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CONTENTS

Dr Goran Putnik ADVANCED MANUFACTURING SYSTEMS AND ENTERPRISES: CLOUD AND 127 - 134 UBIQUITOUS MANUFACTURING AND AN ARCHITECTURE MSc Bojan Jovanovski, Dr Robert Minovski, Dr Siegfried Voessner, Dr Gerald Lichtenegger 135 - 142 COMBINING SYSTEM DYNAMICS AND DESCRETE EVENT SIMULATIONOVERVIEW OF HYBRID SIMULATION MODELS Dr Isabel L. Nunes 143 - 146 FUZZY SYSTEMS TO SUPPORT INDUSTRIAL ENGINEERING MANAGEMENT Dr Mirjana Misita, Dr Galal Senussia, MSc Marija Milovanović A COMBINING GENETIC LEARNING ALGORITHM AND RISK MATRIX MODEL 147 - 152 USING IN OPTIMAL PRODUCTION PROGRAM Jelena Jovanović, Dr Dragan Milanović, Dr Milić Radović, Radisav Đukić INVESTIGATIONS OF TIME AND ECONOMIC DIMENSIONS OF THE COMPLEX 153 - 160 PRODUCT PRODUCTION CYCLE Dr Vidosav Majstorović TOWARDS A DIGITAL FACTORY - RESEARCH IN THE WORLD 161 - 165 AND OUR COUNTRY Dr Jezdimir Knežević TIME TO CHOOSE BETWEEN SCIENTIFIC AND ADMINISTRATIVE 167 - 173 APPROACH TO RELIABILITY EVENTS REVIEW

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ANNOUNCEMENT OF EVENTS 175 - 176 BOOK RECOMMENDATION

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INSTRUCTIONS FOR AUTHORS 178 - 179 EDITORIAL AND ABSTRACTS IN SERBIAN LANGUAGE 180 - 184

Institute for research and design in commerce & industry, Belgrade. All rights reserved.

Journal of Applied Engineering Science 10(2012)3


EDITORIAL

President of the SIE 2012 Organizing Committee PREFACE TO SPECIAL TOPIC: SELECTED PAPERS FROM THE 5TH INTERNATIONAL SYMPOSIUM OF INDUSTRIAL ENGINEERING – SIE 2012

The 5th International Symposium of Industrial Engineering – SIE 2012, held in June, 2012 in Belgrade, Serbia, was aimed at providing a unique platform to meet frontier researchers, scientists, as well as practitioners and share cutting-edge developments in the field. The SIE 2012, fifth in the series of SIE meetings, was organized by Industrial Engineering Department, Faculty оf Mechanical Engineering, University of Belgrade, Serbia and Steinbeis Advanced Risk Technologies Stuttgart, Germany. The Symposium also fostered networking, collaboration and joint effort among the conference participants to advance the theory and practice as well as to identify major trends in Industrial Engineering today. Proceedings with over 70 papers and disscusions by more then 160 authors have contributed to better comprehension the role and importance of Industrial Engineering in this country, both in domain of scientific work and everyday practice. Despite the widely varying backgrounds and interests of the participants, the schedule of the meeting kept them all fully engaged by providing a platform where ideas at the cutting edge of industrial engineering could be exchanged with great enthusiasm. We think that the synergies and international collaborations resulted through sharing of knowledge and cross-fertilization of ideas at the earlier SIE meetings led to greater maturity at the SIE 2012. This traditional symposium has made a great contribution to improving awareness of the importance of industrial engineering at the international, national and local levels. The presentations, which were organized during the symposium, presented the achievement at all levels of scientific research through a systematic approach, to specific practices, both in large systems and small and medium enterprises. Also, further development of cooperation in the field of industrial engineering with the aim of realization of a larger number of projects of international domain is necessary. Great dynamics trends in the field of industrial engineering require expertise and wisdom to preserve the long-lasting knowledge base of industrial engineering, together with establishment of flexibility and customization, that new challenges brought by the times in which we live and do business. Issues number 3 and 4 of the Journal of Applied Engineering Science contain a selection of papers on all aspects of interdisciplinary themes treated in the SIE 2012. There are 11 papers that cover topics in the fields such as quality management, production management, risk management, project`s appraisal etc. The most relevant contributions are selected after a standard review process by disciplinary experts. We give thanks to the members of SIE Scientific Committee and authors, as well to our sponsors. Special thanks are due to the staff of the Journal of Applied Engineering Science and all the referees for their careful work. Belgrade, June 2012 Prof. dr Vesna Spasojević-Brkić

Journal of Applied Engineering Science 10(2012)3


doi:10.5937/jaes10-2511

Paper number: 10(2012)3, 229, 127-134

ADVANCED MANUFACTURING SYSTEMS AND ENTERPRISES: CLOUD AND UBIQUITOUS MANUFACTURING AND AN ARCHITECTURE Dr Goran Putnik * University of Minho, Faculty of Engineering, Braga, Portugal In this paper, in the first part an introduction to development of the concepts of Ubiquitous and Cloud Manufacturing is presented, as a model of advanced manufacturing systems and enterprises. In the second part an architecture, that might guide the implementation and exploitation of the Ubiquitous and Cloud Manufacturing is presented through an informal and conceptual presentation. Key words: Ubiquitous, Manufacturing systems, Enterprises, Clouds, Architecture, Paradigm INTRODUCTION The traditional Manufacturing was superseded. The new dynamic and global business model forced traditional production processes to change in the sense of to be integrated in a global chain of resources and stakeholders. The agility and quick reaction to market changes is essential, and the high availability and capacity to effectively “answer” to requirements is one of the main sustainability criterion. “Globalization, innovation and ICT are transforming many sectors to anywhere, anytime platforms”, towards an intelligent business model under “design anywhere, make anywhere, sell anywhere” paradigm [03]. We would add “anytime” too. Traditional suppliers and customers are “transformed” in services, where supplying or using profiles are a question of needs or context. One service (a Calculator, for instance) can execute (supply) something using other services (Add, Sub, Mult and Div operations) [15]. All these performances are considered on Ubiquitous and Cloud Manufacturing [08, 09, 18] suggest a manufacturing versionof ubiquitous and cloud computing (respectively) – ubiquitous and cloud manufacturing – and manufacturing with direct adoption of ubiquitous and cloud computing technologies. In this context, resources are seen as services, essentially. This manufacturing service-oriented network can stimulate productionoriented to service-oriented manufacturing [01].

towards these virtual architecture. To use efficiently those infra-structures the applications must be transformed and follow services oriented applications pattern. In this paper, in the first part an introduction to development of the concepts of Ubiquitous and Cloud Manufacturing is presented, as a model of advanced manufacturing systems and enterprises. In the second part an architecture, that might guide the implementation and exploitation of the Ubiquitous and Cloud Manufacturing is presented through an informal and conceptual presentation. MANUFACTURING AS SERVICE SYSTEMS Industrial and Product-Service Systems (IPS2) represents a “paradigm shift from the separated consideration of products and services to a new product understanding consisting of integrated products and services creates innovation potential to increase the sustainable competitiveness of mechanical engineering and plant design. The latter allows business models which do not focus on the machine sales but on the use for the customer e.g. in form of continuously available machines. The business model determines the complexity of delivery processes. Characteristics of Industrial Product-Service Systems allow covering all market demands” [05]. Figure 1 shows service offer of Mori Seiki, while Figure 2 and Figure 3 shows types of Product-Service Systems and scientific fields of action respectively.

Many of existent infra-structures are already ubiquitous and/or cloud based or are changing * Faculty of Engineering, Largo do Capo, 4704-553, Braga; putnikgd@dps.uminho.pt Paper presented at the SIE 2012

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Figure 1: Service offer of Mori Seiki [06, 05].

Figure 2: Types of Product-Service Systems [06,05]

Figure 3: Scientific fields of action [05]

UBIQUITOUS SYSTEMS Ubiquity is a synonym for omnipresence, the property of being present everywhere (Wikipedia). “The state or quality of being, or appearing to be, everywhere at once; actual or perceived omnipresence: the ability to be at all places at the same time; usually only attributed to God” (Wiktionary).

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According to Weiser (1993) Ubiquitous Computing represents: “Long-term the PC and workstation will wither because computing access will be everywhere: in the walls, on wrists, and in “scrap computers” (like scrap paper) lying about to be grabbed as needed.”

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Dr Goran Putnik - Advanced manufacturing systems and enterprises: cloud and ubiquitous manufacturing and an architecture

Computing technology has evolved up to the point when Ubiquitous Computing System development and operation are possible, using present network devices, protocols and applications. From the other hand, ubiquity has been addressed in relation to manufacturing systems as well. In (Foust, 1975) [04] “the term “ubiquitous”” is “explicitly defined to be functional in an empirical context …The types of manufacturing which are both market oriented and have a frequency of occurrence greater than a specific limit which can be empirically defined are ubiquitous. …”. Foust (1975) cites Alfred Weber’s definition of ubiquitous manufacturing too: “Ubiquity naturally does not mean that a commodity is present or producible at every mathematical point of the country or region. It means that the commodity is so extensively available within the region that, wherever a place of consumption is located, there are … opportunities for producing it in the vicinity. Ubiquity is therefore not a mathematical, but a practical and approximate, term (praktischerNaherungsbegriff).” To the above definitions (by (Foust, 1975) and (Weber, 1928)), [16] which consider ubiquity of resources – anywhere, we add the ubiquity in time – anytime, which (the “anytime”), from its “side”, implies the dynamic, on-line, seamless, enterprises’ organizational and manufacturing system networking and reconfigurability, or adaptability, that requires new organisational architectures and meta-enterprise organizations as creating and operating environments, makes the UMS a true new paradigm.

Theredore, Ubiquitous Manufacturing Systems and Enterprises concept is related to the availability of management, control and operation functions of manufacturing systems and enterprises anywhere, anytime, using direct control, notebooks or handheld devices. It is related with Ubiquitous Computing Systems. Ubiquitous Manufacturing Systems (UMS), therefore, implies ubiquity of three general types of resources in organizations: • material processing resources (e.g. machine tools and other manufacturing/production equipment as resources), • information processing resources (e.g. computational resources – includes hardware and software), and • knowledge resources (i.e. human resources, considering the humans as unique resources for knowledge generation and new products and services creation, and, at the end, the ultimate effectiveness of organizations). However, there are two quite different approaches to the concept of UMS. • The first concept, considers ubiquity of the MS based on, i.e. uses, the ubiquitous computational systems (UCS), Figure 4.a, • The second one which is original our approach, considers ubiquity of the MS as a homomorphism , i.e. it is a mapping, of the ubiquitous computational systems (UCS), Figure 4.b [08, 09, 10].

Figure 4: a) UMS has UCS as an operating system only – Ubiquity of Computational resources only; b) UMS operates as UCS – Ubiquity of all Resources: Material processing, Knowledge, and Computational resources [10]

The similar idea was referred in (Murakami &Fujinuma; 2000), (ref. in (Serrano & Fischer; 2007)). This approach is referred as well as “Ubiquitous Journal of Applied Engineering Science 10(2012)3, 229

networking” that “emphasises the possibility of building networks of persons and objects for sending and receiving information of all kinds

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and thus providing the users with services anytime and at any place”. The hypothesis is that UMS should be based on a “hyper”-sized manufacturing network, consisting of thousands, hundreds of thousands, or millions of “nodes”, i.e. of manufacturing resources units, freely accessible and independent, Figure 5. Further implications are that 1) UMS manufacturing units should be, in the limit,“primitive”, i.e. individuals, or individual companies, and individually owned headwear/software resources, 2) Management and operation of UMS should ne informed by the discipline of “chaos and complexity management in organizations”, e.g. Chaordic System Thinking (CST) model [02]

3) Specific instruments should be used, such as meta-organizations (e.g. Market of Resources model), brokering and virtuality, 4) These UMS “hyper”-sized manufacturing networks could be seen as manufacturing resources Internet of Things, 5) These UMS “hyper”-sized manufacturing networks could be seen as manufacturing production social networks, 6) These UMS “hyper”-sized manufacturing networks form and use clouds. CLOUD BASED PLATFORM Presentation of the ‘cloud’ is transcribed from (Schubert L., …) - as the reference source created within the EC initiative and therefore it is the most relevant for an advanced Manufacturing Systems and/or enterprise.

Figure 5: Figurative presentation of VE evolution: from conservative, minimal network domain (a), towards ubiquitous network domain (d)

“A ‘cloud’ is a platform or infrastructure that enables execution of code (services, applications etc.), in a managed and elastic fashion, whereas “managed” means that reliability according to pre-defined quality parameters is automatically ensured and “elastic” implies that the resources are put to use according to actual current requirements observing overarching requirement definitions – implicitly, elasticity includes both up- and downward scalability of resources and data, but also load-balancing of data throughput.”

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Cloud has a number of “particular characteristics that distinguish it from classical resource and service provisioning environments: (1) it is (more-or-less) infinitely scalable; (2) it provides one or more of an infrastructure for platforms, a platform for applications or applications (via services) themselves; (3) thus clouds can be used for every purpose from disaster recovery/business continuity through to a fully outsourced ICT service for an organisation; (4) clouds shift the costs for a business opportunity from CAPEX to OPEX which allows finer control of expenditure Journal of Applied Engineering Science 10(2012)3, 229


Dr Goran Putnik - Advanced manufacturing systems and enterprises: cloud and ubiquitous manufacturing and an architecture

and avoids costly asset acquisition and maintenance reducing the entry threshold barrier; (5) currently the major cloud providers had already invested in large scale infrastructure and now offer a cloud service to exploit it; (6) as a consequence the cloud offerings are heterogeneous and without agreed interfaces; (7) cloud providers essentially provide datacentres for outsourcing; (8) there are concerns over security if a business places its valuable knowledge, information and data on an external service; (9) there are concerns over availability and business continuity – with some recent examples of failures; (10) there are concerns over data shipping over anticipated broadband speeds.” Concerning the EU policy towards clouds, the document refers two main recommendations: Recommendation 1: The EC should stimulate research and technological development in the area of Cloud Computing Recommendation 2: The EC together with Member States should set up the right regulatory framework to facilitate the uptake of Cloud computing Concerning the types of clouds, for an advanced Manufacturing Systems and/or enterprise, the most important are the concepts of cloud types: (1) IaaS - Infrastructure as a Service, (2) PaaS - Platform as a Service, (3) SaaS - Software as a Service, and “collectively *aaS (Everything as a Service) all of which imply a service-oriented architecture.”

AN OVERALL SYSTEM ARCHITECTURE FOR ADVANCED MANUFACTURING Advanced manufacturing system architecture, Figure 6, is a ‘cloud’ based architecture that represents the manufacturing system as a service system, integrating the services for 1) Real-time Data Acquisition Services for realtime data acquisition from the equipment through the embedded intelligent information devices – services type/group ‘Equipment Intelligent Monitoring Systems’, 2) Product Design Services, that integrates four environments: 1) Computer Aided Design, 2) Product data repository with embedded Intelligent System for Decision Making (for accessing all relevant data, actual and historic as well as data analysis) from the equipment in use, 3) Mixed-reality Environment, and 4) Co-Creation (Collaborative) Environment for co-creative design – services type/group ‘Product Design Services’; 3) Equipment Operation Services, that integrates four environments: 1) Equipment Data Real-time with embedded Intelligent System for Decision Making, that provides all relevant data, actual and historic as well as data analysis and management suggestions, necessary for the productuion management

Figure 6: Overall System Architecture for development, implementation and validation Journal of Applied Engineering Science 10(2012)3, 229

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2) Management environment, for monitoring, scheduling and controlling management activities, with embedded Intelligent System for Decision Making, 3) Mixed-reality Environment, and 4) Co-Creation (Collaborative) Environment for co-creative management – services; 4) The ‘cloud’ infrastructure, that will provide the 1) infrastructure for the manufacturing system applications – of all three types of resources: material processing resources, information processing resources (i.e. computational resources), and knowledge resources – in the form of IaaS - Infrastructure as a Service; 2) platform for the manufacturing system applications in the form of PaaS - Platform as a Service, and 3) manufacturing system software ‘business’ applications in the form of SaaS - Software as a Service. ICT Platform Architecture The logical architecture of the ICT Platform is architecturefor integration of “Representation”, “Mixed-reality representation”, “Real-time management model”, and “Communication for collaborative management”.

It is basically a 3.tier layer architecture consisting of (1) Presentation Layer, (2) Business Layer and (3) Data Layer. The ‘Presentation Layer’ represents/defines applications and support for all interfaces, views, presentations and communications for users. The ‘Business Layer’ represents/defines applications and support for all ‘business’ applications such as Decision Making applications, Intelligent System applications, Services Workflows. The ‘Data Layer’ represents/defines applications and support for all applications for data repository and management, including knowledge bases (e.g. for Intelligent System on the upper level). For each layer the corresponding technology to be employed is referred. Co-Creation and Semiotics and Pragmatics platform Advanced manufacturing system architecture will integrate environments, or so-called, co-creative platforms, for three co-creative environments: 1) for product design processes, 2) for operation, or production, management processes, and 3) for integrated design-production processes.

Figure 7: Advanced manufacturing system co-creative platform, for three co-creative environments: 1) for product design processes, 2) for operation, or production, management processes, and 3) for integrated design-production processes

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Dr Goran Putnik - Advanced manufacturing systems and enterprises: cloud and ubiquitous manufacturing and an architecture

Figure 8: A vision of the multi-user video-conferencing system as the co-creative environment

It means that the co-creative processes both group of agents will perform independently, i.e. the designers will be capable to perform their processes in their own environment separately from the managers – ‘1st Co-Creative cycle’, and the managers will be be capable to perform their processes in their own environment separately from the designers– ‘2nd Co-Creative cycle’,. However, additionally, both groups will be capable to perform their processes jointly in a fully integrated and systemic way – ‘3rd Co-Creative cycle’, Figure 7.

Social sustainability: Advanced manufacturing system components will support Social sustainability goals enabling “The creation of new jobs” – This effect will be possible because the advanced manufacturing system is conceived as a service system meaning a great degree of “openness” for performing these services, the maintenance management and design services, by individuals (“free-lancers”), micro and small companies, that would form a dynamic network of services providers. In this way a potential for new jobs creation will be dramatically increased.

The supporting technique will be the multi-user video-conferencing with auxiliary functionalities. A vision is presented on the Figure 8. These three cycles, and the video-conferencing environment, will provide full semiotic/pragmatics effects and support in order to enhance to maximum the cognitive and creative capacities of the participants, and a full “co-creative”, or codesign or co-evolving, and truly systemic environment.

CONCLUSIONS

Sustainability The three aspects of sustainability: economic, environmental and social should be implemented in the following way: Figure 7 and Figure 8. Economic and environmental sustainability: Economic and environmental sustainability will be based on implementation of specific softwaremodules, with corresponded analytical models, for continuous evaluation of energy consumption and costs, environmental pollution and associated costs. These models and applications will be embedded in data acquisition services, see the System Architecture, Figure 15. Journal of Applied Engineering Science 10(2012)3, 229

The architecture presented is of a general nature andopen in various aspects, with structural elements, in nature and in number, that enables development of an advanced manufacturing system or enterprise on different complexity levels – which is on of the primary requirements for the capacity of achieving sustainability. Therefore, the architecture presented may have a number of implementation forms. It would be useful to remind that a number of underlying technologies should be considered, and which were not possible to analyze due to the paper’s limited space. E.g. embedded intelligent information devices, real-time management (and design), mixed reality and augmented reality, semiotics and pragmatics, co-creation, chaos and complexity management, the theory of sustainability, web 2.0 to web 4.0, and others. In short, many of technologies are already present. However, from the other hand, there is a number of open technical, organizational and conceptual problems that requires hard work in the future. Two of the virtually most important problems to work on are the interoperability, or integration, of the Ubiquitous and Cloud Manufacturing and their adoption in society (and industry of course).

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ACKNOWLEGMENTS The authors wish to acknowledge the support of: 1) The Foundation for Science and Technology – FCT, Project PTDC/EME-GIN/102143/2008, ‘Ubiquitous oriented embedded systems for globally distributed factories of manufacturing enterprises’, 2) EUREKA, Project E! 4177-ProFactory UES REFERENCES 1) Cheng, Y., Tao, F., Zhang, L., Zhang, X., Xi, G. H., & Zhao, D. (2010).Study on the utility model and utility equilibrium of resource service transaction in cloud manufacturing. Paper presented at the Industrial Engineering and Engineering Management (IEEM), 2010 2) Eijnatten F., Putnik G., Sluga A. (2007) Chaordic Systems Thinking for Novelty in Contemporary Manufacturing, CIRPAnnals, Vol 56, No 1, pp. 447-450 3) Elliott, L. (2010). The Business of ICT in Manufacturing in Africa: Afribiz 4) Foust, Brady J. (1975) Ubiquitous Manufacturing, Annals of the Association of American Geographers, Vol. 65, No. 1 (March 1975), pp. 13-17 5) Meier H., Roy R., Seliger G. (2010) Industrial Product-Service Systems—IPS2, CIRP Annals Manufacturing Technology, 59 (2010) 607–627 6) Mori Seiki CO., LTD, Service/Support von AZ mit der Sicherheit des Herstellers. Service Brochure published by Mori Seiki 7) Murakami, T., Fujinuma, A. (2000).Ubiquitous networking: Towards a new paradigm. Nomura Research Institute Papers, No. 2 8) Putnik G. et al. (2004) Cells for Ubiquitous Production Systems, Proposal for R&D Project, Project reference: POSC/EIA/60210/2004, submitted to Fundaçãopara a Ciência e a Tecnologia (FCT), Lisbon, Portugal 9) Putnik G. et al. (2006) Ubiquitous Production Systems and Enterprises - advanced enterprise networks for competitive global manufacturing, Proposal for R&D Project, Project reference: PTDC/EME-GIN/72035/2006, submitted to Fundaçãopara a Ciência e a Tecnologia (FCT), Lisbon, Portugal 10) Putnik G.D., Cardeira C., Leitão P., Restivo F., Santos J., Sluga A., Butala P. (2007) Towards Ubiquitous Production Systems and Enterprises, in Proceedings of IEEE Int. Symp. on Ind. Electronics - ISIE 2007, Vigo, Spain

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11) Putnik, G. D. (2010). Ubiquitous Manufacturing Systems vs. Ubiquitous Manufacturing Systems: Two Paradigms. In Proceedings of Proceedings of the CIRP ICME ’10 - 7th CIRP International Conference on Intelligent Computation in Manufacturing Engineering - Innovative and Cognitive Production Technology and Systems 12) Putnik, G. D., & Putnik, Z. (2010). A semiotic framework for manufacturing systems integration -Part I: Generative integration model. International Journal of Computer Integrated Manufacturing, 23: 8, 691 - 709 13) Schubert L. (2010) The future of cloud computing opportunities for European cloud computing beyond 2010, European Commission – Information Society and Media 14) Serrano V., Fischer T. (2007) Collaborative innovation in ubiquitous systems, J IntellManuf (2007) 18:599–615 15) Usmani, S., Azeem, N., &Samreen, A. (2011). Dynamic Service Composition in SOA and QoS Related Issues International Journal of Computer Technology and Applications, 2, 1315-1321 16) Weber A. (1928), Theory of the Location of Industries, translated by C. J. Friedrich (Chicago: University of Chicago Press, 1928), p. 51 (emphases by Foust, Brady J. (1975)) 17) Weiser,http://www.ubiq.com/hypertext/weiser/ UbiHome.html, Xerox PARC Sandbox Server. 18) Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing(28), 75-86 Paper sent to revision: 28.08.2012. Paper ready for publication: 27.09.2012.

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doi:10.5937/jaes10-2512

Paper number: 10(2012)3, 230, 135-142

COMBINING SYSTEM DYNAMICS AND DISCRETE EVENT SIMULATIONS - OVERVIEW OF HYBRID SIMULATION MODELS MSc Bojan Jovanovski * Ss. Cyril and Methodi University, Faculty of Mecahical Engineering, Skopje, Macedonia Dr Robert Minovski Ss. Cyril and Methodi University, Faculty of Mecahical Engineering, Skopje, Macedonia Dr Siegfried Voessner Institute of Engineering and Business Informatics, TU Graz, Austria Dr Gerald Lichtenegger Institute of Engineering and Business Informatics, TU Graz, Austria Simulation and modelling has been widely accepted as one of the most important aspects of the Industrial engineering. The application and use of simulation models has grown exponentially since the 1950’ until today. Over the years, the complexity of the simulated aspects has been adapted to the complexity of the analysed cases which has risen proportionally too. That is why techniques used many years ago, can often not give an adequate representation of the real world any more. For that reason, we propose to use hybrid simulation models, which are a combination of simulation paradigms in order to cope with this problem. In this paper, we will give an overview of selected researches and applications with an emphasis on Discrete Event Simulation and System Dynamics, as one of the core simulation based techniques in that area. Key words: Hybrids, Models, Simulation of models, System dynamics, Discrete-event simulation INTRODUCTION The advances in Industrial Engineering (IE) have gone a long way since the early beginnings and the experiments of Taylor, Gilbreth, Babbage, Towne and others. Not so much in the area of the field, but in the direction of tackling even the smallest details possible. In order to do this the complexity of the problems grew, with that the data needed to be obtained and processed was also getting bigger. The computers played huge factor in keeping the Industrial Engineering alive and constantly being in trend. Not only because of the hardware possibilities and the calculations that could have been made now, but also from the point of view that many software packages have been developed in order to solve some kind of an IE problem. There are solutions for finding an optimal layout, managing production processes, tackling ergonomic issues, calculating cost/profit etc. (the intention is not to name vendors here).

Simulation and modelling has been widely accepted as one of the most important aspects of the Industrial Engineering. The application and use of the simulation models has grown exponentially since the 50’ until today. This is mainly because of the advances in the computation field, but also because of the increased number (percentage) of acceptance by the academia and the industry (Robinson 2004a). The complexity of the simulated issues has been adapted to the complexity of the real world cases and has risen proportionally. Many of the tools and techniques used many years ago can not present the level of details that is needed today in some cases. One of the theses for future trends in the field of simulation by Robinson (2004) is that in order to deal with this, a combination of techniques would be required. Also, in (Banks et al. 2003) few of the experts asked for bigger accent to be put in interoperability of simulation software. In that direction, the best from the selected techniques would be taken and they would complement each other, resulting in the synergy factor.

*Ss. Cyril and Methodi University, Faculty of Mecahical Engineering, Skopje, Macedonia; bojan.jovanoski@mf.edu.mk Paper presented at the SIE 2012

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MSc Bojan Jovanovski - Combining system dynamics and discrete event simulations - overview of hybrid simulation models

In this paper, a comparison and combination of System Dynamics and Discrete Event Simulation (DES) will be presented. At the end one research example will be presented, showing why and when this should be done. SYSTEM DYNAMICS System Dynamics (SD) is a relatively new technique that has been populated in the last 20 years. The basic principle underlying system dynamics is that the structure of a system determines its behaviour over time (Forrester 1968; Sterman 2006). SD is all about the whole and looking at the system as a unit. In normal cases, a lot of people use the divide-and-conquer system in order to solve complex problems. The philosophy of SD is that every element is connected somehow with other element(s) and those relationships determine how the system performs over time. It is best used when modelling very complex systems that are very hard to perceive and understand. There are two main approaches that help define a SD model. The first one is the causal loops (and feedback loops), which are widely spread and very useful. Most of the time, they are the first step in developing a SD model, helping in the conceptualisation. The second tool is the stock and flow diagrams, which aid to describe the model using data. The easiest way to describe this is to think of models like system of water tanks with pipes and valves. In the research conducted by Helal et al. (2007) they have stated that “using SD at the operational level of the manufacturing system has failed to offer the needed granularity (Godding et al., 2003; Barton et al., 2001; Baines and Harisson, 1999; Bauer et al., 1982) [03, 07, 08]. The same was observed by Choi et al. (2006) who could not use SD to model the performance of the individual processes in a software development system”. In (Özgün & Barlas 2009) the authors needed to increase the values of some variables by tenfold in order for SD to “capture” them and for the model to make sense. In addition, while SD permits the study of the stability of the system over the long range, the trends of behaviour that it generates do not indicate what specific actions to be made and at what values of the action parameters. Such specifications require more detailed considerations that SD does not seem to work with, while DES has been effective at.

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DISCRETE EVENT SIMULATION DES is a more widely established simulation technique (Banks et al. 2004). “The system is modelled as a series of events, that is, instants in time when a state-change occurs”, (Robinson 2004). The models are stochastic and generally represent a queuing system. From the beginning until now, the models are based on a specific code that manages the simulation. At the beginning, DES was developed and used in the manufacturing sector. But, as the times have changed, so have the areas where DES has found its applicability (hospitals, public offices, document management etc.) Still, the main advantages and principles have never changed no matter if the simulated entities are products, people, documents etc. (Law 2006; Banks 1998). COMPARISON The SD and DES are very different approaches when trying to model a situation and there are distinctive communities that follow each, respectively. Little bit inspired by the title of Sherwood (2002), the following comparison will be made in order to clarify some things. If a task of analysing a forest is given to these two types of modellers, the SD modellers will try to look at the forest from above, or from far away. They will look at the landscape, see how the trees are spread and grouped, analyse the types of trees etc. Meanwhile, the DES modellers will try to go in the forest and search in it, look at every tree as an entity, the leaves of the trees, the structure of the trees etc. Having this in mind, it was not very difficult to accept SD a technique for the attempt to model strategic decisions and use DES for the operational processes and decisions. Based on the work of Chahal & Eldabi (2008c) and Lane (2000) a meta-comparison of both approaches is shown in Table 1. There are numerous articles that describe and compare these techniques, particularly. Maybe one of the first attempts was done by Ruiz-Usano et al. (1996) and before that Crespo-Márquez et al. (1993) concentrating on discrete vs. continues systems. All of them give some kind of proposition or direction what technique is most suitable in which cases. Most of them (Brailsford & Hilton 2001; Özgün & Barlas 2009; Sweetser 1999; Huang et al. 2004; Wakeland & Medina 2010) share the idea of the authors, presented Journal of Applied Engineering Science 10(2012)3, 230


MSc Bojan Jovanovski - Combining system dynamics and discrete event simulations - overview of hybrid simulation models

earlier that SD is more suitable when modelling a system and analysing it as whole and DES when more details are needed for the better representation. The researches have been mainly focused on developing two same models in the different approaches and analysing and sharing the results (Robinson & Morecroft 2006; Crespo-Márquez et al. 1993; Wakeland & Medina 2010; Johnson & Eberlein 2002). Tako & Robinson (2008) have gone a step further and have

analysed a model building process by five SD and five DES modellers on a same situation- a prison population problem. One of the detailed and structured comparison has been done by Chahal & Eldabi (2008), dividing the analysis in more than thirty categories and explaining every one of them. There are even researches that deal with the third possible option when simulating (e.g. a supply chain) - simulation with agents and compare that along the previous two (Owen et al. 2008).

Table 1: Meta-comparison of two approaches DES

SD Problem

Seeking to understand the imapct of randomness on the system

Aiming to understand the feedback within the system and its impact Scope

Operational

Strategy / Policy System

High level of detail that physically represents the system (detail complexity)

More macro level of detail that summarises the system (dynamic complexity)

Methodology Process view

Systems view Philosophy

Randomness

COMBINING TWO MODELLING TECHNIQUES There are couple of examples where the idea of hybrid models has been taken and proved useful, especially combining SD and DES. They will be analysed according the area/industry for which the model was created, how the models are connected, to which level this was applied in the organization, are the models dependent\independent and the format of the hybrid model. In the next section, we will share our insights regarding each of these issues and present you an example of a hybrid model being developed in mean time. Area/industry of application In the manufacturing industry, there is a good example for modelling hierarchical production systems (Venkateswaran et al. 2004; Venkateswaran & Son 2005). The authors are concentrated on the production and production related elements, and have developed a SD model for the longterm plans (developed by the “Enterprise-level decision maker”) and short-term plans (developed by the “Shop-level decision maker”). In the Journal of Applied Engineering Science 10(2012)3, 230

Feedback

paper (Rabelo et al. 2005) the authors have also examined a manufacturing enterprise, where they used SD to simulate a financial (reinvestment) policy and DES to simulate the production process of one machine. They have represented the number of machines in the SD model, so by “multiplying” this variable with the output of the DES process they can generate the production output of the enterprise. Based on the framework of (Helal et al. 2007), same has been tested and a hierarchical production model has been developed (Pastrana et al. 2010). In the recent decade, the healthcare management has been seen as a very interesting field for the industrial engineers (the Institute of Industrial Engineers <www.iienet.org> have classified Healthcare Management in the same importance as Lean & Six Sigma, Supply Chain Management, Ergonomics, Quality systems etc. and some universities have a special IE curriculum for Healthcare management, e.g. TU Eindhoven <www.tue.nl>). This interest has also been shown in using the simulation for tackling issues in the healthcare. Chahal and Eldabi (2008a) have distinguished three formats how

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the models inside a hybrid mode can communicate: Hierarchical, Process- Environment and Integrated format. Later they have suggested a framework for hybrid simulation in the healthcare (Chahal & Eldabi 2010). In the work of Brailsford et al. (2010) the authors used the hybrid models to represent two case. The first one is when the DES model simulates a process of a patient being examined with a whole configuration of a hospital, while the SD simulates the community and how a specific disease would spread. In the second case, the DES was used to simulate operations of a contact centre, and SD to simulate demographic changes of the population being examined. The use of hybrid modelling has found its applicability in the civil engineering as well (PeñaMora et al. 2008; SangHyun Lee et al. 2007; Alvanchi et al. 2009) dealing with problems that are more complex to be solved with independent simulation models or project management tools. One of the few advantages that the authors found with this approach is the benefit of proposals for improvement they got from the models. In the same direction as the previous two papers, Martin and Raffo (2001) have also suggested a hybrid approach in the software industry. They have worked on an issue that can be managed with project management software as well, but they argue that the benefit of the hybrid simulation is the experimentation that can be done. The use of agent-based modelling and SD as hybrid architecture can be also adapted for the automotive industry (Kieckhafer et al. 2009). Type of connection Combining the two different models in one hybrid one is one of the most important thing in this whole process. This defines also how the models will communicate, share data, behave at a certain time point etc. Back in the 1999 there were two papers that stress out the possibilities and the advantages when using HLA (High Level Architecture) to combine two or more models (Schulze 1999; Davis & Moeller 1999). Some research done so far has employed this tool in order to combine their models (Venkateswaran et al. 2004; Rabelo et al. 2003; Alvanchi et al. 2009). Clearly, the benefits are enormous, but also the effort, time and the technicality when using this approach. Some have used a more usual ways to do this, like Excel and Visual Basic for Applications (Brailsford et al. 2010). There are even cases where a specific research has been

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conducted in order to define a generic module in order for SD and DES models to communicate and function (Helal et al. 2007). There are even examples where the modellers have a single software solution (Anylogic, <www. xjtek.com>) and combined a DES model with differential equations (Marin et al. 2010). Maybe it is not as same as the rest of the cases, but is worth mentioning as an approach. Scope of the hybrid model In this section we would like to address at what scope is the hybrid model applied inside one area/organization; whether the hybrid model is about whole organization, two different functional areas inside organization, only one functional area etc. For example, the work of Brailsford et al. (2010) has two different cases, but both use DES to simulate inner situations (hospital and calling centre operations), while SD simulates very broad scenarios (whole community or population demographics). In the case of (Martin & Raffo 2001) the model is a representation of a project being under away. Rabelo et al. (2005) have modelled two different functional areas – SD for the decisions concerning allocation of the financial resources (of the plants) and DES for operational decisions of the plant (number of machines, people etc.). In the case of (Venkateswaran et al. 2004), the whole hybrid model is about the production in the enterprise; SD for the aggregate-planning level and DES for detailed-scheduling level. Dependent\independent models inside hybrid model The intention of the authors was to distinguish if the singular models inside the hybrid one are independent or dependent on each other. The idea was that maybe two different modellers can model their own model “independently” and then combine the model, which is thought of as very practical and less time consuming. This was very hard to distinguish during the research of the papers, because there is not so specific information regarding this issue. The authors have made experiments by themselves regarding this and have successfully paired two independent models.

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Type of hybrid model format Chahal and Eldabi (2008a) have distinguished three formats how the models inside a hybrid model can communicate: Hierarchical, Process - Environment and Integrated format. The works of (Venkateswaran et al. 2004; Rabelo et al. 2005; Rabelo et al. 2003; Pastrana et al. 2010) have a hierarchical model. (Brailsford et al. 2010) and (Martin & Raffo 2001) both deal with processes and how the environment deals with the changes that they bring. In (Brailsford et al. 2010) the authors argue that no one until now has achieved to develop a hybrid model by the Integrated format, but given the progress of the development of hybrid models, the gap is getting narrower. EXAMPLE / CASE For the research that is going on right now, we are in a process of developing a hybrid model, based on the case of one production enterprise.

This was not possible to be done in DES only environment, and when we experimented only with SD we did not get the needed detail level of the production. Because of the nature of the situation, we are developing two separate models. One SD model that will represent the top management decision about how many sales personal need to be (hired/fired) and one DES model about the process of production of the products been sold. The models are of hierarchical format according the classification of (Chahal & Eldabi 2008a) and aid each other so that the number of sales personnel is according the demand, but also according the production capacity (from the DES model). The connection was established using the built-in functions of the used software (Plant Simulation for DES and PowerSim for SD) and we used Excel as data storage media through the simulation runs. The functioning of the hybrid model is presented in Figure 1.

Figure 1: Structure of the hybrid model Journal of Applied Engineering Science 10(2012)3, 230

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The model works in that way that the SD model runs and triggers the DES model (the production) and sends the information regarding the demand. After the production cycle is finished, it sends back to the SD model the number of produced products. This information is received and taken in the SD model in order to calculate the possible sales that is one of the main inputs for determining the number of sales people (which was the initial goal of the simulation model). CONCLUCSIONS This paper summarizes and analyses different hybrid simulation models from selected papers. This is a relatively new area and only handful of research papers exist. Based on the papers and the authors view, the need for this kind of models is very justified and will be even more important in the near future. In order to get the most appropriate and convincing representation of the real world, the suitable modelling approach should be used. Because we try to simulate very complex scenarios, the need for hybrid simulation and modelling is inevitable. For our needs, the usage of System Dynamics and Discrete Event Simulation has been proven most suitable. ACKNOWLEDGEMENTS This research is supported by a MacedonianAustrian research project titled as “Joint simulation model for strategic decision support” funded by both Governments. REFERENCES 1) Alvanchi, A., Lee, S. & AbouRizk, S.M., 2009. Modeling Architecture for Hybrid System Dynamics and Discrete Event Simulation. ASCE Conference Proceedings, 339(41020), p.131. 2) Abu Gaben, M., Krčevinac, S., Vujošević, M.: Modelujući sistemi u optimizaciji, (2007) Journal of Applied Engineering Science (Istraživanja i projektovanja za privredu), no. 18, p. 37-47 3) Baines T, Harrison D. 1999. An opportunity for system dynamics in manufacturing system modeling. Production Planning and Control 10(6): 542-552 4) Banks, J. et al., 2004. Discrete-Event System Simulation (4th Edition), Prentice Hall. 5) Banks, J., Hugan, J. & Lendermann, P., 2003. The future of the simulation industry. In E. S. Chick, P. J. Sánchez, D. Ferrin, and D. J. Morrice, ed. Proceedings of the 2003 Winter Simulation Conference. pp. 2033-2043.

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6) Banks, J. ed., 1998. Handbook of simulation, Wiley Online Library. 7) Barton J, Love D, Taylor G. 2001. Evaluating design implementation strategies using simulation. International Journal of Production Economics 72: 285-299 8) Bauer C, Whitehouse G, Brooks G. 1982. Computer simulation of production system: Phase I. Technical Report COE No. 82-83-1. The University of Central Florida, Orlando, FL 9) Brailsford, S.C., Desai, S.M. & Viana, J., 2010. Towards the holy grail: combining system dynamics and discrete-event simulation in healthcare. In B. Johansson et al., eds. Proceedings of the 2010 Winter Simulation Conference. pp. 2293-2303. 10) Brailsford, S.C. & Hilton, N., 2001. A comparison of discrete event simulation and system dynamics for modelling health care systems. Food in Canada, pp.1-17. 11) Curović, D., Vasić, B., Popović, V., Curović, N.:Ekspertsko planiranje proizvodnje, (2008) Journal of Applied Engineering Science (Istraživanja i projektovanja u privredi), no. 20, p.49-57 12) Chahal, K. & Eldabi, T., 2010. A generic framework for hybrid simulation in healthcare. In Proceedings of the 28th International Conference of the System Dynamics Society. System Dynamics Society. 13) Chahal, K. & Eldabi, T., 2008a. Applicability of hybrid simulation to different modes of governance in UK healthcare. In S. J. Mason et al., eds. Proceedings of the 2008 Winter Simulation Conference. pp. 1469-1477. 14) Chahal, K. & Eldabi, T., 2008b. System Dynamics and Discrete Event Simulation: A Meta-Comparison. In the proccedings of UK Operational Reserach Society Simulation Workshop. pp. 189-197. 15) Chahal, K. & Eldabi, T., 2008c. Which is more appropriate: A multiperspective comparison between System Dynamics and Discrete Event Simulation. In Proceedings of the European and Mediterranean Conference on Information Systems. Al Bustan Rotana Hotel, Dubai 16) Choi K, Bae D, Kim T. 2006. An approach to a hybrid software process simulation using the DEVS formalism. Software Process: Improvement and Practice 11(4): 373-383 Journal of Applied Engineering Science 10(2012)3, 230


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17) Crespo-Márquez, A., Usano, R.R. & Aznar, R.D., 1993. Continuous and Discrete Simulation in a Production Planning System. A Comparative Study. In E. Zepeda & J. A. D. Machuca, eds. Proceedings of the 1993 International System Dynamics Conference. System Dynamics Society, p. 8p. 18) Davis, W. & Moeller, G.L., 1999. The High Level Architecture: is there a better way? In P. A. Farrington et al., eds. Proceedings of the 1999 Winter Simulation Conference. pp. 1595-1601. 19) Forrester, J.W., 1968. Principles of Systems, Pegasus Communications. 20) Godding G, Sarjoughian H, Kempf K. 2003. Semiconductor supply network simulation. The Winter Simulation Conference, Dec 710, New Orleans, LA 21) Helal, M. et al., 2007. A methodology for Integrating and Synchronizing the System Dynamics and Discrete Event Simulation Paradigms. Industrial Engineering. 22) Huang, P. et al., 2004. Utilizing simulation to evaluate business decisions in sense-andrespond systems. Simulation, (2000). 23) Johnson, S. & Eberlein, B., 2002. Alternative modeling approaches: a case study in the oil & gas industry. In 20th System Dynamics Conference, Palermo, Italy. 24) Kieckhafer, K. et al., 2009. Integrating agentbased simulation and system dynamics to support product strategy decisions in the automotive industry. Proceedings of the 2009 Winter Simulation Conference, pp.1433-1443. 25) Lane, D. C., 2000. You Just Don’t Understand Me: Modes of failure and success in the discourse between system dynamics and discrete event simulation.LSE OR Working Paper 00.34. 26) Law, A., 2006. Simulation Modeling and Analysis, Mcgraw Hill Higher Education. 27) Lee, SangHyun, Han, S. & Peña-Mora, F., 2007. Hybrid System Dynamics and Discrete Event Simulation for Construction Management. Computing in Civil Engineering 2007, (May 2011), p.29. 28) Marin, M. et al., 2010. Supply chain and hybrid modeling: the panama canal operations and its salinity diffusion. In B. Johansson et al., eds. Proceedings of the 2010 Winter Simulation Conference. pp. 2023-2033. Journal of Applied Engineering Science 10(2012)3, 230

29) Martin, R. & Raffo, D., 2001. Application of a hybrid process simulation model to a software development project. Journal of Systems and Software, 59, pp.237-246. 30) Meadows, D.H., 2008. Thinking in Systems: A Primer D. Wright, ed., Chelsea Green Publishing. 31) Owen, C., Love, D. & Albores, P., 2008. Selection of simulation tools for improving supply chain performance. Business, pp.199-207. 32) Pastrana, J. et al., 2010. Enterprise scheduling: Hybrid and hierarchical issues. In B. Johansson et al., eds. Proceedings of the 2010 Winter Simulation Conference. IEEE, pp. 3350–3362. 33) Peña-Mora, F. et al., 2008. Strategic-Operational Construction Management: Hybrid System Dynamics and Discrete Event Approach. Journal of Construction Engineering and Management, 134(9), p.701. 34) Rabelo, L. et al., 2003. A Hybrid Approach to Manufacturing Enterprise Simulation. Proceedings of the 2003 International Conference on Machine Learning and Cybernetics; wintersim, 2, pp.1125-1133. 35) Rabelo, L. et al., 2005. Enterprise simulation: a hybrid system approach. International Journal of Computer Integrated Manufacturing, 18(6), pp.498-508. 36) Robinson, S., 2004a. Discrete-event simulation: from the pioneers to the present, what next? Journal of the Operational Research Society, 56(6), pp.619-629. 37) Robinson, S., 2004b. Simulation: The Practice of Model Development and Use, John Wiley& Sons Ltd. 38) Robinson, S. & Morecroft, J., 2006. Comparing discrete-event simulation and system dynamics: modelling a fishery. In Proceedings of the Operational Research Society Simulation Workshop. pp. 137–148. 39) Ruiz-Usano, R. et al., 1996. System Dynamics and Discrete Simulation in a Constant Work-in-Process System: A Comparative Study. In G. P. Richardson & J. D. Sterman, eds. Proceedings of the 1996 International System Dynamics Conference. System Dynamics Society, pp. 457-460. 40) Schulze, T., 1999. On-line data processing in simulation models: new approaches and possibilities through HLA. In P. A. Farrington et al., eds. Proceedings of the 1999 Winter Simulation Conference. pp. 1602-1609.

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41) Spasojević, V., Klarin, M., Curović, D.: Dimenzije menadžmenta kvalitetom isporučioca u industrijskim preduzećima Srbije, (2009), Journal of Applied Engineering Science (Istraživanja i projektovanja u privredi), no. 23-24, p. 67-70 42) Sherwood, D., 2002. Seeing the Forest for the Trees: A Manager’s Guide to Applying Systems Thinking, Nicholas Brealey Publishing. 43) Sterman, J.D., 2006. Business Dynamics, McGraw-Hill. 44) Sweetser, A., 1999. A Comparison of System Dynamics ( SD ) and Discrete Event Simulation ( DES ). System, p.8. 45) Tako, A.A. & Robinson, S., 2008. Model building in System Dynamics and Discreteevent Simulation: a quantitative comparison. Analysis. 46) Venkateswaran, J. & Son, Y.J., 2005. Hybrid system dynamic—discrete event simulationbased architecture for hierarchical production planning. International Journal of Production Research, 43(20), pp.4397-4429.

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47) Venkateswaran, J., Son, Y.J. & Jones, A., 2004. Hierarchical production planning using a hybrid system dynamic-discrete event simulation architecture. Proceedings of the 2004 Winter Simulation Conference, pp.1094-1102. 48) Wakeland, W.W. & Medina, U.E., 2010. Comparing Discrete Simulation and System Dynamics: Modeling an Anti-insurgency Influence Operation. Proceedings of the 28th International Conference of the System Dynamics Society, (1991), pp.1-23. 49) Özgün, O. & Barlas, Y., 2009. Discrete vs. Continuous Simulation: When Does It Matter? Proceedings of the 27th International Conference of The System Dynamics Society, (06), pp.1-22 Paper sent to revision: 27.08.2012. Paper ready for publication: 26.09.2012.

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FUZZY SYSTEMS TO SUPPORT INDUSTRIAL ENGINEERING MANAGEMENT Dr Isabel L. Nunes * Universidade Nova de Lisboa, Faculty of Science and Technology, Portugal This paper presents the potentialities of Fuzzy Set Theory to deal with complex, incomplete and/or vague information which is characteristic of some industrial engineering problems. Two systems that were developed to support the activities of industrial engineering managers are presented as examples of the use of this mathematical methodology. Key words: Work related musculoskeletal disorders, Ergonomics, Resilience, Supply chain, disturbances, Industrial engineering, Fuzzy systems INTRODUCTION Many problems in Industrial Engineering are complex and have incomplete and/or vague information. Also the dynamics of the decision environment limit the specification of model objectives, constraints and the precise measurement of model parameters (Kahraman et al., 2006). Fuzzy Set Theory (FST) developed almost fifty years ago by L.A. Zadeh (Zadeh, 1965), is an excellent framework to help solve these problems. According to (Kahraman, 2006) Industrial Engineering is one of the branches where FST found a wide application area. (Kahraman et al., 2006) present an extensive literature review and survey of FST in Industrial Engineering. A review of the application of FST to human-centred systems can be found in (Nunes, 2010). This paper presents two application examples of fuzzy decision support systems aiming to support industrial engineering managers in two different areas of risk management: ergonomics and supply chain disturbances management. FUZZY SET THEORY FST provides the appropriate logical/mathematical framework to deal with and represent knowledge and data, which are complex, imprecise, vague, incomplete and subjective (Zadeh, 1965). It allows the elicitation and encoding of imprecise knowledge, providing a mean for mathematical modeling of complex phenomena where traditional mathematical models are not possible to apply. A fuzzy set (FS) is the generalization of classical (crisp) set. By contrast with classical sets which

present discrete borders, FS presents a boundary with a gradual contour. Formally, let U be the universe of discourse and u a generic element of U, a fuzzy subset A, defined in U, is a set of dual pairs:

where ÎźA(u) is designated as membership function or membership grade u in A. The membership function associates to each element u, of U, a real number ÎźA(u), in the interval [0,1], which represents the degree of truth that u belongs to A. Using FST it is possible to evaluate the degree of membership of some observed data, originating either from an objective source or a subjective source, to some high-level concept. Let us consider, for example, the evaluation of the delay disturbance based on the continuous membership function presented in Figure . A low degree of membership to the disturbance concept (i.e., values close to 0) means the delay is acceptable; while a high degree of membership (i.e., values close to 1) means the delay is unacceptable (Nunes & Cruz-Machado, 2012). The human-like thinking process, i.e., approximate reasoning is well modeled using Fuzzy Logic (FL), which is a multi-value logic concept based on FST (Zadeh, 1996). Thus FL permits to process incomplete data and provide approximate solutions to problems that cannot be solved by traditional methods. It allows handling the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when Linguistic Variables (LV) are used, these degrees may be managed by membership functions (Zadeh, 1975a;

* Universidade Nova de Lisboa, Faculty of Science and Technology, Portugal; imn@fct.unl.pt Paper presented at the SIE 2012

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1975b; 1975c). A LV is a variable that admits as values words or sentences of a natural language (Figure 2), their terms can be modified using linguistic hedges (modifiers) applied to primary terms.

Figure 1: Fuzzy set delay disturbance (Nunes & Cruz-Machado, 2012)

FST can be used in the development of, for instance, fuzzy expert systems or fuzzy decision support systems. The following cases are examples of these types of systems that can support industrial engineering managers’ activities.

FAST ERGO X (Figure 3) is a fuzzy expert system designed to identify, evaluate and control the risk factors existing in a work situation, due to lack of adequate ergonomics that can lead to the development of WMSD (Nunes, 2006; Nunes, 2009). Fast Ergo X evaluates the risk factors based on objective and subjective data and produce results regarding the degree of possibility of development of WMSD on the upper body joints and about the main contributing risk factors. The results (Conclusions) are presented both quantitatively (as membership degrees to inadequacy fuzzy set, defined in the interval [0, 1]) and qualitatively (as terms of a linguistic variable intensity). For instance “The possibility for development of a WMSD on the Right Wrist is extreme (0.92)”. The Conclusions can be explained (Explanations) by presenting the computed risk factors inadequacy degrees that contributed to the overall result, e.g. “The number of Repetitions performed by the Right Wrist is very high”. The system also presents Recommendations that users can adopt to eliminate or at least to reduce the risk factors present in the work situation. Some of the recommendations are in the form of good practices and graphical illustrations.

EXAMPLES OF FUZZY SYSTEMS FAST ERGO X Work-related musculoskeletal disorders (WMSD) are diseases related and/or aggravated by work that can affect the upper and the lower limbs as well as the neck and lower back areas. WMSD can be defined by impairments of bodily structures such as muscles, joints, tendons, ligaments, nerves, bones and the localized blood circulation system, caused or aggravated primarily by work itself or by the work environment (Nunes & Bush, 2012).

Figure 3: Activities performed on the analysis of a work situation by FAST ERGO X (Nunes, 2009)

A Fuzzy Decision Support System to manage supply chain disturbances

Figure 2: Linguistic variable inadequacy used to evaluate “protection inadequacy” (Nunes & Simões-Marques, 2012)

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Supply Chains (SC) are subject to disturbances that can result from acts or events that are originated inside of the SC (e.g., supplier failures, equipment breakdown, employees’ absenteeism) or may result from extrinsic events (e.g., social turmoil, terrorist attacks, or acts of God such as volcanic eruptions, hurricanes or earthquakes) Journal of Applied Engineering Science 10(2012)3, 231


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(Nunes & Cruz-Machado, 2012). The Supply Chain Disturbance Management Fuzzy Decision Support System (SCDM FDSS) developed by (Nunes et al., 2011) was designed to assess the SC and the organizations belonging to the SC based on their performance considering the following different scenarios, normal operation, when a disturbance occurs and when mitigation and/or contingency plans are implemented to counter the disturbance. The aim of the SCDM FDSS is to assist managers in their decision process related with the choice of the best operational policy (e.g., adoption of mitigation and/or contingency plans) to counter disturbance effects that can compromise SC performance. The system combines the use of FST to model the uncertainty associated with the disturbances and their effects on the SC with the use of discrete-event simulations using the ARENA software (a commercial simulation tool) to study the behavior of the SC subject to disturbances, and the effects resulting from the implementation of mitigation or contingency plans. The block diagram of the proposed SCDM FDSS is illustrated in Figure 4.

Figure 4: Relationship between SCDM FDSS and ARENA (adapted from (Nunes et al., 2011)).

The Inference Engine offers the reasoning capability of the system. It performs the FDSS analysis using a Fuzzy Multiple Attribute Decision Making model, and fuzzy data that characterizes the analyzed situation, using for instance fuzzified Key Performance Indicators (KPI). The inference process includes 7 steps (Nunes et al., 2011): 1. Computing the KPI for each scenario and SC entity for each simulation time period. The KPI are obtained at the end of each ARENA SC simulation; 2. Synthesizing the time discrete KPI into an Journal of Applied Engineering Science 10(2012)3, 231

equivalent KPI for the relevant period considered (obtained through a mean function); 3. Fuzzifying the equivalent KPI into a fuzzy KPI (FKPI). Fuzzy sets convert KPI in normalized FKPI, i.e., fuzzy values in the interval [0, 1], where a fuzzy value close to 0 means a bad performance and a fuzzy value close to 1 means a good performance; 4. Computing of a fuzzy performance Category Indicator (CI) for each scenario and SC entity using weighted aggregations of FKPI, through the following expression:

where: CIik – is the fuzzy performance Category Indicator for ith category of KPI and for kth SC entity; FKPIijk – is the jth Fuzzy Key Performance Indicator of the ith category of KPI and the kth SC entity; wijk – is the weight of jth Fuzzy Key Performance Indicator of the ith category of KPI and the kth SC entity. 5. Computing of a fuzzy Performance Index (PI) for each scenario and SC entity using a weighted aggregation of CI, using the following expression:

where: PIk – is the Performance Index of kth SC entity; CIik – is the fuzzy performance Category Indicator for ith category of KPI and for kth SC entity; wijk – is the weight of the ith category of KPI and the kth SC entity. 6. Computing of a fuzzy Supply Chain Performance Index (SCPI) for each scenario using a weighted aggregation of PI, using the following expression:

where: SCPI – is the Supply Chain Performance Index of the SC for the current scenario; PIk – is the Performance Index of kth SC entity for the current scenario; wk – is the weight of the kth SC entity. 7. Ranking alternatives. Scenario results for each entity and for the SC are ranked based on their PI and SCPI, respectively, in order to identify the operational policy with more merit.

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Using the results produced by the system (PI and SCPI) managers can: forecast the effects of disturbances in SC entities and on a SC as a whole; analyze the reduction of the negative impacts caused by the disturbance when operational policies are implemented; and selecting the operational policy that makes the SC more resilient. The best operational policy corresponds to the implementation that leads to the highest PI/SCPI value. The use of fuzzy modeling and simulation offers several benefits, inter alia, promotes a proactive SCDM, and improves the understanding of the impact of applying different operational policies meant to prevent or counter the effects of disturbances, allowing the selection of the ones that are more effective and efficient. CONCLUSIONS FST has been used since the sixties as a way to deal with complex, imprecise, uncertain and vague data in different areas of industrial engineering. In this paper the main characteristics and advantages of the use of FST were highlighted. Two examples of fuzzy systems applied to support decision-makers in the industrial engineering context were very briefly presented (one in the field of ergonomics and other in the field of supply chains’ management). The objective was to raise awareness to the industrial engineers present in this conference to the potential that FST offers as a modelling tool to address complex phenomena that many industrial problems present. REFERENCES 1) Kahraman, C. (2006). Preface. In: Fuzzy Applications in Industrial Engineering, C. Kahraman (ed). Springer, New York 2) Kahraman, C., Gülbay, M. & Kabak, Ö. (2006). Applications of Fuzzy Sets in Industrial Engineering: A Topical Classification In: Fuzzy Applications in Industrial Engineering, C. Kahraman (ed). pp. 1-55. Springer, New York 3) Nunes, I. L. (2006). ERGO_X - The Model of a Fuzzy Expert System for Workstation Ergonomic Analysis. In: International Encyclopedia of Ergonomics and Human Factors, W. Karwowski (ed). pp. 3114-3121. CRC Press, ISBN 041530430X.

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4) Nunes, I. L. (2009). FAST ERGO_X – a tool for ergonomic auditing and work-related musculoskeletal disorders prevention. WORK: A Journal of Prevention, Assessment, & Rehabilitation, 34(2): pp. 133-148. 5) Nunes, I. L. (2010). Handling Human-Centered Systems Uncertainty Using Fuzzy Logics – A Review. The Ergonomics Open Journal, 3: pp. 38-48. 6) Nunes, I. L. & Bush, P. M. (2012). Work-Related Musculoskeletal Disorders Assessment and Prevention. In: Ergonomics - A Systems Approach, I. L. Nunes (ed). pp. 1-30. InTech, 978-953-51-0601-2. 7) Nunes, I. L. & Cruz-Machado, V. (2012). A fuzzy expert system model to deal with supply chain disturbances. Int. J. Decision Sciences, Risk and Management, 4(1/2): pp. 127–151. 8) Nuens, I. L., Figueira, S. & Machado, V. C. (2011). Evaluation of a Supply Chain Performance Using a Fuzzy Decision Support System. Proceedings of The IEEE International Conference on Industrial Engineering and Engineering Management - IEEM2011Singapore, 6-9 Dec 9) Nunes, I. L. & Simões-Marques, M. (2012). Applications of Fuzzy Logic in Risk Assessment - The RA_X Case. In: Fuzzy Inference System – theory and applications, M. F. Azeem (ed). pp. 22-40. InTech, 978-953-510525-1. 10) Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3): pp. 338-353. 11) Zadeh, L. A. (1975a). The concept of a linguistic variable and its application to approximate reasoning-part I. Information Sciences, 8(3): pp. 199-249. 12) Zadeh, L. A. (1975b). The concept of a linguistic variable and its application to approximate reasoning-part II. Information Sciences, 8(4): pp. 301-357. 13) Zadeh, L. A. (1975c). The concept of a linguistic variable and its application to approximate reasoning-part III. Information Sciences, 9(1): pp. 43-80. 14) Zadeh, L. A. (1996). Fuzzy Logic = Computing with words. IEEE Transactions on Fuzzy Systems, 4(2): pp. 103-111. Paper sent to revision: 29.08.2012. Paper ready for publication: 26.09.2012. Journal of Applied Engineering Science 10(2012)3, 231


doi:10.5937/jaes10-2523

Paper number: 10(2012)3, 232, 147-152

A COMBINING GENETIC LEARNING ALGORITHM AND RISK MATRIX MODEL USING IN OPTIMAL PRODUCTION PROGRAM Dr Mirjana Misita University of Belgrade, Faculty of Mechanical Engineering, Belgrade, Serbia Dr Galal Senussia * Omar El-Mohktar University, Industrial Engineering Department, El-Baitha, Libya MSc Marija MilovanoviÄ&#x2021; University of Belgrade, Faculty of Mechanical Engineering, Belgrade, Serbia One of the important issues for any enterprises is the compromise optimal solution between inverse of multi objective functions. The prediction of the production cost and/or profit per unit of a product and deal with two obverse functions at same time can be extremely difficult, especially if there is a lot of conflict information about production parameters. But the most important is how much risk of this compromise solution. For this reason, the research intrduce and developed a strong and cabable model of genatic algorithim combinding with risk mamagement mtrix to increase the quality of decisions as it is based on quantitive indicators, not on qualititive evaluation. Research results show that integration of genetic algorithim and risk mamagement matrix model has strong significant in the decision making where it power and time to make the right decesion and improve the quality of the decision making as well. Key words: Multi-objective function, Genetic Algorithim, Risk Management, Optimum Production Program, Matrix, Costs INTRODUCTION The analysis of the production program of enterprises is an important and complex segment of managing the enterprise, considering the fact that it influences all elements or resources, such as planning of the material, human resources, machinery resources, research and development, marketing etc. All of these resources influence in multi-criteria optimization of production program. To reduce and improve the decesion making quality, it is important and necessary to evaluate them to minimize the risk of operating losses. In investigations carried out to date the production program optimization was based on multicriteria approach using linear functions [01, 09]. Using nonlinear functions in multi-objective optimization enables the application of genetic algorithms and is a step forward in the analysis of the product optimal quantities to maximize production resources utilization [06, 10, 07]. On the other hand, economic calculation of the product cost price is a complex procedure, so that the analysis of optimal production program most

commonly employed direct costs to determine the cost price and to define the cost function. However, cost functions based only on product variable costs cannot provide real optimal product quantities but are more suitable for ranking products that should be given priority in manufacturing. Introducing overhead costs in the function of cost price is a complex calculation procedure most often difficult to understand by the user in a concrete enterprise, considering that it is not easy to classify individual expenses. It is thought that in metalworking companies, roughly assessing, direct costs account for about 60% of total unit costs, while the share of overhead costs is 40% [03]. In business of enterprises, there are several categories of risk: risk of equipment failure (estimated in relation to human safety, to evironment, to business losses, ect.), risk management as a security measure, finacial risk assessment in cases of loan approval, quality management risk, ect. Generally, Enterprise Risk Management is relatively new concept, Fraser and Simskins [05] distinguish following risk categories: Shareholder

* Faculty of Mechanical Engineering, Kraljice Marije 16, 11000 Belgrade; mmisita@mas.bg.ac.rs Paper presented at the SIE 2012

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Dr Mirjana Misita - A combining genetic learning algorithm and risk matrix model using in optimal production program

value risk, Financial reporting risk, Governance risk, Customer and market risk, Operations risk, Innovation risk, Brand risk, Partnering risk, Communications risk. Risk management consisit of strategic risk, operational risk, financial risk and risk acceptance. Strategic risk deal with competition, market position and economic conditions. Operational risk

Concerned with the daily operations, precisely, to the consequences of daily decisions made in the company. The financial risks are related to relations with banks and stockholders, etc. The types of risk and process steps itroduced by Risk Management Committee 2003 [11].

Table 1: Enterprise Risk Management [8] ERM Framework Process steps

Type of risk Hazard

Financial

Operational Strategic

Establish Context Identify risk Analyze / quantify risks Integrate risk Assess / Prioritize Risks Treat / Exploit Risks Monitor & Review

Figure 1: Risk Impact/Probability Chart

The risk is defined as product of probability and consequence of certain events, which can be expressed in formula: R = P*Q P - Probability a particular event. Q â&#x20AC;&#x201C; Consequences of particular event. For any enterprises, there are external and internal of n-sources of risk. The total risk will represented by high-risk, medium-risk and low-risk sources of operating losses.

148

The based approach of applying risk are risk identification - what can affect the implementation of production program, risk analysis - defining the probability of occurrence of that, and risk assessment - determining the consequences, expressed in the form of operating losses.The most low-risk sources of operating losses refer to good quality decision. Figure 2 shows the map for identifying Business risks. Glover at all [9] states that the most real life optimization and scheduling problems are too complex to be solved completely and that the complexity of real life problems often exceeds the Journal of Applied Engineering Science 10(2012)3, 232


Dr Mirjana Misita - A combining genetic learning algorithm and risk matrix model using in optimal production program

ability of classic methods. Miettinen [08] considered that a key challenge in the real-life design is to simultaneously optimize different objectives through taking into account different criteria low cost, manufacturability, long life and good performance, which cannot be satisfied at the same time. Profit maximization is the main objective of business enterprises and as such the subject of numerous investigations. Profit is defined as the difference between the total revenue generated by selling products on the market and the overall costs, i.e.: P = TR – TC Where: P – Total profit TR – Total revenue TC – Total cost When analyzing the possibilities of profit maximization, it is important to consider the fluctuation of the TR and the TC. The TR depends on supply and market demands for particular types of goods, while the TC depends on different constraints faced by the company, such as the mechanical facilities, number and structure of employees, possibility of providing necessary specific materials for the manufacturing process implementation, delivery etc. For the company, to be competitive on the market means to produce a product at an appropriate price and quantity with the use of capital and labor in the appropriate volume and costs. Therefore, profit maximization refers to the optimization of variable parameters in the observed model, with given production constraints.

Where: P – Profit Q – Quantity of product Wpi – Selling price of the ith product Wvi – Variable cost of the ith product Tc – Constant cost

Figure 2: Graphic representation of profit maximization

In real enterprise’s operating conditions the functions of the TR and the TC are nonlinear and to determine them two different approaches must be applied. The TR function consists of the sum of variable and fixed costs, therefore, the sum of linear mathematical form by applying the Lagrange interpolation polynomial based on the values of variable costs from the previous period. It is possible to determine the nonlinear function of fixed costs in a Lagrange interpolation polynomial is, in our case, a function of production quantity P (Q) with ≤(n-1) level if we have n data points on the value of costs from the previous period.

Where:

In real life, the functions of dependence of production quantity and the TR and the TC are nonlinear. The maximum profit is the maximum difference between the total profit curve and the total cost curve, as represented in the Figure 3.

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METHODOLOGY Methodological steps in developing model for risk management integration methodology and GA is shown on Figure 3.

Figure 3: Steps in developing model for risk management integration methodology and GA

CASE STUDY In the company engaged in manufacturing precision measuring instruments, we have analyzed the available data and formed nonlinear functions of the TR and the TC for the three products: a) Clocks Revenue function:

150

Cost function: b) Water meter Revenue function: Cost function:

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Dr Mirjana Misita - A combining genetic learning algorithm and risk matrix model using in optimal production program

The functions of criteria for profit maximization will have the form:

c) Gas meter Revenue function: Cost function:

Table 2: Evaluation of risk sources and determination of trend Risk Source

Risk rating 1st Q. 2010

Risk 2nd Q. 2010

Risk 3rd Q. 2010

Operation cost.

Low

Medium

Medium

Labor cost

Low

Medium

Medium

Lubricant cost Raw martial cost

Low

Low

Low

Medium

High

High

Fixed cost

Medium

Medium

Medium

capital availability

Medium

Medium

Medium

business operations supply chain management

Medium

Medium

Medium

information technology

Medium

High

High

planning

Medium

Medium

High

reporting

Low

Medium

Medium

Respectively:

After getting the optimum solution, the second step is Identify and analysis of risk sources for the observed optimum product program. In our case, we have focused on the internal resources only. Identification, evaluations, and determination of trend are shown in the table 2.

Constraints: If we consider the production capacity as a key constraint in the production quantity of some products, temporarily ignoring the structure of demand for mentioned products on the market, the restrictions are:

***Employees and raw material in the observed company are not of limiting character. The Pareto front and values of the functions f1 and Figure 1 are shown in Figure 4. From the Pareto front diagram, it is evident that optimum solution for production quantity and profit maximization under given constraints is a set [2312; 219; 944], where the maximum profit is 5,950,340 RSD calculated as max (f1-f2).

Journal of Applied Engineering Science 10(2012)3, 232

Figure 4: The Pareto front of optimum solution

This figure 5 shows a two-dimension risk map. The vertical axis represents loss likelihood and the horizontal axis represents loss impact. The four quarter panels stand for different combinations of likelihood and impact.

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REFERENCES

Figure 5: A Two-Dimensional Risk Map

Risk matrix indicates a small number of highrisky, a small number of low-risk risk sources, but the largest number risk sources with medium probability and consequences for business losses, namely:

Over all research results indicate that at these restrict conditions of production, there is comparatively high risk of production losses. Therefore, it is necessary to resolve our problem to find another optimal solution and repeat analysis until achieved an optimal production program. CONCLUSIONS A strong and cabable model of genatic algorithim combinding with risk mamagement mtrix is intrduced and developed to get optimal production program and increase the quality of decisions. Applying genatic algorithm as a technique deals with huge conflect constrains to create one or altrenative optimal solusions. On ther hand, applying risk mamagement mtrix for choice of optimal production program reduces the risk of operating losses and affects the efficiency of management. Furthermore, qualitative aspects that are defined trough risk sources and by its identification and evaluation, more realistic production program evaluation can be taking into account. Integrated both of them, genetic algorithim and risk mamagement mtrix guide to optimal production program.

1) C. McNair: Defining and Shaping the Future of Cost Management, Journal of Cost Management, Vol. 14, No. 5, 2000, pp. 28-32, ISSN 1092-8057. 2) Curović, D., Vasić, B., Popović, V., Curović, N.: Ekspertsko planiranje proizvodnje, (2008) Journal of Applied Engineering Science (Istraživanja i projektovanja u privredi), no. 20, p.49-57 3) Eckart, Z., Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, November 1999. 4) Glover F., Kelly J.P., Laguna M., New Advances for Wedding Optimization and Simulation, Proceedings of the 1999 Winter Simulation Conference, 1999. 5) J. Fraser, B.J. Simskins: Enterprise risk management: Today’s Leading Research and Best Practices for Tomorrow’s Executives, John WIley & Sons, ISBN 978-0-470-49908-5, USA, 2010. 6) J. Sanchis, et al.:A new perspective on multiobjective optimization by enhanced normalized normal constraint method, Structural and Multidisciplinary Optimization, 2008, Vol. 36, No. 5, pp. 537–546, ISSN 1615-1488. 7) L. Chi-Ming, G.Mitsuo: An Effective DecisionBased Genetic Algorithm Approach to Multiobjective Portfolio Optimization Problem, Applied Mathematical Sciences, 2007, Vol. 1, No. 5, pp. 201 – 210, ISSN 0066-5452. 8) Miettinen, K., Nonlinear multi-objective optimization. Springer, 1999. 9) N. Fafandjel, A. Zamarin, M. Hadjina: Shipyard production cost structure optimization model related to product type, International Journal of Production Reasearch, 2010, Vol. 48, No. 5, pp. 1479-1491, ISSN 0020-7543. 10) S. Utyuzhnikov, P. Fantini, M. Guenov: A method for generating a well- distributed Pareto set in nonlinear multi-objective optimization, Journal of Computational and Applied Mathematics, 2009, Vol. 223, No. 2, pp. 820–841, ISSN 0377-0427. 11) The CAS Enterprise Risk Management Committee: Overview of Enterprise Risk, Management,Casualty Actuarial Society Forum, 2003, Pages 99-164, ISSN 1046-6487 Paper sent to revision: 29.08.2012. Paper ready for publication: 26.09.2012.

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doi:10.5937/jaes10-2514

Paper number: 10(2012)3, 233, 153-160

INVESTIGATIONS OF TIME AND ECONOMIC DIMENSIONS OF THE COMPLEX PRODUCT PRODUCTION CYCLE Jelena Jovanović * Technical College of Applied Studies, Čačak, Serbia Dr Dragan Milanović University of Belgrade, Faculty of Mechanical Engineering, Belgrade, Serbia Dr Milić Radović University of Belgrade, Faculty of Organizational Sciences, Belgrade, Serbia Radisav Đukić Office of the Manufacturing and Engineering Management, “Sloboda” Co. Čačak, Serbia The features of contemporary production process of top organization and management methods are grounded on the principles of economies of times and the principles of lean production, a new philosophy of production. Production should be organized according to the push-pull principle, with minimum inventories, manufacturing only what is really necessary, neither too early, nor too late. The paper presents the design procedure and results of investigations on the production cycle of a complex product included in the production program of “Sloboda” – Čačak Co. Key words: Complex product, Production cycle, Designing, Coefficient of running time, Current assets INTRODUCTION The achievement of Business and Production System (BPS) is largely dependent of adjusting production to the conditions of demand and application of innovative solutions in the sphere of technology, organization and management. To make the price competitive, the costs of business operations should be reduced, the observed losses should be eliminated or reduced to acceptable levels and resources should be engaged accordingly by using the corresponding management methods. Current assets should be engaged to the maximum in the production process, which is determined by the size of the production series, length of production cycle (PC) time, moment and manner of their engagement. The time and economic dimensions of PC should be mastered, so that the system responds promptly, in real time, no matter whether the orders are small-scale, large-scale, standard or special. Investigations of PCs implies a set of activities that have to define optimum production series, calculations for the amount of components required, cycle design, production preparation and launching, management of production activities, with current assets engagement and

the analysis and calculations of the coefficient of material running time. OPTIMUM PRODUCTION SERIES To manufacture only what can be sold, to consolidate all requirements in a single spot, to enable flexible and economic production in smaller-scale series; all this represents the first and foremost principle of contemporary production. So, a problem is posed of inventing the relations that will enable the calculations of optimal production series, with minimizing total business operating costs. This problem comes to the fore particularly in series production performed in ‘Sloboda’ Co. Having in mind that the behavior of costs in series production depends on the volume of production (linear, non-linear, independent), the size of production series should be calculated in such a way that the opposite orientation of the nature of costs is optimally harmonized. This means that the optimum series size (q0) is characterized by minimal costs per unit of product. Respecting mentioned constraints, the analytical expression for calculations is defined by the relation (1):

* Tenical college of Applied Studies, 32000 Čačak, Serbia; jelena.jovanovic@live.com Paper presented at the SIE 2012

(1)

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Jelena Jovanović - Investigations of time and economic dimensions of the complex product production cycle

where: Cn are total fixed costs required to accomplish the order (Q), c1 are variable costs per unit of product in unit of time (day), T is period of time required to accomplish the delivery, N is the number of optimum–launched series. On the grounds of data from the Company’s annual balance sheet, for the year 2011., corresponding technical documentation and relation (1), the optimum size of the production series was calculated, amounting to 3600 pieces.

CALCULATIONS OF THE QUANTITY OF COMPONENTS The plan of components is the most significant production operational plan. Its creation requires: calculations of optimum production series, drawing of products’ hierarchical structure graph (Figure 1), establishment of inventories in unfinished production (warehouses, work tasks), definition of planned technological waste and inventories at the end of the year for the continuity in the production.

Figure 1: Graph of products’ hierarchical structure

Planned quantities of components (qijk) can be calculated using the following formulas:

made for the quantity of components required for further analysis (Table 2). PRODUCTION CYCLE DESIGN

where: xijk is the component designation, qijk are planned quantities of components, Šijk is the planned waste, qijkM are quantities of components in a warehouse, qijkRN are quantities of components in launched work tasks, k is the coefficient that takes into account work task accomplishment level (per cent), mi is the quantity of the i-th component in a final article, ni is the quantity of the i-th component in the first superior level of a hierarchical scheme. For the optimum quantity of 3600 pieces of a complex product, using the corresponding formulas (2) – (5), calculations were

154

The technological (ideal) PC comprises the time required to perform all operations (ti) according to the technological procedure on all products of the optimum series (q0). The workpiece movement plays important role in calculating the technological cycle, where movement procedures can be consecutive (6), parallel (7) and combined (8, 9). Combined movements are most commonly encountered in a series production.

The complexity of a product imposes multi-level approach to the analysis and design of PCs, beJournal of Applied Engineering Science 10(2012)3, 233


Jelena JovanoviÄ&#x2021; - Investigations of time and economic dimensions of the complex product production cycle

cause production interferes with the assembly of units, sub-units and final article, so that parallel PC proceeding is possible by the stages of manufacturing and assembly. Using above presented considerations, calculations of the PC length for each operation will be made according to formula (10), in compliance with the adopted work organization, while PC for components will be calculated based on a combined workpiece movement applying the relations (11) and (12).

The designed PC length of a complex product can be determined using a network diagram, a gantogram (Figures 3 and 4) or calculations to define the longest path in a complex-productstructure graph (Figure 1) in compliance with relation (13): (13) where: T(pf)i is PC length of the i-th operation of the observed production stage by days, pf is the designation of the production stage (component), qpf is planned quantity pf, qSi is the capacity in a shift of the i-th operation, Sni is the number of work shifts during the day on the i-th operation, rmi is the number of workplaces where production of the i-th operation is organized, pni is norm accomplishment on the i-th operation, Tpf is the designed PC length pf, T(pf)1 is the designed PC length of the first operation of the observed pf, nopf is the number of operations pf (from the technological procedure), p is the designation of the operation that satisfies the condition: Tp > Tp-1, Tcp is the designed PC length, T (i-j) is the PC length of production stages found on the (i-j)-th path of a complex-product structure (i is the designation of the graph initial node, j is the designation of the graph terminal node), l is the total number of paths in a graph that connect the initial with the terminal nodes, Ď&#x201E; is average backup time between operations (compensation for all losses in PC).

Journal of Applied Engineering Science 10(2012)3, 233

PRODUCTION CYCLE ANALYSIS AND CALCULATIONS OF THE COEFFICIENT OF RUNNING TIME Unlike the technological (Tci) and designed PC (Tcopt) length, the actual (Tcs) length, apart from production (technological) time, includes PC non-production time and disruptions that cause losses Gc (Figure 2). In most cases PC disruptions are the result of inconsistency of production processes, bottlenecks in production, shortage of material, tools and energy, poor organization and handling of workplaces, stoppages due to machine breakdown, tool failure and lack of discipline in workers.

Figure 2: Production cycle duration

On the grounds of designed operation cycles (Tcp) and components involved in a complex product, production documentation was launched. The designed but also subsequently accomplished dates of the initiation and termination of production are recorded in a production date chart, a constituent part of work tasks. These data were used to determine the actual PC lengths (Tcs) (Tab. 1) and the coefficients of material running time (Kp) were calculated applying the relation (14).

The coefficient of running time indicates how much the actual PC length is longer than the designed one. Table 1 shows the designed and actual PC lengths of all production stages of the analyzed complex product, losses in the cycle and corresponding values of the running time coefficient. On the grounds of the PC designed, Tcp = 96 days, and actual, Tcs = 122 days, length, the running time coefficient of a complex product Kp = 1.27 was established.

155


Jelena JovanoviÄ&#x2021; - Investigations of time and economic dimensions of the complex product production cycle

Table 1: PC lengths (Tcp and Tcs), losses in the cycle and coefficient of material running time Tcp, Kp, Tcs

Designd PC lenght Tcp (days)

Actual PC lenghts Tcs (days)

Losses in the cycle (%)

Coeficient of running time Kp

2

3

4

5=(4-3)/4

6=4/3

E1

7

9

22

1.29

E2

14

19

26

1.36

E3

14

18

22

1.29

E4

12

17

29

1.42

E5

14

20

30

1.43

E6

40

51

22

1.28

E7

13

17

24

1.31

E8

14

19

26

1.36

P1

17

20

15

1.18

P2

16

20

20

1.25

SK1

12

14

14

1.17

SK2

24

30

20

1.25

A

19

21

10

1.11

Production stage 1

Elements

sub-assemblies assemblies final assembly

Note: Tcs value was established on the grounds of production monitoring and analysis of production and plan documentation

Figure 3: Gantt diagram â&#x20AC;&#x201C; The latest beginning

156

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Jelena JovanoviÄ&#x2021; - Investigations of time and economic dimensions of the complex product production cycle

Figure 4: Gantt diagram â&#x20AC;&#x201C; the earliest beginning

CURRENT ASSETS ENGAGEMENT The basic purpose of current assets is to finance the production process, i.e., to settle current obligations, to supply the material and to pay salaries. Unlike fixed assets partially spent in the production process, current assets are a part of business assets that are entirely spent in the production process and their overall value is transferred onto the product. Current assets can be engaged in the production process in a smaller- or larger-scale, depending on the production series size, time period, moment and manner of engagement. Business operating costs (Tp) can be calculated using the formula (15):

Current assets engaged prior to the beginning of production (point P, Fig. 5) amount to 17 093 264 dinars, relation (18):

Current assets engagement depending on the actual PC length will be calculated using the gantograms (Figures 3 and 4) and relation (19).

(15) Other costs (To) are divided into variable and constant, relation (16): (16)

Results are presented in Tab. 3, correlation coefficient and regression curves are defined by relations (20) and (21), and diagrams of current assets engagement are given in Figures 5 and 6.

Using previous formula, one can derive the formula for calculating the value of norm-hours for other variable costs (VNÄ&#x152;)ov: Journal of Applied Engineering Science 10(2012)3, 233

157


158

A

S1

S2

P1

P2

E1

E2

K1

E3

E4

K2

K3

E5

K4

E6

K5

E7

E8

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

2

1

1.

Mark

4

0.05

2

0.05

TOTAL:

6

1

1

3

3

3

28

4

4

7

1

1

8

3

3

2

0.2

0.2

1

4

2

0.16

1

1

1

2

2

0.04

1

1

1

4

mi

1

3

ni

Production stage

Ordianl number

76

1900

9042

0

0

0

2587

536

0

0

1553

1608

320

1850

536

3014

1553

2587

5

qi(3) (piece)

126

8116

15090

41953

16046

229

3091

2112

5004

6287

2571

6335

526

8023

2112

5030

2571

3091

6

8.5035

10.5

0.1125

0

0.061

0

5

0

0

0.051

0.1375

0

0.125

0.275

0.45

0.425

0.475

0.3375

0.375

7

ti qi(4) (hourly (piece) rates/ piece)

16920.41

1323

913.05

0

2559.133

0

1145

0

0

255.204

864.4625

0

791.875

144.65

3610.35

897.6

2389.25

867.7125

1159.125

8=6*7

qi(4)*ti (hourly rates)

4070

1967.4

117

0

42

0

175.4

0

0

25.5

6.6

0

94

15.75

200

180

276

60

320

9

Wmi (din/ piece)

1701

2100

22.5

0

12.2

0

1000

0

0

10.2

27.5

0

25

55

90

85

95

67.5

75

10

Wri (din/ piece)

111

136.5

1.4625

0

0.793

0

65

0

0

0.663

1.7875

0

1.625

3.575

5.85

5.525

6.175

4.3875

4.875

11=7*VNČ

Woi=ti*(VNČ)ov

2747378

149522.4

222300

0

0

0

0

0

0

0

0

0

151152

5040

370000

96480

831864

93180

827840

12=5*9

Tmi (din)

15068.625

14=6*11

Tovi (din)

0.000

10294.375

1880.450

46934.550

11668.800

31060.250

0.000

14885.000

0.000

0.000

3317.625

3384082

264600

182610

0

219965

17199.000

11869.650

0.000

511826.6 33268.729

0

229000

0

0

51040.8

172892.5 11238.013

0

158375

28930

722070

179520

477850

173542.5 11280.263

231825

13=6*10

Tri (din)

Table 2: Parameters for determining total and variable business operating costs

6351426

431321.40

416779.65

0.00

545095.33

0.00

243885.00

0.00

0.00

54358.45

184130.51

0.00

319821.38

35850.45

1139004.55

287668.80

1340774.25

278002.76

1074733.63

15=12+13+14

Tvi din) (din)

Jelena Jovanović - Investigations of time and economic dimensions of the complex product production cycle

Journal of Applied Engineering Science 10(2012)3, 233


Jelena JovanoviÄ&#x2021; - Investigations of time and economic dimensions of the complex product production cycle

Table 3: Dynamics and amount of current assets engagement in the latest and earliest beginning The latest beginning

Or. nu.

The latest beginning Day

Current assets (Os)

Cumulative Os

Current assets (Os)

Cumulative Os

1

2

3

4

5

6

1.

0

17093264.4

17093264.4

17621278.8

17621278.8

2.

15

160322.2

17253586.5

1102684.4

18723963.2

3.

31

171010.3

17424596.8

607533.2

19331496.4

4.

38

160176.8

17584773.6

234913.1

19566409.5

5.

49

251706.3

17836479.9

262787.5

19829196.9

6.

51

439421.3

18275901.2

417779.5

20246976.5

7.

60

757835.2

19033736.4

359253.7

20606230.1

8.

71

1787147.2

20820883.6

1254816.5

21861046.6

9.

82

538927.4

2135981.0

186600.4

22047647.1

10.

92

574364.0

21934175.0

169636.8

22217283.8

11.

101

1263621.9

23197796.9

980513.1

2319796.9

12.

111

117568.4

23315365.3

117568.4

23315365.3

13.

122

129325.2

23444690.5

129325.2

23444690.5

Figure 5: Diagram of current assets engagement (Os) as a function of time (Tcs), the latest beginning

Figure 6: Diagram of current assets engagement (Os) as a function of time (Tcs), the earliest beginning

Journal of Applied Engineering Science 10(2012)3, 233

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Jelena Jovanović - Investigations of time and economic dimensions of the complex product production cycle

CONCLUSIONS

REFERENCES

Respecting technical, technological, production and plan documentation and graph theory the paper describes the hierarchical structure of a complex product (Fig. 1). The oriented graph represents a basis for applying the algorithm that synthesizes the processes of optimization, planning, designing and analysis of PC of a complex product and components it is made up of. The systems for weaponry and military equipment production have a specific position and role in the economic environment of the Republic of Serbia. Threats to survival, uncertain trends of changes in the environment, a host of constraints, globalization of business operations and impact of diverse markets impose to ‘Sloboda’ – Cacak Co. two key dimensions of the strategy: forecasting and risking. Viewed within this context, the principle of economies of times in the manufacturing domain requires thorough investigation and mastering of time and economic dimensions of PCs. The coefficient of time of a complex product is at the satisfactory level (1.27) having in mind the designed and actual PC length (96 and 122 days). Taking into account the scale of uncompleted production, this coefficient value was expected to be lower. The diagrams of assets engagement for two diametrically opposed manners of production organization (Figs 3 and 4) are similar, which indicates a great value of inventories in unfinished production process (point P, Figure 5) amounting to 73%.

1) Curović, D., Vasić, B., Popović, V., Curović, N.: Ekspertsko planiranje proizvodnje, (2008) Journal of Applied Engineering Science (Istraživanja i projektovanja u privredi), no. 20, p.49-57 2) Djukic R., Milanovic D., Klarin M., Jovanovic J., Determinants of the dynamic managing of the BPS, Tehnika i praksa, No 1, VSTSS Cacak, Cacak, 2010. 3) Eckert C., Clarkson P., Planning development processes for complex products, Research in Engineering Design, Vol. 21(3), p153-171, 2010. 4) Jovanovic J., Milanovic D. D., Djukic R. et al., Analysis of the production cycle and the dynamics of the use of working capital, Tehnika i praksa, No 6, VSTSS Cacak, Cacak, 2011. 5) Uskoković, P.: Planiranje - jedna od osnovnih aktivnosti menadžmenta, (2009) Journal of Applied Engineering Science (Istraživanja i projektovanja za privredu), no. 8, p. 33-41

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doi:10.5937/jaes10-2513

Paper number: 10(2012)3, 234, 161-165

TOWARDS A DIGITAL FACTORY - RESEARCH IN THE WORLD AND OUR COUNTRY Dr Vidosav MajstoroviÄ&#x2021; * University of Belgrade, Faculty of Mechanical Engineering, Belgrade, Serbia This paper presents an analysis and synthesis of research carried out in the field of digital factory and digital manufacturing. The aim was to present different approaches and concepts, digital manufacturing and digital factory, for the purpose of establishing a common research approach. The engineering model of manufacturing based on digital models of products, processes and resources is the future of manufacturing engineering in this area, and are therefore subject to analysis in this study particularly important. At the end are particularly given to future research directions in the field of digital factory and of manufacturing. Key words: Digital factory, Digital manufacturing, Manufacturing, Modeling, Factories INTRODUCTION Todayâ&#x20AC;&#x2122;s business structure is more complex and dynamic than ever before. The market requires rapid changes in the industry with new products, which directly reflects on the work of the factory. On the other hand, digitization and information technology (IT) provide new, unimagined possibilities, engineers in the design and planning. These two approaches have led to two concepts that have since emerged: the digital factory and digital manufacturing. They allow to improve the engineering product development and create a new era in business and manufacturing, where the sustainability of one of the most important factors of business [18]. Targets set in the digital factory are: to improve the manufacturing technology, reduce the costs of planning, improving the quality of manufacturing / products, and increase adaptability to new demands of customers and markets [12]. In the area of production, the words digital factory, digital manufacturing, product modeling, etc., are now widely used. What do these concepts actually mean? The answer is not simple, because the meaning of these terms depends on the views of users, their perception, application, knowledge, and much more. This requires very careful use of these terms. There are some concepts and acronyms that are related to the digital factory and digital manufacturing, which are essential to highlight. This specifically includes the definition of the concept of virtual factories and virtual manufacturing [20], the same

types of problems encountered and the digital factory and digital manufacturing. Definitions of these concepts varies depending on the time of research and researchers who appointed them. The definition of virtual factory should be synonymous with the digital factory, a virtual manufacturing should be synonymous with digital manufacturing. Our research shows that we should not distinguish between the concept of virtual and digital factory / manufacturing in this area. According to [14] there are some common characteristics in the research areas of digital / virtual manufacturing, factories and enterprises. These are, for example: (a) an integrated approach to improve products, processes and technologies (integrated digital model), (b) the application of computer tools, such as modeling and simulation, planning and analysis of real technological processes, and (c) framework for the application of new technologies, including development of new methods and systems. BASICS OF THE DIGITAL FACTORY AND DIGITAL MANUFACTURING Basic digital factory No universally accepted definition for the digital factory, but can give some of them: (a) on the digital factory make animated visualization and simulation, which includes: advanced methods and processes in planning, integration of software tools and a competent staff, (b) digital factory a static model that includes geometric, technical and logistics data, given as an image object. Digital Factory contains digital informa

* Faculty of Mechanical Engineering, Kraljice Marija 16, 11000, Belgrade, Serbia; vidosav.majstorovic@sbb.rs Paper presented at the SIE 2012

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tion on the plant and its resources: location, media, logistics, simulation tools, and so on, [07] (c) is a generic digital factory digitized model of the factory, with its technological systems as key model from which others derive models as a mirror of the real manufacturing system. The digital factory design information (and present), evolving from the initial state of design, the final state, passing through various stages of reconfiguration. Information on manufacturing equipment and its features, tools, clamping accessories, material handling devices, etc., were also identified in the digital model. Therefore, we can say that digital information platform of factories manufacturing system in its lifetime, (d) digital factory generic term for a wide network of digital models, methods and tools, including simulation and 3D visualization [17]. If we now go from the foregoing definitions, one can derive common features for the digital factory / manufacturing, [05] as: interoperability, database / knowledge, information capture and digital plant architecture. Interoperability data, together with the portability, expandability and scalability is the most important features of information models . To achieve this, the models should be in a neutral format, which provides that the models and explicit information for them, or the system is independent. One way to achieve this is to use existing standards for information modeling [10]. Database / knowledge is used to generate different models of digital factories, which are associated IT tools for modeling and performing various processes in it. The most common approach is to develop joint / unified data base for the digital factory, which develops after defining the information architecture of the digital factory. The most common option is the development of these models in a neutral format, because the information model is the core of the digital factory. In developing the information model must also be taken into account the life cycle of information, their domain, resources and processes that affect them. But the truth is here to say that the single database is not the only solution, and the second solution is a distributed database, which reduces the problems that appear errors in it. But no matter which solution is used, it is necessary to have a good information architecture and IT tools for its support. Information capture and digital plant architecture - generally speaking, digital factory planning is not only digital, but should be a database for her life. Therefore the main issue, as the its

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structure and organization affects an enormous amount of information that is generated and used constantly. As noted above, the digital factory is mainly used for digital planning products, processes and resources for manufacturing, and therefore for each of the elements necessary information. But here it must be noted that all of this information need not be in the digital oblku. What does it depend. The answer is that it depends on what we mean by the definition of business system, factory, manufacturing system and its operation [01]. Only when these things have clearly defined, then we can define the scope of information for our definition of a digital factory. If we look at the digital factory as a technological system, it is a product of its materialization rather than design. This means that the digital product model should not be included in a digital model of factories. But on the other hand, on (digital model of the product) must be compatible with the digital model of factories, to make it possible simulation of manufacturing. As a result, digital factory should be configured in the resource and process information. The process is a set of one or more activities related to the work process or work flow processes, the manufacturing of products in the factory itself. This manufacturing process is necessary and appropriate support: tools, accessories, transportation, maintenance, etc., because the factory can not function without them. Models of support processes provide better knowledge of them and reduce the volume of uncertain knowledge in the factory. Resources in the digital factory include: human resources (employees and their skills), physical resources such as machinery and equipment (all operating data on them) and information resources (management and administration of the factory). Processes and resources are defined in such a way to organize an information model, but thatâ&#x20AC;&#x2122;s not enough, when the plant operates on the basis of models of manufacturing activities. Because of this process and resource model should be presented so that their information domain can be modeled as an activity. BASIC DIGITAL MANUFACTURING Today there are real industrial plants, based on the concept of digital factory. Also, these studies deal with the load by research institutions, so the concept of digital manufacturing will be considered from two angles. From the perspective of industrial applications [15], manufacturing Journal of Applied Engineering Science 10(2012)3, 234


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of digital computer includes support for the planning, engineering and 3D computer visualization. On the other hand in [19], the digital output is defined as a methodology that uses depth IT knowledge and technology. Profound knowledge in this model is used in digital form. CIRP dictionary, defines manufacturing as follows:’’ the whole of interrelated economic, technological and organizational measures, directly related to the processing of materials, ie. all functions and activities that directly contribute to the creation of goods. It includes all activities and operations relating to the product and its maintenance after manufacturing, and everything in between’’ [20]. In this case, the digital output of the digital factory. This definition is used by all researchers, members of the CIRP’s. For example, the manufacturing model of the web-based multi agent system is defined as a digital manufacturing [07]. This concept promotes collaboration between product development and manufacturing, but different plants, using a digital model of the product. Another example [17] proposes a model of STEP-NC manufacture, using a digital concept, which includes: (a) vane-standardized data exchange and use, (b) web communication and decision making, and (c) integration of the entire chain of manufacturing process. From the above analysis we can conclude that the volume of digital manufacturing can vary, depending on the definition that we use. Generally speaking, becomes the three most important elements that determine what is a digital manufacturing: IT system and its application, the theoretical concept of digital manufacturing - the scope of profound knowledge is used as a digital manufacturing methodologies, and using specific techniques and methods, such as for example web-based multi agent systems and the like [14, 17]. When we talk about the basic characteristics necessary information in digital manufacturing, we can say is: its digital format, multiple use and its independence of distance, time and place of use. Another aspect of digital manufacturing of its framework and the principles it uses. If we start from the principle, first defining the model, indicating that the two approaches for this purpose may be used. The first is - common denominator for simplification and abstraction of something that may not be realistic [15]. The second is - If we look at an object B, which is building a model, and we ask him about the object A, and from him (facility B) to get an answer on the object A [13]. Journal of Applied Engineering Science 10(2012)3, 234

If this definition digital transfer of manufacturing, it is a virtual stock manufacturing, and these actions are performed on models of manufacturing systems and factories. Thus, the digital output should be a mirror of actual manufacturing with a few limited detail. Digital manufacturing for example uses a digital information product, you can verify the digital manufacturing through various aspects of the planning process. That’s why we say that product information is extremely important in the context of various activities carried out in the factory and they’d never could be performed without them. Each product should have a digital model that can be used to simulate the benefits of digital manufacturing at the factory or for verification of different planning scenarios. All this means that the essential compatibility between digital product models and factory. The purpose of the digital output is: (a) verification through simulation facilities for manufacturing process planning, tool path and sensors for the inspection, (b) verification and performance analysis of digital manufacturing with the simulation of flow, geometry or performance machine tools. Forward the facts clearly define the scope of digital manufacturing, related to all manufacturing activities from beginning to end development of a product, where the IT system only tool to support digital manufacturing [06, 02].Digital manufacturing is performed and the analysis and simulation of digital factories, creating its model, using some or all models of the product, so that digital manufacturing includes resources and processes of the factory. This means that digital manufacturing is a way to verify the manufacturing of appropriate options for the type of product. The analysis shows that today still perform specific research in the field of digital manufacturing / factory, with no uniform definitions for these areas. For these reasons, all studies in this area of work, you need to start from the definition of digital manufacturing / factory which is used in this study. OUR RESEARCH ACTIVITIES IN DIGITAL MANUFACTURING Serbian as National Technology Platforms related to the Manufuture ETP was created in individual Member States and adopt the main development goals identified in both Manufuture – a vision for 2020 and the current document [11, 08, 09]. This initiatives can also encourage the emergence at regional levels of equivalent concepts promoting competitiveness by stimulation

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between sciences, education and industry in Serbia. Our national Manufuture initiatives, while adopting different models of organisation, should share the common Manufuture vision and aim to promote widening acceptance of, and participation in, Manufuture by Serbian industry, by [11, 08, 09]: (a) alerting public opinion and politicians to the challenges that Serbian manufacturing faces, as well as to industry’s critical role in delivering economic output, skilled employment and sustainable growth, (b) aligning the interests of the R&D community and technology providers in strong and effective cooperation networks that develop and source knowledge and technology, and (c) identifying and strengthening the highly competitive local networks of large companies, SME suppliers, technological partners, consultants and R&D contractors. The most important contributions of these Serbian initiatives should be in: (i) build a clear link to and incorporate a wide SME participation, as especially smaller SMEs can harder participate on European levels of platforms than international large companies, (ii) horizontal integration, coordination and synchronisation of R&D efforts in Serbia, (iii) vertical application of competitive technologies, products, methods and processes in enterprises (both OEMs and SMEs) – including multidisciplinary networks coordinating R&D activities in new industrial sectors such as medical technologies, telematics, nanotechnologies and mechatronics in EU and Serbia. Manufuture will promote successful Europe-wide implementation of solutions at various levels facilitating the structuring of effort and funding, and encouraging pan-European convergence between regional centres of industrial competitiveness [11, 08, 09]. Over the next decade, the integration of Serbia in EU will have a significant influence on European manufacturing of products for global markets. In a strategy of integration and cohesion, they could become world-class suppliers to OEMs [11, 08, 09]. This can be seen as an EU/Serbia strategy of transition, to maintain strong national/ regional sectors in the interim period, opening a competition between EU members in all areas, even in R&D as a key factor to promote excellence and fostering the European manufacturing progresses connected to the high-added-value industrial paradigm [10, 11, 08, 09]. Serbian as national initiatives will be particularly important in the new MS, such as Serbia. After many years of

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socialist regulation, their move towards market economy – in R&D, as in other spheres – is a major mental, organisational, technical and financial challenge. CONCLUSIONS Starting from the facts stated in the text, according to some directions for research in the field of digital factories, such as: (a) establishing a single definition, scope and structure of the digital factory, (b) decomposition of the information structure of digital factories and the use of ISO 10303 standards, (c) explore suitable IT architecture that will be used for the development, transfer and use different models of digital products, processes and resources, and (d) development of an ontological concept for linking models and their structure in a digital factory. Our research is now related to the last aspect of the systemic approach to the development of digital manufacturing and digital factory [10, 11, 08, 09]. Note: This article is part of the research carried out within the Project TR 35 022, supported by the Ministry of Education and Science. REFERENCES 1) Bley, H., Franke, C., Integration of Product Design and Assembly Planning in Digital Factory, Annals of the CIRP, 53/1:25-30, 2004. 2) Brogren, C., Implementation of a Sustainable European Manufacturing Industry, Proceedings of Manufuture Conference, Nancy, 2009. 3) CIRP, Dictionary of Production Engineering Vol.3, Manufacturing Systems 1st Edition, ISBN –540-20555-1 4) Jovane, F., Global experiences: sustainable manufacturing, Politecnico di Milano, 2010, Milano. 5) Kjellberg, T., Katz, Z., Larsson, M., The Digital Factory supporting Changeability of Manufacturing Systems, Proceedings of CIRP ISMS, pp. 102-106, 2005. 6) Lee, J., E-manufacturing - fundamental, tools, and transformation, IJ Robotics and Computer Integrated Manufacturing 19 (2008) 501–507. 7) Mahesh, M., Ong, S.K., Nee, A.Y.C., Fuh, J.Y.H., Zhang, Y.F., Towards a generic distributed and collaborative digital manufacturing, Robotics and Computer-Integrated Manufacturing, 23:267–275, 2007. Journal of Applied Engineering Science 10(2012)3, 234


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8) Majstorovic, V., Center of Excellence for Manufacturing Engineering and Management (CEMEM) , Facts – Objectives – Goals - Researches Framework, Mechanical Engineering Faculty, Belgrade, 2008. 9) Majstorovic, V., Manufuture Serbia – Strategic Research Agenda 2008-2015, Mechanical Engineering Faculty, Belgrade, 2008. 10) Majstorovic, V., Šibalija, T., EU / Serbia Manufuture Excellence, Introduction paper, Proceedings of Manufuture Conference, pp. 28/34, Tampere, 2007. 11) Majstorovic, V., Sibalija, T., ManuFuture & Factories of the Future - Contribution from ManuFuture Cluster Serbia, Second Serbian’s Manufuture Conference, Belgrade, 2011. 12) Mattucci, M., Factories of the Future, COMAU, EFFRA, Milano, 2010. 13) Minsky, M. L., Matter, minds and models, Proc. International Federation of Information Processing Congress, 1:45-49, 1965. 14) Nylund, H., Salminen, K., Andersson, P., Digital Virtual Holons – An Approach to Digital Manufacturing Systems, Proceedings of CIRP Conference on Manufacturing Systems, pp. 64-68, 2008. 15) Petrović, P., Milačić, V.: Nacionalne tehnološke platforme Srbije - novi formalni okvir za reinženjering industrije Srbije, (2010) Journal of Applied Engineering Science (Istraživanja i projektovanja za privredu), no. 29, p. 147-161

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16) Rogstrand, V., Nielsen, J., Kjellberg, T., Integrated Information as an Enabler for Change Impact Evaluation in Manufacturing Lifecycle Management, Proceedings of CIRP Conference on Manufacturing Systems, pp. 162-166, 2008. 17) Wenzel, S., Jessen, U., Bernhard, J., Classifications and conventions structure the handling of models within the Digital Factory, Computers in Industry, 56:334-346, 2005. 18) Westkämper, E., Manufuture and Sustainable Manufacturing, Proceedings of CIRP Conference on Manufacturing Systems, pp. 20-28, 2008. 19) Westkämper, E., Strategic Development of Factories under the Influence of Emergent Technologies, Annals of the CIRP, 56/1:419422, 2007. 20) Yang, W., Xu, X., Modelling machine tool data in support of STEP-NC based manufacturing, International Journal of Computer Integrated Manufacturing, 21/7:745–763, 2008. 21) Zülch, G., Stowasser, S., The Digital Factory: An instrument of the present and future, Computer in industry, 56:323-324, 2005. Paper sent to revision: 31.08.2012. Paper ready for publication: 27.09.2012.

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TIME TO CHOOSE BETWEEN SCIENTIFIC AND ADMINISTRATIVE APPROACH TO RELIABILITY Dr Jezdimir Knežević * MIRCE Akademy, Woodbury Park, Exeter, United Kingdom “If you watch a glacier from a distance, and see the big rocks fallings into the sea, and the way the ice moves, and so forth, it is not really essential to remember that it is made out of little hexagonal ice crystals. Yet if understood well enough the motion of the glacier is in fact a consequence of the character of the hexagonal ice crystals. But it takes quite a while to understand all the behaviour of the glacier (in fact nobody knows enough about ice yet, no matter how much they’ve studied crystal). However, the hope is that if we do understand the ice crystal we shall ultimately understand the glacier.” R. Feynman, “The Character of Physical Law” INTRODUCTION Reliability Theory, since it’s beginning in 1950’s, has been based on mathematical theorem rather then on scientific theories. Massive attempts where made to further applications of the existing mathematical and statistical methods and analysis without attempts for understanding “failure mechanics”. Then, in 1980s, practicing reliability engineers and analysts, who have neither ability to understand the mathematics, turned to what they have had, which is enormous practical experience of the observed failure modes of existing systems. Thus, a large number of “practical reliability methods” have been developed and used, all of which were based on the Failure Mode, Effect and Criticality Analysis, FMECA, but still without any attempt to understand and address physical mechanisms that generate failures. Consequently, during the last 50 years the Reliability Theory made very little progress, a part from a few exceptions (one should put some references here), in the direction of becoming the science, in terms of making accurate predictions that could be confirmed with practical observations. The reason is very simple; neither statistics, which does not study causes of statistical behaviour, nor engineers whose “applied methods” were focused on meeting contractual and legal requirements, by doing FMECA to “prove” Mean Time Between Failures, MTBF, were able to provide a fertile ground for the development of reliability.

To illustrate the above statement the fundamental expression for reliability will be used. It is generally accepted that reliability is the probability that a system will operate without failure during a stated period of time, which is mathematically represented by the following expression: (1) where: TTF is a random variable known as the Time To Failure and R(t) is the reliability function. However, today there are two distinguished approaches to calculation of the probability defined by the above equation. They are: Approach 1, where calculation of the probability of a successful operation with internal of time from 0 to t is based on the following expression: (2) where nfm is a total number of competing failure mechanisms that can generate a failure event. It is necessary to stress that a probability distributions that define individual failure mechanisms are exclusively determined by the physical processes that generate them, like fracture, single event upset, electrostatic discharge, fatigue, creep, wear, radiation, hot electron, embrittlement, depolymerisation, charge trapping in oxides, glass transition and many others. Approach 2, well established within western defence aerospace, oil and other industries, for all reliability predictions, risk and safety as

* MIRCE Akademy, Woodbury park, Exeter, United Kingdom; jk@mirceakademy.com

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sessments, conformances, contracting and similar activities, where the probability of operation without failure during a given interval of time t is defined by the following expression: (3) where: is the failure rate of each failure mechanisms that can generate a failure even. Both expressions for reliability function clearly demonstrate that the system reliability follows the laws of probability. However, the expression 2 allows the probability laws to be driven by physical processes and mechanisms that take place in the system or result from the interaction of a system with natural and human environment, whereas the expression 3 has one, and only one, predetermined future, irrespective of physical properties of systems, their operational conditions, maintenance policies and support strategies. In fact the second approach completely ignores existence of corrosion, fatigue, creep and many others, scientifically observed and well understood mechanisms, which have time-dependent failure mechanisms. To make the distinction between these two approaches to reliability the former will be called the scientific approach and the latter the administrative approach. Consequently, the main objective of this paper is to argue that the scientific approach to reliability is the only way forward for all members of the reliability community who wish to make accurate predictions that will be confirmed during the operational processes of the future systems. Only then, accurate and meaningful reliability predictions become possible, which is imperative for the development of Risk-Based Technology and its successful applications. SCIENTIFIC APPROACH TO RELIABILITY Mathematically, reliability is defined as a probability that a system will maintain a required function during a stated period of time (see equations 1, 2 and 3). However, as a probability cannot be seen or measured directly, engineers and managers, have fundamental difficulty in understanding and interpreting statistical and probability functions associated with their systems. This is because physical characteristics of a system like the weight, temperature, volume and similar

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have a clear and measurable meaning. However, the concepts of probability, and hence reliability, is an abstract property of a system that obtains a physical meaning only when behaviour of a large sample of systems is considered. Hence, understanding of reliability is reduced to the scientific observation and analysis of system failures, which are observable and measurable physical phenomena. According to the Mirce Mechanics, system failures are events that cause transition of a system from positive to negative functionability state [1] due to some of the following reasons, or combinations of them: a) Built-in design errors (incorrect selection of materials, stresses shapes, etc) b) Production problems (human errors, material and process deficiencies) c) Irreversible changes in the condition of components with time due to wear, fatigue, creep, corrosion, and similar degradation processes d) Imposition of external overstress mechanisms resulting from collisions, harsh landings, extreme weather conditions, etc e) Human errors in execution of maintenance tasks f) Human errors in execution of in-service support tasks At the MIRCE Akademy a large number of failure events and associated phenomena have been observed and analysed to understand the physical mechanisms that generate occurrences of failures. Consequently, systematic studies are applied to understand phenomena that cause thermal aging, thermal buckling, photo-chemical degradation, reduction in dielectric strength, evaporation, metal fatigue, actinic degradation, photo-oxidation, swelling/ shrinking, degradation of optical qualities, fogging, photochemical decomposition of paint, blistering, warping, thermal stress, breakdown of lubrication film, increased structural loads, shift in the centre of gravity, jammed control surfaces, attenuation of energy, clutter echoes, blocking of air intakes, decreased lift and increased drag, unequal loading, removal of coating protection, pitting, roughening of the surface, acid reactions, leakage currents, promotion of mould growth, reduction of heat transfer, caking and drying, premature cracking, hot spots creation, erosion, bleaching preservatives, Journal of Applied Engineering Science 10(2012)3, 235


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abrasive wear, corrosion, alkaline reactions and similar. For years, research studies, international conferences, summer schools and other events have been organised in order to understand just a physical scale at which failure phenomena should be studied and understood. In order to understand the motion of failure events it is necessary to understand the physical mechanisms that cause the motion. That represented a real challenge, as the answers to the question “what are physical and chemical processes that lead to the occurrence of failure events” have to be provided. Without accurate answers to those questions the prediction of their future occurrences is not possible, and without ability to predict the future, the use of the word science becomes inappropriate. After a numerous discussions, studies and trials, it has been concluded that any serious studies in this direction, from Mirce Mechanics point of view, have to be based between the following two boundaries: • the “bottom end” of the physical world, which is at the level of the atoms and molecules that exists in the region of 10-10 of a metre [03], • the “top end” of the physical world, which is at the level of the solar system that stretches in the physical scale around 10+10 of a metre. [04] This range is the minimum sufficient “physical scale” which enables scientific understanding of relationships between system operational processes and system operational events. In other words, this is the physical range within which, the system operational processes mentioned above (fatigue, the wind direction change, suncups formation on the blue ice runway, bird strike, perished rubber, carburettor icing) take place and as such they could be understood and predicted. THE BOTTOM END: ATOMIC SYSTEM All matter in the Universe is made of elementary building blocks called atoms. Complex interactions between atoms govern existence of larger building blocks. [2] For example two or more atoms form molecules, ranging from simple oxygen molecules to large polymers and other macromolecules. Besides this way of building the matter, atoms can arrange in periodic structures called crystals. Examples of crystals are numerous, from the rock salt (crystal of Na and Cl), over diamond (made of C atoms) and crystal of Journal of Applied Engineering Science 10(2012)3, 235

Iron to recently synthesized crystals in the field of Nanotechnology, to mention just nanotubes and graphene – the miracle materials with large promise for the future applications. While the average size of atoms is 10-10 m crystals can grow to macroscopic dimensions of the order of a meter, making objects like airplane wings, car bodies etc. The very atomistic nature of these objects governs their mechanical, electronic, thermal and other physical properties, which are of interest for Mirce Mechanics. Additionally material defects, fatigue and other features, which can in the final instance, lead to the failure of material and finally a cancellation of flight or even a disaster, are originated at the atomic level. Quantum mechanics, a physical theory developed in 1920s, in exact way describes the matter at the atomic scale. This theory has the power to predict the evolution of material under stress, corrosion or other environmental influences, which complements Mirce Mechanics, giving meaningful values to the missing parameters of the theory. THE TOP END: SOLAR SYSTEM The Solar System may seem enormous, looking from the human perspective, but it is only a very small corner of the Universe. However, the entire solar system contains only eight planets that move in elliptic paths around the Sun. All of them are lit by the Sun and do not produce their own light. The distance between the Earth and the Sun is 150 million kilometres; hence the number for the top end of 1010. Thanks to its thermonuclear reactions which last for 5 billion years, the Sun irradiates enormous energy each second in the form of electromagnetic and other radiations, out of which only ~1/109 fraction reach the Earth. Owing to them rivers flow, winds blow, forest rustle and the human race flourish.) About a half of that energy (0.8x1017 watts) reaches the terrestrial surface, which is 5x1014 square metres, making the average power of the solar radiation at ground level is 160 watts/m2. The 99.9 % of it is absorbed by the soil, and goes into the evaporation of water, causing winds, thunderstorms, and all that we loosely call weather. Thus, only 0.1 per cent of the radiant energy of the Sun (around 1014 watts) is captured by plants through photosynthesis of organic substances from carbon dioxide and water. This energy supports all the living things on Earth, from bacteria to animals and human.

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From system reliability point of view, the solar system is significant in the respect to the “making” of the weather, which is the day-to-day condition of the atmosphere. It is one of the main drivers of system reliability, as it is “responsible” for the: • temperature and pressure of the air, • wind speeds and directions, • moisture in the air, precipitated as rain, snow, hail, sleet, dew or frost. All air contains moisture in the form of water vapour, which is water in gaseous form. As warm air can hold more water vapour than cold air, when it is cooled its capacity to hold water vapour decreases, and finally the air is completely saturated, having a relative humidity of 100 per cent, known as dew point. Further cooling beyond dew point leads to water vapour condensing around nuclei, such as specks of dust or salt, to form water droplets or, in cold air, minute ice crystals. Large quantities of condensed water vapour form clouds, by which water is continually conveyed from the oceans to the land, where it is released from the air as precipitation. This provides the land with the fresh water needed by animal and plant life. Finally, the water completes the cycle by returning to the oceans. AN EXAMPLE: IMPACT OF COSMIC RAYS ON AVIONICS RELIABILITY In order to illustrate the necessity for the physical scale of studies of reliability phenomena proposed in this paper to be from 10-10 to 10+10 of a metre, the impact of cosmic rays on reliability of avionics will be presented here. It has been concern for avionics, since the late 1980’s when the primary radiation phenomenon, which had previously been observed in orbiting satellites only, also began to appear in aircraft electronic systems (Put some references here). The interaction of this radiation with avionics can result in occurrence of Single Event Effect, SEE, which can be manifested as a transient ‘soft error’ effect such as a bit flip in memory or a voltage transient in logic. Alternatively, a ‘hard error’ can be induced resulting in permanent damage such as the burn out of a transistor. Due to the rapid advances in electronics technology and the unrelenting demand for increased avionics functionality in the competitive commercial aircraft industry, the complexity of avionics systems has risen exponentially. If device memory cells used

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for flight safety or mission critical functions are affected the concern is that the loss of key system functionality due to corrupted data could cause a flight safety or mission critical failure. Baumann in [3] stated that: “Left unchallenged, SEEs have the potential for inducing the highest failure rate of all other reliability mechanisms combined”. Advanced microprocessor and memory semiconductor devices used in modern avionics exhibit an increased susceptibility to SEEs caused by ionising radiation from the following two main sources: • Cosmic rays from space (10+10 of a metre and beyond) that are individual energetic particles that originate from a variety of energetic sources ranging from our Sun to supernovas and other phenomena in distant galaxies all the way out to the edge of the visible universe. Although the term cosmic ray is commonly used, this term is misleading because no cohesive ray actually exists. The majority of cosmic rays consist of the nuclei of atoms (atoms stripped of their outer electrons) ranging from the lightest to the heaviest chemical elements. In terms of composition about 90% of the nuclei are hydrogen, therefore just single protons, 9% are helium, alpha particles with the remaining 1% a mix of heavier element nuclei, high energy electrons, positrons and other subatomic particles. Within the atmosphere the three most important parameters used to define the variability of the particle flux at a specific location are: altitude, latitude and energy. Within the field of cosmic ray physics altitude is expressed in terms of atmospheric depth, which is the mass thickness per unit of area in the Earth’s atmosphere. Cosmic rays can be broadly divided into two main categories, primary cosmic rays and secondary cosmic rays. Primary cosmic rays are particles accelerated at astrophysical sources and generally do not penetrate the Earth’s atmosphere. Secondary cosmic rays are created when primary cosmic rays collide with oxygen and nitrogen nuclei in the atmosphere and break into lighter nuclei in a process known as cosmic ray spallation. • Alpha particles from radioactive impurities in the materials of which device are made (10-10 of a metre and below). They are doubly ionised helium atom consisting of two Journal of Applied Engineering Science 10(2012)3, 235


Dr Jezdimir Knežević - Time to choose between scientific and administrative approach to reliabillity

neutrons and two protons that can also be described as a helium atom that has been stripped of its electrons. When an alpha particle travels through a material it will lose kinetic energy primarily through interactions with the materials electrons, leaving a trail of atoms with “kicked out” orbital valence electrons. This process is called ionisation and it can be described as the physical mechanism that converts an atom or molecule, into a positively or negatively charged state by either adding or removing charged particles. The resulting atom is then referred to as an ion, or more specifically a cation if positively charged or an anion if negatively charged. The issue of alpha particle generating source

contaminates first arose in the late 1970s when Intel discovered high soft error rates in new DRAMs when the integration density increased from 16K to 64K. The problem was traced to a semi-conductor packaging plant that had just been built downstream from an abandoned uranium mine. The ceramic packages were being contaminated by radioactive contaminants in the water. Low energy alpha particles are emitted from the decay of trace radioactive materials in semi-conductor device and packing materials. The relationship between the radiation particles and the failure mechanisms of the single events upsets is shown in the Table below [04]:

Table 1: Summary of Failure Mechanisms Radiation Type

Radiation Source

Method of Charge Deposition

Failure Mechanism

Thermal neutrons

Secondary cosmic ray neutrons

Indirect Ionisation

Interaction between thermal neutrons and materials containing the Boron-10 isotope creates secondary ionising particles.

Low energy alpha particles

Radioactive decay of uranium and thorium impurities located within the device materials.

Direct Ionisation

4 to 9 MeV alpha particle, creating an electron hole funnel.

High energy neutrons ( 10 MeV - 1 GeV )

Secondary cosmic ray neutrons

Indirect Ionisation

High energy neutron collisions with silicon nuclei.

As the reliance on avionics systems within aircraft increases so do concerns regarding the reliability of these systems, particularly for those systems, which are considered safety critical. Hence, to take the appropriate mitigating actions and enable decisions to be made at the design stage a method need to be devised that will facilitate the calculation of soft errors rates due not only to quiescent conditions, but also to take into account more exceptional solar influenced events. The research currently undertaken within the MIRCE Akademy has two main objectives: • the development of an SEE functionability prediction model • the use of the model to investigate the influence of space weather, flight route and a multitude of other aircraft and system design factors on the resultant shape of the distribution of SEE initiated failure events throught time. Journal of Applied Engineering Science 10(2012)3, 235

The main areas of research are: the investigation on the influence of the aircraft structure on the internal neutron flux spectra at specific inside locations of the architectures of future commercial aircraft and the evaluation of the methods and techniques used by the electronics industry today to assess their suitability for the inclusion into the SEE functionability prediction model. A plethora of device and circuit level simulation methods exist together with a range of empirical techniques exist that could be used at various indentures levels. The integration of these methods into an SEE functionability model may lead to an improved understanding of SEE fault propagation mechanisms resulting in a more accurate prediction of failure events at system level. The final goal is the creation of an innovative SEE functionability prediction model that

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will enable the future behaviour of an avionics system to be predicted for a whole host of different external parameters such as the extremes of space weather or different flight routes. Furthermore the model should allow system designers the flexibility to examine the full range of system design options such as device selection, system configuration and SEE reduction solutions to allow early functionability improving design decisions to be made, with least investment in time and resources.

As science is the proved model of reality that is confirmed through observation, the summary recommendation of this paper to reliability professionals is to move from the universe in which the laws of science are suspended to the universe that is based on the laws of science in order for their predictions to become future realities. It is encouraging to know that Rolls Royce reliability department in Darby, England, routinely recognises over 50 different failure mechanisms in reliability modelling of their jet engines.

CONCLUSION AND RECOMMENDATION

ACKNOWLEDGMENT

The main objective of this paper was to present the authors approach to Reliability, one that is based on the laws of science. I do not believe in the existence of parallel universes where the laws are either ignored or bent to accommodate administrative or contractual requirements. A prime example of the later is the well accepted model of system reliability that requires the acceptance of “alternative universes” to support the argument that the components and consequently systems possess a constant, time independent, failure rate, as described by the equation 2. This approach stems from neither science nor observation, but from imaginary steps envisaged in the minds of its proponents who allowed all laws of science to be suspended. However, this view is in direct opposition to the observed functionability phenomena like corrosion, fatigue, creep, wear, quality problems and many other time dependent physical processes that clearly demonstrated that the components/system reliability for a stated period of time could have increasing, constant and decreasing probability of success in respect to the stage of the life of a system, consisting components and maintenance policies applied, as the science based approach caters for through the reliability function defined by the equation 1.

I wish with this paper to pass my very best wishes to Dr Jovan Todorovic, retired Professor of Mechanical Engineering from Belgrade University for his 80th birthday. Not that he introduced the subject of Reliability to the University of Belgrade in mid 1970s, but he has done it by applying the scientific way, from the beginning, and enthusiastically shared his knowledge with all of those who were able to embrace it. I was one, of many, benefactors of Professor Todorovic’s pioneering work in reliability theory.

Finally, it is essential to distinguish the scientific approach to the formulation and modelling of the motion of reliability through the life of a system, contained in Mirce Mechanics and presented in this paper, from administrative approach that is based on reliability models of systems that are created to demonstrate the contractual compliance of the legally binding acquisition processes, in western defence and aerospace industries.

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REFERENCES 1) Bader, R.F.W., Atoms in Molecules: a Quantum Theory, Oxford University Press, Oxford, UK, 1990. 2) Baumann, R Radiation-induced soft errors in advanced semiconductor technologies, IEEE Transactions on Device and Materials Reliability, Vol 5, No 3, pp. 305–316, Sept. 2005. 3) Knezevic J., Reliability, Maintainability and Supportability – A Probabilistic Approach, with Probchar Software. pp 292, McGraw Hill, UK, 1993. 4) Todorović, J.: Upravljanje održavanjem na bazi rizika, (2009) Journal of Applied Engineering Science (Istraživanja i projektovanja za privredu, no. 1, p. 23-33 5) Zaczyk, I, Analysis of the Influence of Atmospheric Radiation Induced Single Event Effects on Avionics Failures, Master Dissertation, pp 77, MIRCE Akademy, UK, 2009. Paper sent to revision: 28.08.2012. Paper ready for publication: 26.09.2012.

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Appendix 1: Mirce Mechanics Concept Mirce Mechanics - scientific study of the motion of functionability through the life of a human made and managed system to: • Experimentally determine the pattern of the motion, • Scientifically understand mechanisms of the motion, • Mathematically defined laws of the motion • Predict the pattern of the motion of a given system. Functionability, the ability of being functional, is the fundamental property of in-service performance of any system. It is an emergence property of a system, in time domain, resulting from the complex interactions of natural phenomena, such as fatigue, corrosion, creep, wear, humidity, wind, hail, foreign object damage, solar radiation and similar, on one hand and from human actions taken in respect to the type, content and timing of operational, maintenance and support processes, on the other. To achieve the above objectives Mirce Mechanics concept, principles and methods have evolved from the experimental, theoretical, computational and applied aspects of research, each of which is briefly described below. Experimental Mirce Mechanics focuses on the determination of the pattern of the motion of functionability through the life of a system resulting from the occurrence of functionability events. Existing experimental and observed data clearly demonstrate that the motion of functionability through life of a large number of “identical” systems deliver a large number of different functionability patterns, while delivering “identical” functionality. Consequently, it is statistical experiment that requires the use of statistical methods to calculate the average pattern and associated measures. However, as statistics does not study the causes of statistical behaviour it is the task of Mirce Mechanics to scientifically understand the mechanisms that cause the motion of functionability in time. Thus, functionability phenomena that cause occurrence of positive and negative functionability events are subjected to the analyses within physical scale between 10-10 metre (for the understanding atomic and molecular phenomena) and 10+10 metre (for the understanding of cosmic and environmental phenomena). Journal of Applied Engineering Science 10(2012)3, 235

Theoretical Mirce Mechanics focuses on the mathematical definition of the patterns of the motion of functionability through the life of a system. Mathematically formulated law of the motion, in respect through time, which accurately represents the observed patterns, is defined by the expression, named Mirce Functionability Equation, which has been developed by Dr J. Knezevic at the MIRCE Akademy. It defines, in the probabilistic terms, the expected patterns of functionability trajectory and associated measures for a given system, operational rules and conditions. Although the laws of probability are just as rigorous as other mathematical laws they are not able to predict the motion of functionability through the life of each individual system, they can only predict the probability of each individual system being in a given functionability state at a given instant of time. Computational Mirce Mechanics focuses on the quantitative evaluation of Mirce Functionability Equation for a given system and given in-service rules and conditions, as the analytical solutions to these equations are too complex to be solved mathematically. Consequently, it is the task of Mirce Mechanics to develop effective computational methods that will enable construction of models that accurately represent the observed reality of system behaviour, rather then to simplify system reality to cope with mathematical limitations. The Monte Carlo method has proved very successful in Quantum Mechanics for finding practical solutions to multi-dimensional integral equations that are of similar nature to those of the Mirce Mechanics.

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EVENTS REVIEW

THE 5TH INTERNATIONAL SYMPOSIUM OF INDUSTRIAL ENGINEERING â&#x20AC;&#x201C; SIE 2012 The 5th International Symposium of Industrial Engineering â&#x20AC;&#x201C; SIE 2012, held in June, 2012 in Belgrade, Serbia, was aimed at providing a unique platform to meet frontier researchers, scientists, as well as practitioners and share cutting-edge developments in the field. The SIE 2012, fifth in the series of SIE meetings, was organized by Industrial Engineering Department, Faculty Of Mechanical Engineering, University Of Belgrade, Serbia and Steinbeis Advanced Risk Technologies Stuttgart, Germany.

The Symposium also fostered networking, collaboration and joint effort among the conference participants to advance the theory and practice as well as to identify major trends in Industrial Engineering today. Proceedings with over 70 papers and disscusions have contributed to better comprehension the role and importance of Industrial Engineering in this country, both in domain of scientific work and everyday practice. Despite the widely varying backgrounds and interests of the participants, the schedule of the meeting kept them all fully engaged by providing a platform where ideas at the cutting edge of industrial engineering could be exchanged with great enthusiasm. We think that the synergies and international collaborations resulted through sharing of knowledge and cross-fertilization of ideas at the earlier SIE meetings led to greater maturity at the SIE 2012. This special issue of the Journal of Applied Engineering Science contains a selection of papers on all aspects of interdisciplinary themes treated in the SIE 2012. The most relevant contributions are selected after a standard review process by disciplinary experts. Special thanks are due to the staff of the Journal of Applied Engineering Science and all the references for their careful work.

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ANNOUNCEMENT OF EVENTS IIPP QUALITY MANAGEMENT SCHOOL Considering business conditions of European market, quality has a significant role, not only in providing new markets, but also in maintaining the existing ones. Nowadays, customers do not only expect a quality product, but they require a proof that the company is capable to produce high quality products and provide quality services. Obtaining of this evidence should be the first goal for each company that has high aspirations when it comes to new markets but also standard’s procedure in order to maintain its reputation. Implementation is not complete if employees are not familiar with standards. With the aim to closer inform the employees of the meaning and significance of ISO standards, Institute for research and design in commerce & industry – IIPP organize training “School of Quality”. During the training participants will: • expend their knowledge about implementation of ISO standards, • learn how to maintain and improve quality level of companies • learne how to verify and improve business performance of companies Training will be held during four days in two locations. First lectures will be held at the Faculty of Mechanical Engineering in Belgrade, while the final lecture and the test will take place in attractive location in Serbia - Zlatibor. Programme • Fundamentals of quality concepts, definitions, approaches • Standards, review and interpretation • Management Responsibility • System and process approach • Data management, information system • Statistical methods (engineering methods, quality management methods) • RISK, FMEA, FTA • Supply and storage, evaluation of supplier • Maintenance • Evaluation, audit, certification • Examples, practice, Deming management experiment • PAS 99 - Integrated Management Systems Result After implemented training, Qiipp consultant is able to assume responsibility for independent work in the following fields of activity: • Implementation of quality standards • Maintaining a high level of quality • Constant improvement of the quality system • Assessment and audits of own companies and their suppliers Candidates who passe the test will get a diploma “Qiipp consultant for implementation, maintenance, analysis, evaluation and testing, design and improvement of the quality system”. Time and location: 20.10.2012. - Belgrade, Faculty of Mechanical Engineering 22- 24.10.2012. - Zlatibor, hotel Dunav, phone: +38131 841-126, contact person: Brankica Penezić Institute for research and design in commerce & industry Phone: 011/6300750; Fax: 011/6300751; E-mail: office@iipp.rs; web: www.iipp.rs Journal of Applied Engineering Science 10(2012)3

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ANNOUNCEMENT OF EVENTS IIPP MAINTENANCE MANAGEMENT SCHOOL Maintenance Management School presents practical experience in combination with adopted theoretical knowledge, thus creating maintenance management experts capable to perform and coordinate the maintenance of complex technical systems. Use unique opportunity to expand knowledge in the field of technical systems maintenance. During fourdays training focus will give to the following topics: • Maintenance Objectives and Policies • Corporate/Company Environment • Maintenance Concepts • Work Planning • Maintenance Terminology • Team Working and Communications • Laws and Regulations • Information Technology • Condition Monitoring • Quality Assurance (Systems) • Fault Finding Techniques • Environment and Occupational Health and Safety • Spare Part Management The school program merges best local knowledge and experience modernized and harmonized with the recommendations of European Federation of National Maintenance Societies. Since Maintenance Management School connected and unified local tradition and experience in the maintenance process with the European norms and requirements, it’s result is thus twofold - to all who signed up gives a chance to gain national certificate ’’Expert for maintenance management” and to those who can and want more, Maintenance management school opens the possibility of obtaining the International certificate “European maintenance manager”. Result: More than 240 national certificates and 16 internationally recognized certificates: European Maintenance Manager. Time and location: 20.10.2012. - Belgrade, Faculty of Mechanical Engineering 22 - 24.10.2012. - Zlatibor, Hotel Dunav, Phone:+38131 841-126, contact person: Brankica Penezić

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BOOK RECOMMENDATION Recommended by doc. dr Vladimir Popović TEORIJSKE OSNOVE AUTOMATIZOVANOG PROJEKTOVANJA MOTORNIH VOZILA Author: prof. dr Miroslav Demić Monograph entitled “Theoretical basis for the automated design of motor vehicles” is the result of the author’s many years of work in the field of motor vehicles, particularly in the area of dynamics, ride comfort and optimal design of systems for motor vehicles. It presents the fundamental procedures of automated and optimum design of mechanical systems, such as motor vehicles and their systems. As modeling present a basis for automated and optimal design, this issue has found an important place in this book. The content of the monograph is more general than the one defined by the title, as it relates to mechanical systems in a broad sense, with most examples from motor vehicles presented within fourteen chapters. The first chapter presents the basic definitions of design, the objectives pursued during the design process, the definition of virtual prototypes, etc. while second chapter of the monograph is devoted to describing of the characteristic operational regimes to which mechanical systems or motor vehicle are subjected in service conditions. The importance of modeling as a basis for automated design of mechanical systems and motor vehicles is presented in chapter three. Presented are types of commonly used models in mechanical engineering, starting from the simplified form of the imagination of actual object, through the mechanical (or model of the other sort of models) to the mathematical model. Within this chapter, it is particularly pointed out the importance of knowing the fundamental principles of theoretical disciplines as the basis for successful modeling. Bearing in mind that mechanics is the fundamental of mechanical engineering and motor vehicles, the fourth chapter shows the general theory of rigid body motion and mechanics of rigid bodies systems, because most of the software for automatically writing of the differential equations of motion are based on these problems. The fifth chapter is dedicated to the presentation of Finite Element Method and analysis of mechanical structures and problems in motor vehicles, using this method. Modal analyzes is an important tool used in design of mechanical systems, in particular motor vehicles, because it allows for the successful resolution the problems of noise and vibration. The sixth chapter is dedicated to this theory. The solutions based on fluid mechanics (various hydraulic and pneumatic installations, power-systems, etc.) have a broad application in mechanical systems and in motor vehicles. Therefore, in the seventh chapter there is a presentation of the theory of fluid mechanics, with emphasis on the compressible fluids. In the eighth chapter, the basic concepts of thermodynamics are given. It is significant here to point out that over 90% of the overall number of the existing vehicles is still driven by the internal combustion engine, whose operation is based on thermodynamic cycles. In modern mechanical systems, especially in motor vehicles, electrical and electronic systems have an important application. Therefore, the ninth chapter gives basic concepts of electrical engineering. Automation is a modem scientific discipline that is very common in all areas of mechanical engineering, including motor vehicles. Therefore, the tenth chapter presents the basic concepts of the automation, including the most modern systems, and systems with multiple input and output variables. The analogy of electrical and other physical variables has not lost its significance, despite the existence of powerful computer systems for numerical solution of the problem. The reason for this is the fact that the knowledge of engineers is focused on narrow areas, and for solving specific problems in practice (especially in automatics), it is suitable to present some systems as a “black box” to solve a system by using the above-mentioned analogy. An example of this can be a problem in the automation systems on vehicles, when electro-engineers can translate mechanical systems into analogue electrical circuits, which makes them easier to solve. This problem is described in the eleventh chapter and a number of examples from mechanics, acoustics, vibration and sound illustrates the procedures. Twelfth chapter is devoted to optimization methods as the basis for optimal design of mechanical systems and motor vehicles. Special attention was paid to the method of “stochastic parameter optimization”, and a process of optimization is illustrated by a complex task of optimal design in the area of motor vehicles. One of the stages of design is the experimental verification of the designed system. The experimental studies require expensive measuring chains, considerable time and significant financial resources. As it is known, planning of the experiment is a discipline aimed to reducing costs for testing of mechanical systems and motor vehicles. The thirteenth chapter is devoted to this issue while fourteenth chapter gives an example of optimal design of a complex system - system of active suspension of motor vehicles. Format B5, Publisher: Centаr zа nаučno-istrаživаčki rаd SANU i Univerzitet u Krаgujevcu; ISBN 978-86-81037-31-7

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INSTRUCTIONS FOR AUTHORS The benefits of publishing in Journal for Applied Engineering Science are: • No page charges • World wide exposure of your work • Accelerate publication times • Online author service • Automatic transfer of metacontent in SCOPUS, SJR, SCIndeks and other bases supporting international protocols for data transfer • Assignment of numerical identifiers DOI • Fair, constructive and able to follow reviewing process • Dedicated team to manage the publication process and to deal with your needs Submission of the papers has to be done online, trough journal e-service at http://aseestant.ceon.rs/index.php/jaes/login For assistance during the process of submission and publication, please contact graphical editor Mr. Darko Stanojevic at dstanojevic@iipp.rs or +381 116300750 Every manuscript submitted to JAES will be considered only if the results contained in the paper were not already published, that are not currently in the process of publishing and not to be published in another journal. Each paper is sent to a review by two independent experts and the authors are obligated to adopt the observations and comments of the reviewers. Articles presented at conferences may also be submitted, provided these articles do not appear in substantially the same form in published conference proceedings. All articles are treated as confidential until they are published. Manuscripts must be in English free of typing errors. The maximum length of contributions is 10 pages. THE FORMAT OF THE MANUSCRIPT The manuscript should be written in the following format: • A Title, which adequately describes the content of the manuscript. • An Abstract should not exceed 250 words. The Abstract should state the principal objectives and the scope of the investigation, as well as the methodology employed. It should summarize the results and state the principal conclusions. • Not more than 10 significant key words should follow the abstract to aid indexing. • An Introduction, which should provide a review of recent literature and sufficient background information to allow the results of the article to be understood and evaluated. • A Theory or experimental methods used. • An Experimental section, which should provide details of the experimental set-up and the methods used for obtaining the results. • A Results section, which should clearly and concisely present the data using figures and tables where appropriate. • A Discussion section, which should describe the relationships and generalizations shown by the results and discuss the significance of the results making comparisons with previously published work. (It may be appropriate to combine the Results and Discussion sections into a single section to improve the clarity). • Conclusions, which should present one or more conclusions that have been drawn from the results and subsequent discussion and do not duplicate the Abstract. • References, which must be cited consecutively in the text using brackets [1] and collected together in a reference list at the end of the manuscript and in alphabetic order.

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SADRŽAJ

OD UREĐIVAČKOG ODBORA Prof. dr Vesna Spasojević - Brkić UVODNIK

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REZIMEI RADOVA Dr Goran Putnik NAPREDNI PROIZVODNI SISTEMI I PREDUZEĆA: “CLOUD” I UBIKVITNA PROIZVODNJA I JEDNA ARHITEKTURA

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MSc Bojan Jovanovski, Dr Robert Minovski, Dr Siegfried Voessner Dr Gerald Lichtenegger KOMBINOVANI SISTEM DINAMIKE I SIMULACIJE DISKRETNIH DOGAĐAJAPREGLED HIBRIDNIH SIMULACIJSKIH MODELA

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Dr Isabel L. Nunes FAZI SISTEMI ZA PODRŠKU UPRAVLJANJU U INDUSTRIJSKOM INŽEMNJERSTVU

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Dr Mirjana Misita, Dr Galal Senussi, MSc Marija Milovanović PRIMENA KOMBINOVANOG MODELA GA (GENETSKIH ALGORITAMA) I RM (MATRICA RIZIKA) U ODREĐIVANJU OPTIMALNOG PROIZVODNOG PROGRAMA

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Jelena Jovanović, Dr Dragan Milovanović, Milić Radović, Radisav Đukić ISTRAŽIVANJE VREMENSKE I FINANSIJSKE DIMENZIJE PROIZVODNOG CIKLUSA SLOŽENOG PROIZVODA

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Dr Vidosav Majstorović U SUSRET DIGITALNOJ FABRICI - ISTRAŽIVANJA U SVETU I U NAŠOJ ZEMLJI

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Dr Jezdimir Knežević VREME JE ZA IZBOR IZMEĐU NAUČNOG I ADMINISTRATIVNOG PRISTUPA POUZDANOSTI

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OD UREĐIVAČKOG ODBORA

Predsednik Organizacionog odbora SIE 2012 UVODNIK POSEBNOJ TEMI: ODABRANI RADOVI SA 5. MEĐUNARODNOG SIMPOZIJUMA INDUSTRIJSKO INŽENJERSTVO - SIE 2012

Peti Međunarodni simpozijum ``Industrijsko inženjerstvo 2012`` (SIE 2012) imao je za cilj da obezbedi jedinstvenu platformu za susret istraživača, naučnika i stručnjaka i razmenu najsavremenijih dostignuća u oblasti. SIE 2012, održan je u organizaciji Katedre za Industrijsko inženjerstvo Mašinskog fakulteta Univerziteta u Beogradu, Srbija i Steinbeis Advanced Risk Technologies Stuttgart, Nemačka u junu 2012, u Beogradu, u Srbiji. Ovaj međunarodni događaj takođe je ispunio cilj da umrežavanje, saradnja i kolaboracija među učesnicima konferencije unaprede teoriju i praksu i identifikuju najznačajnije trendove u oblasti industrijskog inženjerstva danas. Zbornik sa preko 70 radova i diskusije od strane preko 160 autora doprineli su boljem razumevanju uloge i značaja industrijskog inženjerstva, a uprkos prikazima različitih konteksta i različitim interesovanjima učesnika, program skupa je spajajući različitosti potpunim angažmanom učesnika pružio mogućnosti za razmenu različitih stavova i ideja sa velikim entuzijazmom i sinergetskim efektima. Ovaj tradicionalni simpozijum dao je veliki doprinos unapređenju svesti o značaju industrijskog inženjerstva na međunarodnom, nacionalnom i lokalnom nivou. Prezentacije koje su organizovane tokom simpozijuma, prikazale su dostignuća na svim nivoima naučnog istraživanja kroz primenu sistemskog pristupa na prakse poslovanja kako velikih sistema tako i malih i srednjih preduzeća. Zaključak je da jaki dinamički trendovi u oblasti industrijskog inženjerstva zahtevaju stručnost i mudrost da se očuva dugogodišnja baza znanja u oblasti, zajedno sa uspostavljanjem fleksibilnosti i prilagođavanjem, neophodnim za susret sa novim izazovima, koje donosi vreme u kome živimo i poslujemo. Brojevi 3 i 4 časopisa ``Istraživanja i projektovanja za privredu`` sadrže izbor 11 radova, u oblastima disciplina industrijskog inženjerstva kao što su menadžment kvalitetom, upravljanje proizvodnjom, menadžment rizikom, ocena projekata itd. Najrelevantniji doprinosi odabrani su nakon standardnog procesa revizije od strane Uređivačkog odbora, a na osnovu mišljenja recenzenata SIE 2012. Ovim putem se zahvaljujemo članovima Naučno-programskog odbora SIE 2012 i autorima, kao i pokroviteljima simpozijuma. Posebnu zahvalnost dugujemo uredniku i osoblju časopisa ``Istraživanja i projektovanja za privredu``, kao i recenzentima za njihov predan rad. Beograd, jun 2012 Prof. dr Vesna Spasojević-Brkić

Journal of Applied Engineering Science 10(2012)3

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REZIMEI RADOVA Broj rada: 10(2012)3, 229

doi:10.5937/jaes10-2511

NAPREDNI PROIZVODNI SISTEMI I PREDUZEĆA: “CLOUD” I UBIKVITNA PROIZVODNJA I JEDNA ARHITEKTURA Dr Goran Putnik University of Minho, Faculty of Engineering, Braga, Portugal U prvom delu rada je predstavljen koncept ubikvitne (“svudaprisutne”, “sveprisutne”) i “cloud” proizvodnje, kao model naprednih proizvodnih sistema i preduzeća. U drugom delu, predstavljena je jedna arhitektura, koja može sadržati implementaciju i eksploataciju ubikvitne i “cloud” proizvodnje kroz neformalnu i konceptualnu prezentaciju. Ključne reči: Ubikvitna proizvodnja, Svudaprisutna proizvodnja, Sveprisutna proizvodnja, Proizvodni sistem, Arhitektura, Sistem usluga, Paradigma doi:10.5937/jaes10-2512

Broj rada: 10(2012)3, 230

KOMBINOVANI SISTEM DINAMIKE I SIMULACIJE DISKRETNIH DOGAĐAJA-PREGLED HIBRIDNIH SIMULACIJSKIH MODELA

MSc Bojan Jovanovski Ss. Cyril and Methodi University, Faculty of Mechanical Engineering, Skopje, Makedonia Dr Robert Minovski Ss. Cyril and Methodi University, Faculty of Mechanical Engineering, Skopje, Makedonia Dr Siegfried Voessner Institute of Engineering and Business Informatics, TU Graz, Austria Dr Gerald Lichtenegger Institute of Engineering and Business Informatics, TU Graz, Austria Simulacija i modeliranje su široko prihvaćeni kao jedan od najvažnijih aspekata industrijskog inženjerstva. Primena i upotreba modela simulacije je eksponencijalno rasla od 1950. godine do danas. Svih tih godina kompleksnost simulacionih aspekata je adaptirana u odnosu na kompleksnost analiza slučaja koja je rasla proporcionalno. To je razlog zašto davno korišćene tehnike, često ne mogu više dati adekvatnu predstavu realnog sveta. Iz tog razloga, predloženo je upotreba hibridnih simulacionih modela, koji predstavljaju kombinaciju simulacionih obrazaca za rešavanje problema. U radu je predstavljen pregled odabranih istraživanja I upotreba sa naglaskom na simulacije diskretnih događaja i dinamičkih sistema, kao ključnu simulaciju zasnovanu na tehnikama i predstavljenoj oblasti. Ključne reči: Hibrid, simulacije, Model, Dinamički sistem, Simulacija diskretnih događaja doi:10.5937/jaes10-2510 Broj rada: 10(2012)3, 231 FAZI SISTEMI ZA PODRŠKU UPRAVLJANJU U INDUSTRIJSKOM INŽENJERSTVU Dr Isabel L. Nunes

Universidade Nova de Lisboa, Faculty of Science and Technology, Portugal Rad predstavlja prikaz potencijala primene teorije “Fuzzy`` skupova u rešavanju problema, koji sadrže kompleksne, nepotpune i/ili nejasne informacije, često karakteristične za probleme u industrijskom inženjerstvu. Predstavljena su dva sistema za podršku aktivnostima upravljanja u industrijskom inženjerstvu kao primer upotrebe ove matematičke metode. Ključne reči: Ergonomija, Poremećaji mišićne muskulature, Lanac snabdevanja, Otpor, Poremećaji

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REZIMEI RADOVA

doi:10.5937/jaes10-2523

Broj rada: 10(2012)3, 232

PRIMENA KOMBINOVANOG MODELA GA (GENETSKIH ALGORITAMA) I RM (MATRICA RIZIKA) U ODREĐIVANJU OPTIMALNOG PROIZVODNOG PROGRAMA

Dr Mirjana Misita Univerzitet u Beogradu, Mašinski Fakultet, Beograd, Srbija Dr Galal Senussia Omar El-Mohktar University, Industrial Engineering Department, El-Baitha, Libya MSc Marija Milovanović Univerzitet u Beogradu, Mašinski Fakultet, Beograd, Srbija Jedan od veoma važnih ciljeva u svakom preduzeću je naći optimalno rešenje kod inverznih višekriterijumskih funkcija. Funkcija kojom se opisuju troškovi i funkcija kojom se opisuje profit po jedinici proizvoda su dve inverzne funkcije sa mnogo konfliktnih informacija o proizvodnim parametrima. Pored toga, za donosioca odluke veoma važno je ukazati na rizik koje optimalno rešenje nosi sa sobom, Iz tog razloga u radu je razvijen model koji predstavlja kombinaciju primene genetskih algoritama (GA) i matrica riika, radi poboljšanja kvaliteta odluke koja se bazira na na kvantitativnim indikatorima, a ne samo na kvalitativnim. Rezultati istraživanja ukazuju da model integracije GA i RM ima veoma veliki značaj u olakšanju procesa odlučivanja o optimalnom proizvodnom programu uz istovremeno i povećanje kvaliteta donesenih odluka. Ključne reči: Troškovi, Matrice, Optimalni program proizvodnje, Upravljanje rizikom, Genetski algoritmi, Više-ciljna funkcija

doi:10.5937/jaes10-2514

Broj rada: 10(2012)3, 233

ISTRAŽIVANJE VREMENSKE I FINANSIJSKE DIMENZIJE PROIZVODNOG CIKLUSA SLOŽENOG PROIZVODA

Jelena Jovanović Tehnički fakultet, Čačak, Srbija Dr Dragan Milanović Univerzitet u Beogradu, Mašinski Fakultet, Beograd, Srbija Dr Milić Radović Univerzitet u Beogradu, Fakultet Organizacionih Nauka, Beograd, Srbija Radisav Đukić Kancelarija za proizvodnju i upravljanje inženjerstvom, “Sloboda” Co. Čačak, Srbija Obeležja savremene proizvodnje sa vrhunskom organizacijom i metodama upravljanja zasnivaju se na načelima ekonomije vremena i principima nove proizvodne filozofije-lean production. Proizvodnju treba organizovati po principu usisavanja, sa minimalnim zalihama, radeći samo ono što je stvarno potrebno, ni prerano ni prekasno. U radu je prikazan postupak projektovanja i rezultati istraživanja proizvodnog ciklusa složenog proizvoda koji se nalazi u proizvodnom programu kompanije “Sloboda” Čačak. Ključne reči: Kompleksni proizvod, Proizvodni ciklus, Projektovanje, Koeficijent radnog vremena, Trenutna sredstva

Journal of Applied Engineering Science 10(2012)3

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REZIMEI RADOVA Broj rada: 10(2012)3, 234

doi:10.5937/jaes10-2513

U SUSRET DIGITALNOJ FABRICI - ISTRAŽIVANJA U SVETU i U NAŠOJ ZEMLJI

Dr Vidosav Majstorović Univerzitet u Beogradu, Mašinski Fakultet, Beograd, Srbija Rad predstavlja analizu i sintezu istraživanja u oblasti digitalne fabrike i digitalne proizvodnje. Cilj je predstavljenje različitih pristupa i koncepta, digitalne proizvodnje i digitalne fabrike, za predlog uvođenja sličnog istraživačkog pristupa. Inženjerski model proizvodnje zasnovan na digitalnom modelu proizvoda, procesi i sredstva su budućnost proizvodnog inženjerstva u ovoj oblasti, i šta više oni su subjekat analize u studiji od posebne važnosti. Na kraju posebno su predstvaljene buduće smernice za istraživanje u polju digitalne fabrike i proizvodnje. Ključne reči: Digitalna fabrika, Digitalna proizvodnja, Proizvodnja, Modeliranje, Fabrike

Paper number: 10(2012)3, 235

doi:10.5937/jaes10-2507

VREME JE ZA IZBOR IZMEĐU NAUČNOG I ADMINISTRATIVNOG PRISTUPA POUZDANOSTI

Dr Jezdimir Knežević MIRCE Akademy, Woodbury Park, Exeter, United Kingdom Glavni cilj rada je da predstavi autorov pogled na pouzdanost, pristup koji se zasniva na zakonima nauke. Tipičan primer koji je prikazan je dobro poznat i prihvaćen model pouzdanosti sistema koji zahteva prihvatanje “alternativnih univerzuma” da bi podržao tezu da komponente a i sami sistemi poseduju konstantnu, vremenski nezavisnu meru otkaza. Ovaj pristup ne proizilazi niti iz konvencionalne nauke niti iz eksperimentalnih istraživanja, već od imaginarnih koraka predviđenih u glavama njegovih pristalica gde je po njihovom stavu dozvoljeno suspendovanje svih zakona nauke. Međutim, ovaj pristup je u direktnoj suprotnosti sa uočenim pojavama kao što su korozija, zamor, habanje, problem kvaliteta i sa mnogim drugim vremensko zavisnim fizičkim procesima koji jasno pokazuju da pouzdanost komponenti/sistema za određeni vremenski period može imati povećanu, konstantnu i opadajući verovatnoću uspeha poštujući životni ciklus sistema, komponenti i primenjeni način održavanja, kao naučni pristup zasnovan na funkciji pouzdanosti. Ključne reči: Nauka, Sistem, Pristup, Pouzdanost, Zakon

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Journal of Applied Engineering Science 10(2012)3


Journal of Applied Engineering Science 10(2012)3  

Journal of Applied Engineering Science publish original and review articles covering the concept of technical science, energy and environmen...

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