. The vibration frequency computations were performed at 298.15 K and the standard pressure as applied in our previous works (Wanno & Ruangpornvisuti, 2006). The highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) energy gaps and density of states (DOSs) were also been determined. All calculations were performed with the GAUSSIAN 03 program (Frisch et al., 2008). The molecular graphics of all related species were generated with the MOLEKEL 4.3 program (Flükiger et al., 2000).

Figure 1: The optimized structures of single walled carbon nanotube (SWCNT), nitrosoalkene (NA) and thionitrosoalkene (TNA) reactants.

3 Results and discussion  The

structural

optimizations

of

(5,5)

armchair

SWCNT,

nitrosoalkene

and

thionitrosoalkene, their product and transition state structures were carried out at the ONIOM(B3LYP/6–31G(d,p):AM1) level of theory. The optimized structures of SWCNT, nitrosoalkenes, and thionitrosoalkenes are displayed in Figure 1. The selected bond distances and bond angles of the optimized structures are considered and discussed. The average C1–C2 bond distance of SWCNT was 1.380 Å which is in good agreement with the previous reports (Raksaparm et al., 2012). For the nitrosoalkene reactants, the C3–C4 bond distances were *Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: banchobw@gmail.com 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf

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1.335, 1.343, and 1.342 Å, respectively whereas the N–O bond distances are 1.220, 1.215, and 1.213 Å. The C3–C4–N bond angles were 123.7, 119.0, and 119.3°, respectively whereas the C4–N–O bond angles were 114.5, 115.5, and 115.3° for NA, PhNA, and NO2PhNA, respectively. The C3–C4 bond distances were 1.346, 1.353, and 1.352 Å, respectively whereas the N–S bond distances were 1.602, 1.596, and 1.594 Å. Moreover, the C3–C4–N bond angles were 128.2, 122.8, and 123.2°, respectively whereas the C4–N–S bond angle were 121.8, 123.8, and 123.6° for TNA, PhTNA, and NO2PhTNA, respectively.

Figure 2: The ONIOM(B3LYP/6–31G(d,p):AM1)–optimized transition state structures for nitrosoalkenes (above) and thionitrosoalkenes (bottom). Imaginary frequencies (in cm–1) are also presented. Considering the transition state as show in Figure 2, the reaction started from nitrosoalkene or thionitrosoalkene molecules reacted to C=C bond of SWCNT via the transition state to form the functionalized SWCNT products. For the transition state structures of nitrosoalkenes, the

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Figure 3: The ONIOM(B3LYP/6–31G(d,p):AM1) – optimized product structures for nitrosoalkene (above) and thionitrosoalkene (bottom) functionalizations. C1–O and C2–C3 bonds were formed at the sidewall of SWCNT, the C3–C4 and N–O bond distances were then elongated when comparing with its corresponding reactant structures. The *Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: banchobw@gmail.com 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf

149


C1–O bond distances were 2.268, 1.939, and 1.969 Å for NA, PhNA, and NO2PhNA, respectively, whereas the C2–C3 bond distances were 2.059, 2.176 and 2.168 Å for NA, PhNA, and NO2PhNA, respectively. For the transition state structures of thionitrosoalkenes, when the C1–S and C2–C3 bonds were formed at the sidewall of SWCNT which the C3–C4 and N–S bond distances were also elongated. The C1–S bond distances were found to be 2.649, 2.498, and 2.675 Å for TNA, PhTNA, and NO2PhTNA respectively, whereas the C2–C3 bond distances were 2.239, 2.088, and 2.221 Å for TNA, PhTNA, and NO2PhTNA, respectively. Geometrical structures of products are displayed in Figure 3 in which the products were represented the formation of the newly six-member ring of functionalized SWCNTs. The C1–O bond distances were 1.485, 1.486, and 1.493 Å for NA, PhNA, and NO2PhNA, respectively, while the C2–C3 bond distances were 1.583, 1.583, and 1.583 Å for NA, PhNA, and NO2PhNA, respectively, and C1–S bond distances were 1.928, 1.927, and 1.930 Å for TNA, PhTNA, and NO2PhTNA, respectively, while the C2–C3 bond distances were 1.583, 1.582, and 1.581 Å for TNA, PhTNA, and NO2PhTNA, respectively. After the functionalization completed each of C1 and C2 atoms formed 4 chemical bonds with neighboring atoms. This indicated that hybridizations of C1 and C2 atoms were completely changed from sp2 to sp3.

3.1 Reaction Energies and Energy Profiles  Energy profiles based on the ONIOM(B3LYP/6–31G(d,p):AM1) computation for the hetero-Diels-Alder functionalizations of nitrosoalkenes and thionitrosoalkenes onto SWCNT are displayed in Figure 4 and the reaction energies, reaction energy barriers, and imaginary frequencies of the functionalizations are listed in Table 1. The reaction profiles with initial reactants (R), transition state and reaction products (P) are also represented in Figure 4. The relative energy profiles showed that the energy barriers for nitrosoalkene functionalizations were 21.88, 24.76, and 27.64 kcal/mol for the NA, PhNA, and NO2PhNA, respectively. It should be noted here that the NA addition showed the lowest in the activation barrier. In addition, the energy barriers of the thionitrosoalkene functionalizations were 15.01, 13.16, and 12.32 kcal/mol for the TNA, PhTNA, and NO2PhTNA, respectively, in which the NO2PhTNA addition showed the lowest in the activation barrier. Clearly, all of functionalizations were occurred via exothermic process. In both system, these energy barriers are strongly dependent on the nature of the heteroatoms and the molecular geometries presented on the reaction.

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Figure 4: The reaction profiles and relative energy profiles (in kcal/mol) of (a) nitrosoalkene and (b) thionitrosoalkene functionalizations. Where R is reactants, TS is transition state and P is reaction products. Table 1: Reaction energies (ΔE), reaction barriers (ΔE ≠) and the imaginary frequencies (vi) for the transition state of hetero Diels-Alder functionalizations computed at the ONIOM(B3LYP/6–31G(d,p):AM1) level of theory. Reactions ΔE a vi b ΔE ≠,a Nitrosoalkene addition SWCNT+NA Æ NA/SWCNT SWCNT+PhNA Æ PhNA/SWCNT SWCNT+NO2PhNA Æ NO2PhNA/SWCNT Thionitrosoalkene addition SWCNT+TNA Æ TNA/SWCNT SWCNT+PhTNA Æ PhTNA/SWCNT SWCNT+NO2PhTNA Æ NO2PhTNA/SWCNT a In kcal/mol. b Imaginary frequencies (cm–1).

-0.11 -2.05 -2.60

21.88 24.76 27.64

561.4i 519.8i 536.8i

-8.55 -12.34 -12.95

15.01 13.59 12.32

498.5i 450.5i 507.6i

Table 2: The ELUMO and EHOMO energies and Egap of tube and its adduct complexes computed at the ONIOM(B3LYP/6–31G(d,p):AM1) level of theory Species ELUMOa EHOMOa Egapa, b SWCNT –2.28 –4.45 2.17 [2.20] c NA/SWCNT –2.31 –4.47 2.16 PhNA/SWCNT –2.29 –4.45 2.16 NO2PhNA/SWCNT –2.61 –4.60 1.99 TNA/SWCNT –2.35 –4.48 2.13 PhTNA/SWCNT –2.33 –4.45 2.12 NO2PhTNA/SWCNT –2.58 –4.61 2.03 a In eV. b Egap = ELUMO – EHOMO. c Computed at B3LYP/6–31G* level (reported by Zhou et al. 2004) *Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: banchobw@gmail.com 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf

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Figure 5: The density of states of the SWCNT, compared with (a) nitrosoalkene and (b) thionitrosoalkene complexes.

3.2 Electronic properties and density of state  The ELUMO and EHOMO energies and frontier molecular orbital energy gaps (Egap) of SWCNT and its adduct complexes computed at the ONIOM(B3LYP/6–31G(d,p):AM1) level are displayed in Table 2. The results showed that, Egap for the pure SWCNT was 2.17 eV which is in good agreement with the previous results (2.20 eV) reported by Zhou et al. (2004). For NA, TNA, PhNA, and PhTNA complexes with SWCNT, the Egap were slightly different from SWCNT. On the other hand, for the NO2PhNA and NO2PhTNA complexes with SWCNT, their Egap values were 1.99 and 2.03 eV, respectively which was different from the other products.

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Plots of the density of states of the NA, PhNA, and NO2PhNA and TNA, PhTNA, and NO2PhTNA functionalized SWCNTs, compared with the armchair (5,5) SWCNT are displayed in Figure 5. It was shown that electronic structure of the SWCNT was sensitive to the hetero Diels–Alder functionalizations. The band gaps of SWCNT near Fermi level become narrower, which suggested that the conductivity of SWCNT was modified by nitrosoalkene and thionitrosoalkene functionalizations.

4 Conclusion The hetero-Diels-Alder functionalizations of various nitrosoalkenes (NAs) and thionitrosoalkenes (TNAs) onto side-wall (5,5) armchair SWCNT were investigated by using the two-layered ONIOM method at the ONIOM(B3LYP/6-31G(d.p):AM) theoretical level. The results indicated that SWCNT can be functionalized with NAs and TNAs. The energy barriers of TNAs funtionalized SWCNT were lower than those of NAs. This implied that TNAs are easier to react with SWCNT than those of NAs.

In addition, hetero-Diels-Alder

functionalizations affected to electronic properties and density of states of SWCNT.

5 Acknowledgements The authors appreciate the Research Affairs, Tungmanee School, Ubonratchathani, for partial support of this research and the facility provided by Supramolecular Chemistry Research Unit, Department of Chemistry, Faculty of Science, Mahasarakham University. The Institute for the Promotion of Teaching Science and Technology, THAILAND, for financial support is also gratefully acknowledged. The authors are also grateful to Dr. Wandee Rakrai and Dr. Chanukorn Tabtimsai for their helps.

6 References Bahr, J.L., Yang, J., Kosynkin, D.V., Bronikowski, M.J., Smalley, R.E. & Tour, J.M. (2001). Functionalization of carbon nanotubes by electrochemical reduction of aryl diazonium salts: A bucky paper electrode. Journal of American Chemical Society. 123, 6536-6542. Bryce, M.R., Heaton, J.N., Taylor, P.C. & Anderson, M. (1994). Diels–Alder and ene reactions of new transient thionitrosoarenes (Ar-N=S) and thionitrosoheteroarenes (Het–N=S) generated from N-(arylaminosulfanyl) and N-(heteroarylaminosulfanyl)-phthalimides: synthesis of cyclic and acyclic sulfenamides. Journal of the Chemical Society, Perkin Transactions 1, 1935-1944. *Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: banchobw@gmail.com 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf

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50, 7337-7339. Yang, H.T., Wang, G.W., Xu, Y. & Huang, J.C. (2006). Heterocycloaddition of thermally generated 1,2-diaza-1,3-butadienes to [60]fullerene. Tetrahedron Letters. 47, 4129-4131. Yim, W.L. & Johnson, J.K. (2009). Ozone oxidation of single walled carbon nanotubes from density functional theory. The Journal of Physical Chemistry C. 113, 17636-17642. Yurovskaya, M.A. & Trushkov, I.V. (2002). Cycloaddition to buckminsterfullerene C60: advancements and future prospects. Russian Chemical Bulletin, International Edition. 51, 367-443. Zhang, C., Li, R., Liang, Y., Shang, Z., Wang, G., Xing, Y., Pan, Y., Cai, Z., Zhao, X. & Liu, C. (2006). The nitrene cycloaddition on the sidewall of armchair single-walled carbon nanotubes. Journal of Molecular Structure: THEOCHEM. 764, 33-40. Zhou, Z., Steigerwald, M., Hybertsen, M., Brus, L. & Friesner, R.A. (2004). Electronic Structure of Tubular Aromatic Molecules derived from the Metallic (5,5) Armchair Single Wall Carbon Nanotube. Journal of the American Chemical Society. 126, 3597-3607.

Danai Pankhao is an M.Sc. student at the Department of Chemistry, Faculty of Science, Mahasarakham University, THAILAND. He received a B.Sc. in Chemistry from Ubon Ratchathani Rajabhat University, THAILAND.

Dr.Nongnit Morakot is an Associate Professor of Department of Chemistry at Mahasarakham University. She received a B.Sc. and M.Sc. from Chiang Mai University, THAILAND. She holds a Ph.D. from Chulalongkorn University, THAILAND. Associate Professor Dr. Morakot is interested in Supramolecular Chemistry. Dr.Somchai Keawwangchai is working in the Department of Chemistry at Mahasarakham University. He received a B.Sc. in Chemistry from Mahasarakham University, THAILAND, He holds his Ph.D. from Chulalongkorn University, THAILAND. Dr. Keawwangchai’s research fields are supramolecular investigations and host–guest investigations, reaction mechanism investigations under non-catalytic and catalytic reactions of olefins on zeolite, organometallic catalysts. Dr.Banchob Wanno is working in the Department of Chemistry at Mahasarakham University. He received a B.Sc. in Chemistry from Mahasarakham University, THAILAND, and M.Sc. in Physical Chemistry from Mahidol University, THAILAND. He holds his Ph.D. from Chulalongkorn University, THAILAND. Dr. Wanno’s research fields are nanomaterials and nanosensors, reaction mechanism investigations and host–guest complex investigations.

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

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2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

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

The Phenomenology of Lamban Tuha: The Local Wisdom of South Sumatra Traditional Architecture a,b*

Ari Siswanto Ahmad Hariza

b

b

, Azizah Salim Binti Syed Salim , Nur Dalilah Dahlan ,

c

a

Faculty of Engineering, University of Sriwijaya, INDONESIA Faculty of Design and Architecture, Universiti Putra Malaysia, MALAYSIA c Faculty of Human Ecology, Universiti Putra Malaysia, MALAYSIA b

ARTICLEINFO

A B S T RA C T

Article history: Received 20 July 2012 Received in revised form 23 January 2013 Accepted 08 February 2013 Available online 14 February 2013

Local wisdom of traditional architecture is towards extinction along with the existence an increasingly neglected traditional house, including the one who understands it reduced drastically. Lamban Tuha in South Sumatra has demonstrated the ability to adapt to its environment and able to withstand natural catastrophes. The study used phenomenological method to reveal information from the first person who is considered experts on the local wisdom of Lamban Tuha. This study shows the construction of kalindang provide an excellence effect of providing high flexibility in case of earthquakes. The separation structure between lower, middle and upper parts is done to give building more flexible. Local wisdom is reflections of valuable experience which can be utilized as the concept of a sustainable housing development in the context of anticipate natural disasters. The existence of Lamban Tuha is an interesting experience that can be used as thoughts on designing earthquake resistant buildings.

Keywords: Phenomenology; Local wisdom; Traditional architecture; Lamban tuha, Earthquake resistant structures.

2013 INT TRANS J ENG MANAG SCI TECH.

1. Introduction South Sumatra has a rich history of diverse culture that is very stunning in architectural treasures. Culture is an expression of society in adapting to an environment adapted to the *Corresponding author (A. Siswanto). Tel. +60173167312 Email address: ari_sisw58@yahoo.co.id 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf

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necessities of life. One of the cultural heritages in architecture is traditional ulu house type called lamban tuha. Lamban tuha which means ancient house reflects the traditional house which resist to earthquake. Typical houses in Surabaya village are ulu house and gudang house. Ulu house is the common term for a traditional house outside the city of Palembang while gudang house types can be found in all areas in South Sumatra (Barendregt, 2003; Siregar and Abu, 1985). Ulu house recognized by the local community and are classified as lamban tuha, currently amounted two houses. The existence of lamban tuha 1 very impressive considering the house has been occupied for 11 generations. According to the heirs, lamban tuha 1 was founded first in the hamlet of Canti (now a forest), then it moved to Surabaya Talang village and finally lamban tuha moved to Surabaya village in Banding Agung sub district which closes to Lake Ranau.

One exceptional of lamban tuhas are the elastic ability of those traditional buildings against earthquake that happen in Liwa, Lampung province in 1933. Both lamban tuhas are the only building that remained standing despite the devastating earthquake in 1933, while the other buildings in the village of Surabaya collapsed and mostly flattened to the ground. Typical system of traditional construction similar to lamban tuha is only about four houses including the new ones.

Lack of attention from the public and local government and the financial inability of lamban tuha’s owners will caused the loss of assets in term of local wisdom (Oliver, 2006). Traditional houses in South Sumatra have demonstrated exceptional indigenous knowledge of our ancestors in shaping the quality of their lives. This indigenous knowledge will regain its meaning and value in the society, we should aware of the glory of the inherited tradition. The bearers of indigenous knowledge might be developed in recent and future for sustainable housing development.

2. Methodology Phenomenological approach is an attempt to reveal a phenomenology of the experience from a person in everyday life in the context of the time, place and consciousness (Creswell, 1998). Context of time has to do with history, important events, technology and character. Context of place has to do with users, objects, physical space, the atmosphere and the

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environment of human life. While the context of feeling have to do with experience, awareness and knowledge visible and invisible. Based on the objectives, this research used phenomenological method. In addition to in-depth interviews were carried out against the respondents, this research will also see the relevance of the information provided with the environmental conditions around it, the existence of traditional houses and history of the houses. The first information obtained by previous research, community leaders, the owner or tenant on the basis of their advices, and then traced the people who have a relationship with traditional houses such as local builders, carpenters, local leaders and experts (Satori and Komariah, 2009). Data collection will be primary data which consists of in-depth interview, physical traditional houses and secondary data which consists of literature, journal and research.

3. Analysis Three analyses are used in this research: a description, a comparison, and an evaluation. The description is about architecture style, system structure and detail of structures which are related to the environment, philosophy and their indigenous techniques. Interpretation of local wisdom of traditional architecture would be conducted as a part of description with sources of the owners / users, local community leaders, experts and local carpenters. The comparison is between the people experiences in applying local wisdom.

4. Discussion 4.1 Physical Characteristic of Lamban Tuha  Traditional knowledge, indigenous knowledge, and local knowledge refer to the long-standing traditions and practices of certain regional, indigenous, or local communities. Therefore, traditional knowledge also encompasses the wisdom, knowledge, and teachings of these communities. Traditional knowledge has been orally accepted for generations from person to person. The wisdom in creating natural system of thermal comfort is often found in traditional architecture (Hardiman, 2000). Slightly different, lamban tuha has shown evidence of local wisdom in the traditional house in anticipation of natural disasters such as earthquake. Meanwhile, result from collective local wisdom of the contextual been able to adjust over time and was attuned with nature and local lifestyle (Limthongsakul et al, 2005). *Corresponding author (A. Siswanto). Tel. +60173167312 Email address: ari_sisw58@yahoo.co.id 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf

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Vernacular that related to the process of designed and built it usually close relation between the form and the culture. Vernacular architecture has limitation in delivering a variety of expression however, at the same time in accordance with the characteristics of different situations can create their respective places (Rapoport, 1969). Similar to the traditional buildings in most parts of Indonesia, the South Sumatra traditional house shows characteristics of timber buildings on stilts in different system structure based on the geography while others is a kind of raft house. Due to different environment and culture, indigenous knowledge creates traditional architecture which is adaptive with their environments. South Sumatra traditional houses could be dismantled and rebuild in another location with mostly reusing of origin housing materials. The typical construction of traditional house is with flexible nail-less joints, and non-load bearing walls.

Lamban tuha has saddle-shaped roof that rise high and put the tiber angin (gable end) on front and rear parts of the roof (Figure 1). The high rise roof has rake cross at the top as other ulu house type. Distinctive roof form, relatively high and in accordance with the dimensions of the house can create the beauty that is easily recognizable from a distance (Zumthor, 1998). The construction of roof related to large span, wind and rain in specific areas. In different geography, dwellings including roof, reflect the local knowledge, local technology and environment (Ohno and Xihui, 2008). It explained very detail about roof structure, roof layer construction and support systems for pitched roof. Conventional construction systems of pitched roof in many countries always related to environmental conditions, cultural aspects and local knowledge, it is typically seen in traditional houses such as lamban tuha. In general, the roof truss structure of lamban tuha is very simple. Minimized the weight of steep roof of traditional houses is an important issue for smart construction (Gruber and Herbig, 2007). Expenses due to own weight, the wind and earthquake can reduce the risk of severe damage to the roof.

Lower construction part of lamban tuha is a series of pillars that have stone footings combined with a pile of round logs in rectangular shaped without finishing. Stacks of logs with a square form support the building load known as the kalindang. Kalindang which has 7 – 11 layers of logs uses the notch on each layer as connection (Figure 5). However, not all parts of the house supported by kalindang. Lamban tuha has a stair for entrance on the front side to toward the garang/porch and the other in the rear for services. Porch is a transition space before

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entering the house and serves for guests or a place to sit on an informal basis. The floor surface in different rooms in lamban tuha has no height difference.

Figure 1: Lamban tuha 1 (left) and lamban tuha 2 (right).

The composition of rooms in lamban tuha is very simple and tends to be symmetrical. Arrangement of rooms on lamban tuha is as follows: a. Garang (porch), a transition space. b. Lapang unggak (living room) c. Lapang doh (dining room) d. Lapang tengah (bedroom) e. Kebik (front porch) f. Parogan (side porch), storage for goods or old coconuts. g. Dapo (kitchen) h. Pagu hantu (attic), a place to store the heirlooms and spears sacred objects.

*Corresponding author (A. Siswanto). Tel. +60173167312 Email address: ari_sisw58@yahoo.co.id 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf

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Figure 2: Floor plan of lamban tuha 1(left) and changing orientation of lamban tuha 2 (right).

Lamban tuha 1 is still the original shape as before; there has been no change in orientation and buildings addition (Figure 2). In contrast to the lamban tuha 1, lamban tuha 2 has changed the orientation of the building and built new stair due to consider the access road (Figure 2). At first, lamban tuha 2 facing Qibla (west), then converted facing east because the road consideration. As a result, the main entrance at the western is cut and moved to the east by making entrance door facing the north.

4.2 Local Wisdom of Lamban Tuha Acting as Earthquake Resistant    Based on the identification of floor plan, indigenous building materials and timber construction system, lamban tuha has local wisdom that can be proved by experience that is quite convincing during occupied by 11 generations (lamban tuha 1) and 6 generations (lamban tuha 2) until today. During that time, lamban tuha 1 has moved for three times and hit by a severe earthquake in 1933 which had destroyed all the buildings except lamban tuhas in the village of Surabaya. The site selection to establish lamban tuha based on the land that has good carrying capacity, far from the possibility of landslides or flooding. While the orientation of

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lamban tuhas face the Qibla, or in this case is the west. Until know, lamban tuhas never hit by floods and landslides struck.

According to the informants, people who sleep in lamban tuha should put the head and feet directed west to the east, may not sleep in addition to that direction. They believe there are certain rules of superstitious that apply to lamban tuhas (Fishwick and Vinning, 1982). Attic is an important part of the house is believed to be a sacred place, so this place is respected and used to store the heirlooms of their ancestral heritage. Believe in a supernatural or who has the power associated with the presence of the house is something that is common in the past.

Lamban tuha has a simple floor plan without a rigid division of rooms and tends to symmetrical. This indicates if the relationship between family members is very close, open and has the nature of togetherness. Social life and communication between family members are very close and communal. The simplicity of the floor plan and symmetrical shape is very precise from that anticipates the influence of the earthquake. Symmetrical shapes can create a balance of construction in every corner of the house when rocked by an earthquake.

The effect of earthquake was the collapse buildings because of bad reinforcement structures, unreinforced masonry walls and brick walls. On the other hand, timber houses performed relatively well compare to brick house during the earthquakes (Maidiawati. and Sanada, 2008). Traditional houses still stand usually because of using timber structure, lighter building material, and applying flexibility of structure. Furthermore, materials and structures that are used in traditional houses have been made to reduce the effects that occur in the event of earthquake (Audefroy, 2011). Lamban tuha, a typical traditional house in South Sumatra has identifiable timber structure which resist to earthquake.

Most of the sufferers of the

earthquake are the victims of collapsed concrete structures (Gruber, 2007). Building of traditional architecture has a symmetrical shape and express in the form of floor plan and facade. The concept of the design through the axis of symmetry generally implies a balance of organization and function space with macro cosmos. Emphasizes a balance by referring to the axis is the most elementary concepts of earthquake resistant buildings. Floor plan of lamban tuha is a simple open plan design while the physical form of the house tends to be symmetrical *Corresponding author (A. Siswanto). Tel. +60173167312 Email address: ari_sisw58@yahoo.co.id 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf

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and proportional. The horizontal load displacement characteristics of a traditional timber house can be simulated fairly well by adapting a mud wall and hanging wall models. This model is embodiment of Japanese culture that has so concern about their familiar natural disaster such as earthquake and typhoon (Fujita et al, 2004.). Most traditional houses in South Sumatra are timber houses, only a small part of the house with bamboo or a combination of both.

Figure 3: Fundamental timber construction of press, pivot, pinch and pull.

When lamban tuha 1 was built, indigenous building materials were collected in advance by soaking in Lake Ranau. After the perceived amount sufficient, then the house was built with no nails, just using timber connection that is fundamental of press, pivot, pinch, and pull (Figure 3). The construction of lamban tuha could be dismantled because it has nail-less timber construction (Figure 4). This typical construction can provide excellent flexibility in case of oscillation due to earthquakes.

Figure 4: Nail-less timber construction of lamban tuha.

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In addition to separating the construction of the house with lower construction, the separation is also reinforced by the provision of ijuk (palm fibres) on the stone footing between stilts with beams, kalindang with beams, kalindang with stone footing and stack wood blocks on kalindang (Figure 7). Interestingly, the fibres can be seen by its presence at the bottom construction of lamban tuha 1 (Figure 7). The informant strongly believes that the palm fibres can serve as a sort of bearing on the structure in anticipation of earthquake. Lamban tuha 2 does not use palm fibres in separation between structures. This difference indicate if an understanding of fibres function of bearing structures have not understood more as a local wisdom.

Figure 5: Kalindang of lamban tuha 1 (9 layers) and kalindang lamban tuha 2 (11 layers).

Lamban tuha has three important parts of construction that is the bottom, the middle and the upper (Figure 6). Construction of the bottom part is the poles and kalindangs, construction of the middle part is the framework of the house while construction of the upper part is the roof truss. Further information mentioned that the owner of lamban tuha 1 had planned a strong and sturdy timber construction system but also can be flexible during an earthquake. The area around Lake Ranau is prone to earthquake disaster. Therefore, the construction lamban tuha 1’s body is separated by lower structure, the construction of the house just rested on the structure; this gives the effect of high flexibility. In the context of house construction, there are interesting things, piles on the outer wall are not in a straight line with stilts or in other words, the outer wall is cantilever, about 30 cm from the composition of stilts.

*Corresponding author (A. Siswanto). Tel. +60173167312 Email address: ari_sisw58@yahoo.co.id 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf

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Earthquakes give bigger impact to reinforced concrete building than traditional building (Dogangun et al, 2006). Relatively, most type of traditional building performed well during earthquakes. Some tribes in Sumatera have local wisdom about timber construction which resist to earthquake. Some type of traditional timber house construction which resist to earthquake is not found in other areas such as traditional nias houses in North Sumatra, gadang house in West Sumatra and lamban tuha in South Sumatra. Typical wood construction shows in understanding the specific geographical conditions to adapt and survive. Lamban tuha 1 has kalindang at four points while the lamban tuha 2 only has kalindang at two points. The number of kalindang adapted to the dimensions of the house, the more spacious houses more kalindang required.

Figure 6: The separation of structure into three parts (lower, middle and upper).

Lower construction of lamban tuha consists of stilts and wooden blocks shaped square called kalindang (Figure 6). Poles and kalindangs as a whole bear lamban tuha. Poles and kalindangs rested on stone footing, it also provides high flexibility in case of shaking during earthquakes (Rautela and Joshi, 2008). This condition also can keep the timber from moisture and termites influence.

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Based on the information, the main strength of the lamban tuha 1 is four pillars as main structure inside the house that bear the beams, these beams become the basis for pillars in the attic. Construction of lamban tuha 1 does not have a constant pillar intact from the bottom up to the roof.

Figure 7: The lower structure of lamban tuha (kalindang and stone footings)

Traditional houses in Nias are based on the structure of vertical and slanted posts structures placed on a stone footing. Vertical posts and X and V are strengthens the element of this substructure. A three-dimensional structure offers greater resistance and has the elasticity required for not sticking in the ground (Gruber, 2007). Based on the experience of local communities, kalindang construction on the lamban tuha has a big role in anticipating the effects of earthquakes.

South Sumatra traditional architecture belongs to the grand tradition and requires special skills and expertise in indigenous knowledge. Traditional architecture is not only beautiful and elegant but also has flexible nail-less construction that has been proven to be earthquake resistant buildings. This technique adds to the flexibility of the house. Indigenous knowledge there is representing local wisdom that people have developed for centuries. It is based on long experience, adapted to local culture and environment.

*Corresponding author (A. Siswanto). Tel. +60173167312 Email address: ari_sisw58@yahoo.co.id 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf

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5. Conclusion In principle, lamban tuha have different lower construction system with other traditional houses in South Sumatra. A series of stilts and kalindangs worked as a system that supports the load of the house. The building is only supported by wooden pillars and beams as a foundation and located above stone footing and the pile of timber logs (kalindang).

Besides kalindang, lamban tuha which has connections and details of timber without nails believed to be powerful force able to withstand earthquake shaking. The timber connections are very appropriate considering the tensile strength and shear caused by the earthquake. Placement of kalindang to support the weight of the house is symmetrical and synergize with stilts resting on stone footings. Overall, the timber connection practices form of press, pivot, pinch, and pull with reinforced by the dowel.

Local wisdom is reflections of valuable experience from the South Sumatra traditional architecture which can be utilized as the concept of a sustainable housing development in the context of anticipate natural disasters such as floods, landslides and earthquakes.

The

existence of lamban tuha is an interesting experience of our ancestors that can be used as thoughts on designing earthquake resistant buildings.

6. Acknowledgements The authors would like to show gratitude to the respondents who took time and patience to share their experience of phenomenology. Also the authors deliver high appreciation to the University of Sriwijaya, Indonesia for providing financial support for field data collection.

7. References Audefroy, J. F. (2011). Haiti: Post-earthquake lessons learned from traditional construction. Environment and Urbanization, 23(2), 447-462. Barendregt, B. (2003). Architecture on the move. Processes of migration and mobility in the South Sumatran highlands. In Indonesian Houses Tradition and transformation in vernacular architecture., eds. R. Schefold, J. M. P. Nas & G. Domenig. KTLV Press, Leiden. Creswell, J. W. (1998). Qualitative Inquiry and Research Design Choosing among five

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Ari Siswanto is a Ph.D. Student at the Department of Architecture, Faculty of Design and Architecture, University Putra Malaysia. MALAYSIA. He is a lecturer at the Department of Architecture, Faculty of Engineering, University of Sriwijaya. INDONESIA. He received a B.Sc. in Architecture from 10th November Surabaya Institute of Technology, INDONESIA and Master of City & Regional Planning from The Ohio State University, USA.

Dr. Azizah Salim is an Associate Professor of Department of Architecture at Universiti Putra Malaysia. She received a B.Sc. in Architecture from Robert Gordon’s Institute of Technology, Aberdeen, SCOTLAND and M.sc. from University College London. U.K. She holds a Ph.D. from University Newcastle-upon-Tyne in U.K. Her interest is in research related to housing and development policies

Dr. Ahmad Hariza is an Associate Professor of Department of Architecture at Universiti Putra Malaysia. MALAYSIA. He received a B.Sc. In Human Development from University Pertanian Malaysia. MALAYSIA. He received M.Sc. and holds a Ph.D. from The University of Birmingham, U.K. His research in housing involves person and environment relationship and housing studies.

Dr. Nur Dalilah Dahlan is a senior lecturer at the Department of Architecture at Universiti Putra Malaysia. She received a B.Sc. in Architecture from University of Malaysia, MALAYSIA and M.sc. In Architecture from Universiti Putra Malaysia. MALAYSIA. She holds a Ph.D. from Cardiff University. U.K. Her interest is in studies how people behave in response to their sensual perceptions when exposed to architectural ambiances developed using passive design approaches.

Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines given at the journal’s website. Note: The original of this article was accepted and presented at the 2nd International Conference-Workshop on Sustainable Architecture and Urban Design (ICWSAUD) organized by School of Housing, Building & Planning, Universiti Sains Malaysia, Penang, Malaysia from March 3rd -5th, 2012.

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Ari Siswanto, Azizah Salim Binti Syed Salim, Nur Dalilah Dahlan and Ahmad Hariza


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