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IJAT Vol.6 No.3 pp. 296-303
doi: 10.20965/ijat.2012.p0296
(2012)

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State of Art and Research Demands for Simulation Modeling of Green Supply Chains

Markus Rabe and Maik Deininger

Fachgebiet IT in Produktion und Logistik, Fakultät Maschinenbau, Technical University Dortmund, Leonhard-Euler-Str. 5, 44227 Dortmund, Germany

Received:
November 14, 2011
Accepted:
March 13, 2012
Published:
May 5, 2012
Creative Commons License
Keywords:
green supply chain, sustainable supply chain, simulation, optimization
Abstract
In this paper, we review simulation and modeling techniques focusing on green and sustainable supply chains. We start by introducing green supply chains and the importance of being aware of environmental friendliness. We show how environmental performance is measured and analyzed, and then discuss the extension of green to sustainable supply chains. Taking into account the complex interrelations within supply chains, we give an overview of modeling and simulation techniques. This enables us to explain how supply chain behavior can be predicted and optimized under a set of given objectives. We conclude by suggesting the possibilities provided by modeling and simulating green and sustainable supply chains and propose future research.
Cite this article as:
M. Rabe and M. Deininger, “State of Art and Research Demands for Simulation Modeling of Green Supply Chains,” Int. J. Automation Technol., Vol.6 No.3, pp. 296-303, 2012.
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References
  1. [1] J. Sarkis, Q. Zhu, and K.-H. Lai, “An Organizational Theoretic Review of Green Supply Chain Management Literature,” Int. J. of Production Economics, Vol.130, No.1, pp. 1-15, 2011.
  2. [2] S. Seuring and M. Müller, “From a literature review to a conceptual framework for sustainable supply chain management,” J. of Cleaner Production, Vol.16, No.15, pp. 1699-1710, 2008.
  3. [3] Q. Zhu, J. Sarkis, and K. Lai, “Green supply chain management: pressures, practices and performance within the Chinese automobile industry,” J. of Cleaner Production, Vol.15, No.11-12, pp. 1041-1052, 2007.
  4. [4] I. Ferretti, S. Zanoni, et al., “Greening the aluminium supply chain,” Int. J. of Production Economics, Vol.108, No.1-2, pp. 236-245, 2007.
  5. [5] D. Holt and A. Ghobadian, “An empirical study of green supply chain management practices amongst UK manufacturers,” J. of Manufacturing Technology Management, Vol.20, No.7, pp. 933-956, 2009.
  6. [6] S. Vachon, “Green supply chain practices and the selection of environmental technologies,” Int. J. of Production Research, Vol.45, No.18-19, pp. 4357-4379, 2007.
  7. [7] S. Vachon and R. D. Klassen, “Supply chain management and environmental technologies: the role of integration,” Int. J. of Production Research, Vol.45, No.2, pp. 401-423, 2007.
  8. [8] M. T. Frohlich and R. Westbrook, “Arcs of integration: an international study of supply chain strategies,” J. of Operations Management, Vol.19, No.2, pp. 185-200, 2001.
  9. [9] Q. Zhu and J. Sarkis, “The moderating effects of institutional pressures on emergent green supply chain practices and performance,” Int. J. of Production Research, Vol.45, No.18-19, pp. 4333-4355, 2007.
  10. [10] M. Saint-Jean, “Polluting emissions standards and clean technology trajectories under competitive selection and supply chain pressure,” J. of Cleaner Production, Vol.16, No.1, Supplement 1, pp. 113-123, 2008, .
  11. [11] Y. Gao, J. Li, and Y. Song, “Performance evaluation of green supply chain management based on membership conversion algorithm,” Proc. of ISECS Int. Colloquium on Computing, Communication, Control, and Management, Sanya, China, pp. 237-240, 2009.
  12. [12] Z. H. Che, “Using fuzzy analytic hierarchy process and particle swarm optimisation for balanced and defective supply chain problems considering WEEE/RoHS directives,” Int. J. of Production Research, Vol.48, No.11, pp. 3355-3381, 2010.
  13. [13] Deutsches Institut für Normung, “Environmental management systems – Requirements with guidance for use,” 2009.
  14. [14] “Directive 2002/96/EC of the European Parliament and of the Council of 27 January 2003 on waste electrical and electronic equipment (WEEE),” Official J. of the European Union, pp. 24-38, 2003.
  15. [15] “Directive 2002/95/EC of the European Parliament and of the Council of 27 January 2003 on the restriction of the use of certain hazardous substances in electrical and electronic equipment,” Official J. of the European Union, pp. 19-23, 2003.
  16. [16] S. Kara, F. Rugrungruang, and H. Kaebernick, “Simulation modelling of reverse logistics networks,” Int. J. of Production Economics, Vol.106, No.1, pp. 61-69, 2007.
  17. [17] M. P. de Brito, V. Carbone, and C. Blanquart, “Towards a sustainable fashion retail supply chain in Europe: Organisation and performance,” Int. J. of Production Economics, Vol.114, No.2, pp. 534-553, 2008.
  18. [18] J. Fiksel, “Evaluating Supply Chain Sustainability,” Chemical Engineering Progress, Vol.106, No.5, pp. 28-38, 2010.
  19. [19] S. K. Kumar, M. Tiwari, and R. F. Babiceanu, “Minimisation of supply chain cost with embedded risk using computational intelligence approaches,” Int. J. of Production Research, Vol.48, No.13, pp. 3717-3739, 2010.
  20. [20] M.-C. Chiu, A. J. Alsaffar, et al., “Reducing supply chain costs and carbon footprint during product design,” Proc. of the 2010 IEEE Int. Symp. on Sustainable Systems & Technology (ISSST), Arlington, VA, U.S.A, pp. 1-6, 2010.
  21. [21] D. Wu and D. L. Olson, “Supply Chain Risk, Simulation, and Vendor Selection,” Int. J. of Production Economics, Vol.114, No.2, pp. 646-655, 2008.
  22. [22] A. Karagiannaki, I. Mourtos, and K. Pramatari, “Simulating and evaluating the impact of RFID on warehousing operations: a case study,” Proc. of 2007 Summer Computer Simulation Conf., San Diego, CA, U.S.A, short paper without pages, 2007.
  23. [23] M. Rabe and F.-W. Jäkel, “Simulation von Nachhaltigkeitsaspekten im industriellen Umfeld und deren Auswirkungen auf die Simulationstechnik,” Proc. of ASIM 2009 Simulationstechnik, Cottbus, Germany, pp. 36-43, 2009.
  24. [24] L. Chwif, M. R. P. Barretto, and E. Saliby, “Supply chain analysis: spreadsheet or simulation?” Proc. of the 2002 Winter Simulation Conf., San Diego, CA, U.S.A, pp. 59-66, 2002.
  25. [25] O. Jellouli and E. Chatelet, “Monte Carlo simulation and genetic algorithm for optimising supply chain management in a stochastic environment,” Proc. of the Int. Conf. on Systems, Man and Cybernetics, Piscataway, NJ, U.S.A, pp. 1835-1839, 2001.
  26. [26] D. Hellström and M. Johnsson, “Using discrete event simulation in supply chain planning,” Proc. of 14th Annual Conf. for Nordic Researchers in Logistics, Trondheim, Norway, 2002.
  27. [27] Y.-H. Low, B.-P. Gan, et al., “Parallel discrete-event simulation of a supply-chain in semiconductor industry,” Proc. of The Fourth Int. Conf. Exhibition on High-Performance Computing in the Asia-Pacific Region, Beijing, China, pp. 1154-1157, 2000.
  28. [28] B.-P. Gan, L. Liu, et al., “Distributed supply chain simulation across enterprise boundaries,” Proc. of 2002 Winter Simulation Conf., Orlando, FL, U.S.A, pp. 1245-1251, 2000.
  29. [29] M. Preusser, C. Almeder, et al., “LP Modelling and Simulation of Supply Chain Networks,” Supply Chain Management und Logistik, pp. 95-113, 2005.
  30. [30] H. S. Sarjoughian, D. Huang, et al., “Hybrid Discrete Event Simulation with Model Predictive Control for Semiconductor Supplychain Manufacturing,” Proc. of 2005 Winter Simulation Conf., Orlando, FL, U.S.A, pp. 256-266, 2005.
  31. [31] L. Rabelo, M. Helal, and C. Lertpattarapong, “Analysis of Supply Chains Using System Dynamics, Neural Nets, and Eigenvalues,” Proc. of 2004 Winter Simulation Conf., Washington, D.C, U.S.A, pp. 81-89, 2004.
  32. [32] K. H. Shin, I.-H. Kwon, et al., “Performance trajectory-based optimised supply chain dynamics,” Int. J. of Computer Integrated Manufacturing, Vol.23, No.1, pp. 87-100, 2010.
  33. [33] S. Jin, J. Zhuang, and Z. Liu, “Monte Carlo simulation-based supply chain disruption management for wargames,” Proc. of the Winter Simulation Conf. (WSC) 2010, pp. 2682-2693, 2010.
  34. [34] L. A.W. da Silva and L. d. S. Coelho, “An Adaptive Particle Swarm Approach Applied to Optimization of a Simplified Supply Chain,” Proc. of 19th Int. Conf. on Production Research, Valparaiso, Chile, 2007.
  35. [35] S. J. Lim, S. J. Jeong, K. S. Kim, and M. W. Park, “Hybrid approach to distribution planning reflecting a stochastic supply chain,” The Int. J. of Advanced Manufacturing Technology, Vol.28, No.5-6, pp. 618-625, 2006.
  36. [36] L. Y. Y. Lu, C. H. Wu, and T.-C. Kuo, “Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis,” Int. J. of Production Research, Vol.45, No.18-19, pp. 4317-4331, 2007.
  37. [37] F. Pirard, S. Iassinovski, and F. Riane, “A generic scalable simulation model for strategic supply chain management with emphasis on production activities,” Int. J. of Computer Integrated Manufacturing, Vol.21, No.4, pp. 455-467, 2008.
  38. [38] M. D. Rossetti and Y. Liu, “Simulating SKU proliferation in a health care supply chain,” Proc. of 2009 Winter Simulation Conf., Piscataway, NJ, U.S.A, pp. 2365-2374, 2009.
  39. [39] A. H. Dong, W. K. Wong, et al., “Developing an Apparel Supply Chain Simulation System with the Application of Fuzzy Logic,” Computational Textile, Springer, Berlin, Heidelberg, Vol.55, pp. 185-199, 2007.
  40. [40] B.-P. Gan, Y.-H. Low, et al., “Load balancing for conservative simulation on shared memory multiprocessor systems,” PADS ’00 Proc. of the fourteenth Workshop on Parallel and Distributed Simulation, Los Alamitos, CA, U.S.A, pp. 139-146, 2000.
  41. [41] K. Mertins and M. Rabe, “Inter-Enterprise Planning of Manufacturing Systems applying Simulation with IPR Protection,” Proc. of 5th Int. Conf. on Design for Information Systems for Manufacturing (DIISM), Osaka, Japan, pp. 149-156, 2002.
  42. [42] E. F. Camacho and C. Bordons, “Model predictive control,” 2nd (Ed.), Springer, London, 2007.
  43. [43] W. Wang and D. E. Rivera, “Model Predictive Control for Tactical Decision-Making in Semiconductor Manufacturing Supply Chain Management,” IEEE Trans. on Control Systems Technology, Vol.16, No.5, pp. 841-855, 2008.
  44. [44] J.-P. Ylén and V. Hölttä, “System Dynamics – a Tool for Designing and Analysing Complex Processes,” Simulation News Europe, Vol.17, No.1, pp. 27-31, 2007.
  45. [45] N. Costantino, M. Dotoli, et al., “A model for the strategic design of Distribution Networks,” Proc. of IEEE Conf. on Automation Science and Engineering (CASE), Piscataway, NJ, U.S.A, 2010.
  46. [46] F. T. S. Chan, S. H. Chung, and S. Wadhwa, “A heuristic methodology for order distribution in a demand driven collaborative supply chain,” Int. J. of Production Research, Vol.42, No.1, pp. 1-19, 2004.
  47. [47] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proc. of the Int. Conf. on Neural Networks, Piscataway, NJ, U.S.A, pp. 1942-1948, 1995.
  48. [48] A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Selforganizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Trans. on Evolutionary Computation, Vol.8, No.3, pp. 240-255, 2004.
  49. [49] L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol.8, No.3, pp. 338-353, 1965.

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