Behzad Behdani
ABSTRACT
Each simulation paradigm is characterized by a set of core assumptions and some underlying concepts to describe the world. These assumptions, in fact, constrain the development of a conceptual model for the system of study. Consequently, the choice of appropriate simulation paradigm is an important step in the model development process. In this paper, selection of a simulation approach for supply chain modeling is discussed. For this purpose, the supply chain is described from perspective of two well-established system theories. Firstly, supply chains are defined as socio-technical systems. Afterwards, they are described from complex adaptive systems perspective. This study gives a set of features for supply chains as complex socio-technical systems which is subsequently used to compare three simulation paradigms for supply chain modeling -- namely, system dynamics, discrete-even simulation and agent-based simulation.
- Allen, T. F. H., and T. B. Starr. 1982. Hierarchy: Perspectives in Ecological Complexity. University of Chicago Press.Google Scholar
- Altiok, T., and B. Melamed. 2007. Simulation Modeling and Analysis with Arena, Academic Press.Google Scholar
- Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol. 2010. Discrete-Event System Simulation. Prentice Hall.Google Scholar
- Bar-Yam, Y. 2003. Dynamics of Complex Systems, Westview Press. Google ScholarDigital Library
- Berkes, F. 2006. "From Community-Based Resource Management To Complex Systems." Ecology and Society 11: 45.Google ScholarCross Ref
- Bonabeau, E. 2002a. "Predicting the Unpredictable." Harvard Business Review 80: 109--117.Google Scholar
- Bonabeau, E. 2002b. "Agent-based Modeling: Methods and Techniques for Simulating Human Systems." In Proceedings of the National Academy of Science 99: 7280--7287.Google ScholarCross Ref
- Bozarth, C. C., D. P. Warsing, B. B. Flynn, and E. J. Flynn. 2009. "The Impact of Supply Chain Complexity on Manufacturing Plant Performance." Journal of Operations Management 27: 78--93.Google ScholarCross Ref
- Cassandras, C. G., and S. Lafortune. 1999. Introduction to Discrete-Event Systems. Kluwer Academic Publishers.Google Scholar
- Choi, T., K. Dooley, and M. Rungtusanatham. 2001. "Supply Networks and Complex Adaptive Systems: Control versus Emergence." Journal of Operations Management 19: 351--366.Google ScholarDigital Library
- Choi, T., and D. R. Krause. 2006. "The Supply Base and Its Complexity: Implications for Transaction Costs, Risks, Responsiveness, and Innovation." Journal of Operations Management 24: 637--652.Google ScholarCross Ref
- Crooks, A., C. Castle, and M. Batty. 2008. "Key Challenges in Agent-Based Modeling for Geo-Spatial Simulation." Computers, Environment and Urban Systems 32: 417--430.Google ScholarCross Ref
- Coyle, R. G. 1996. System Dynamics Modeling: A Practical Approach. Chapman & Hall.Google Scholar
- Daft, R. L., and R. H. Lengel. 1984. "Information Richness: A New Approach to Managerial Behavior and Organization Design." Research in Organizational Behavior 6: 191--223, JAI Press.Google Scholar
- Daft R. L. 2004. Organization Theory and Design. Thomson South-Western.Google Scholar
- de Bruijn, J. A., and P. M. Herder. 2009. "System and Actor Perspectives on Sociotechnical Systems." IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 39: 981--992. Google ScholarDigital Library
- Demirel, G. 2007. "Aggregated and Disaggregated Modeling Approaches to Multiple Agent Dynamics." In Proceedings of the 25th International Conference of the System Dynamics Society.Google Scholar
- Eisner, H. 2005. Managing Complex Systems: Thinking Outside the Box. John Wiley & Sons.Google Scholar
- Epstein, J. M. 2006. Generative Social Science: Studies in Agent-based Computational Modeling. Princeton University Press. Google ScholarDigital Library
- Finnigan, J. 2005. The Science of Complex Systems. Australasian Science 26: 32--34.Google Scholar
- Forrester, J. 1961. Industrial Dynamics. MIT Press.Google Scholar
- Forrester, J. 1968. Principles of Systems. OR: Productivity Press.Google Scholar
- Forrester, J. 1969. Urban Dynamics. Productivity Press.Google Scholar
- Foster, J., and W. Hölzl. 2004. Applied Evolutionary Economics and Complex Systems. Edward Elgar.Google Scholar
- Garcia, R. 2005. "Uses of Agent-Based Modeling in Innovation/New Product Development Research." Journal of Product Innovation Management 22: 380--398.Google ScholarCross Ref
- Ghiani G., G. Laporte, and R. Musmanno. 2004. Introduction to Logistics Systems Planning and Control. John Wiley & Sons. Google ScholarDigital Library
- Gilbert, N., and K. G. Troitzsch. 1999. Simulation for the Social Scientist. Open Univ. Press. Google ScholarDigital Library
- Gilbert, N., and P. Terna. 2000. "How to Build and Use Agent-Based Models in Social Science." Mind and Society 1: 57--72.Google ScholarCross Ref
- Gilbert, N. 2007. Agent-based Models. Sage Publications.Google Scholar
- Heath, S. K., S. C. Brailsford, A. Buss, and C. M. Macal. 2011. "Cross-paradigm Simulation Modeling: Challenges and Successes." In Proceedings of the 2011 Winter Simulation Conference, 2788--2802. Google ScholarDigital Library
- Helbing, D. 2001. "Traffic and Related Self-Driven Many-Particle Systems." Reviews of Modern Physics 73: 1067--1141.Google ScholarCross Ref
- Helbing, D., W. Yu, and H. Rauhut. 2011. "Self-Organization and Emergence in Social Systems: Modeling the Coevolution of Social Environments and Cooperative Behavior." Journal of Mathematical Sociology 35: 177--208.Google ScholarCross Ref
- Holland, J. H. 1996. Hidden Order: How Adaptation Builds Complexity. Addison-Wesley. Google ScholarDigital Library
- Holland, J. H. 1999. Emergence: from Chaos to Order. Addison-Wesley. Google ScholarDigital Library
- Kauffman, S. A. 1993. The Origins of Order Self-Organisation and Selection in Evolution. Oxford University Press.Google Scholar
- Kauffman, S. and W. MacReady. 1995. "Technological Evolution and Adaptive Organizations." Complexity 1: 26--43.Google ScholarCross Ref
- Lane, D. A. 2002. "Complexity and Local Interactions: Towards a Theory of Industrial Districts." In Complexity and Industrial Clusters: Dynamics and Models in Theory and Practice, edited by Quadrio Curzio A and Fortis M, Springer Verlag.Google Scholar
- Martinez, J. C., and P. G. Ioannou. 1999. "General-purpose Systems for Effective Construction Simulation." Journal of Construction Engineering and Management 125: 265--276.Google ScholarCross Ref
- Meadows, D. and J. Robinson. 1985. The Electronic Oracle. Computer Models and Social Decisions. John Wiley & Sons.Google Scholar
- Meadows, D. 1989. "System Dynamics Meets the Press." System Dynamics Review 5: 68--80.Google ScholarCross Ref
- Mentzer, J. T., W. DeWitt, J. S. Keebler, S. Min., N. W. Nix, C. D. Smith, and Z. G. Zacharia. 2001. "Defining Supply Chain Management." Journal of Business Logistics 22: 1--25.Google ScholarCross Ref
- Miller, J. A., G. Baramidze, P. A. Fishwick, and A. P. Sheth. 2004. "Investigating Ontologies for Simulation Modeling." In Proceedings of the 37th Annual Simulation Symposium, 55--71. Google ScholarDigital Library
- Miller, J. H., and S. E. Page. 2007. Complex Adaptive Systems: An Introduction to Computational Models Of Social Life. Princeton University Press Google ScholarDigital Library
- Mitleton-Kelly, E. 2003. "Ten Principles of Complexity and Enabling Infrastructures". In Complex Systems and Evolutionary Perspectives of Organisations: The Application of Complexity Theory to Organisations, edited by E. Mitleton-Kelly, Elsevier.Google Scholar
- Mussa, Y. M. 2009. A System Dynamics Model for Operations Management Improvement in Multi-Plant Enterprise. Master's thesis, Delft University of Technology, Faculty of Technology, Policy and Management.Google Scholar
- North, M. J. and, C. M. Macal. 2007. Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation. Oxford University Press. Google ScholarDigital Library
- Ottens, M., M. Franssen, P. Kroes, and I. Van De Poel. 2006. "Modelling Infrastructures as Socio-technical Systems." International Journal of Critical Infrastructures 2: 133--145.Google ScholarCross Ref
- Page, S. E. 2011. Diversity and Complexity. Princeton University Press. Google ScholarDigital Library
- Page, S. E. 2006. "Path Dependence." Quarterly Journal of Political Science 1: 87--115.Google ScholarCross Ref
- Pidd, M. 1998. Computer Simulation in Management Science. John Wiley and Sons. Google ScholarDigital Library
- Rahmandad, H., and J. D. Sterman. 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-based and Differential Equation Models." Management Science 54: 998--1014. Google ScholarDigital Library
- Richardson, G. P. 1991. Feedback Thought in Social Science and Systems Theory. University of Pennsylvania Press. Google ScholarDigital Library
- Salamon, T. 2011. Design of Agent-based Models: Developing Computer Simulations for a Better Understanding of Social Processes. Bruckner Publishing. Google ScholarDigital Library
- Schneeweiss, C. 2003. Distributed Decision Making. Springer-Verlag.Google Scholar
- Schieritz, N. and A. Grobler. 2003. "Emergent Structures in Supply Chains --A Study Integrating Agent-Based and System Dynamics Modeling." In Proceedings of the 36th Annual Hawaii International Conference on System Sciences. Google ScholarDigital Library
- Schieritz, N., and P. Milling. 2003. "Modeling the Forest or Modeling the Trees - A Comparison of System Dynamics and Agent-Based Simulation." In Proceedings of the 21st International Conference of the System Dynamics Society.Google Scholar
- Scholl, H. J. 2001. "Agent-based and System Dynamics Modeling: A Call for Cross Study and Joint Research." In Proceedings of the 34th Hawaiian International Conference on Systems Science. Google ScholarDigital Library
- Siebers, P. O., C. M. Macal, J. Garnett, D. Buxton, and M. Pidd. 2010. "Discrete-event Simulation is Dead; Long Live Agent-based Simulation." Journal of Simulation 4: 204--210.Google ScholarCross Ref
- Senge, P. M. 1990. The Fifth Discipline: the Art and Practice of the Learning Organization. Doubleday Dell Publishing.Google Scholar
- Silver, G. A., J. A. Miller, M. Hybinette, G. Baramidze, and W. S. York. 2010. "DeMO: An Ontology for Discrete-event Modeling and Simulation." Simulation 87: 747--773. Google ScholarDigital Library
- Simon, H. A. 1962. "The Architecture of Complexity." Proceedings of the American Philosophical Society 106: 467--482.Google Scholar
- Sterman, J. and J. Wittenberg. 1999. "Path Dependence, Competition and Succession in the Dynamics of Scientific Revolution." Organisation Science 10: 322--341. Google ScholarDigital Library
- Sterman, J. D. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill. Google ScholarDigital Library
- Surana, A., S. Kumara, M. Greaves, and U. N. Raghavan. 2005. "Supply-chain Networks: a Complex Adaptive Systems Perspective." International Journal of Production Research 43: 4235--4265.Google ScholarCross Ref
- Wooldridge, M., and N. Jennings. 1995. "Intelligent Agents: Theory and Practice." Knowledge Engineering Review 10: 115--152.Google ScholarCross Ref
- Wooldridge, M. 2002. An Introduction to Multiagent Systems. John Wiley & Sons. Google ScholarDigital Library
- Yolles, M. 2006. Organizations as Complex Systems. An Introduction to Knowledge Cybernetics. Information Age Publishing (IAP).Google Scholar
- Zhao, X., J. Xie and W. J. Zhang. 2002. "The Impact of Information Sharing and Ordering Co-ordination on Supply Chain Performance." Supply Chain Management 7: 24--40.Google ScholarCross Ref
Index Terms
- Evaluation of paradigms for modeling supply chains as complex socio-technical systems
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