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Supply Chain Design Using Simulation-Based NSGA-II Approach

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Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

Abstract

This chapter addresses the design of supply chain networks including both network configuration and related operational decisions such as order splitting, transportation allocation and inventory control. The goal is to achieve the best compromise between cost and customer service level. An optimisation methodology that combines a multi-objective genetic algorithm (MOGA) and simulation is proposed to optimise not only the structure of the network but also its operation strategies and related control parameters. A flexible simulation framework is developed to enable the automatic simulation of the supply chain network with all possible configurations and all possible control strategies. To illustrate its effectiveness, the proposed methodology is applied to two real-life case studies from automotive industry and textile industries.

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References

  1. Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2003). Designing and managing the supply chain: Concepts, strategies and case studies. New York: McGraw-Hill.

    Google Scholar 

  2. National Research Council, Visionary manufacturing challenges for 2020 (1998). Committee on visionary manufacturing challenges, board on manufacturing and engineering design, commission on engineering and technical systems. Washington, DC: National Academy Press.

    Google Scholar 

  3. Schmidt, G., & Wilhelm, E. (2000). Strategic, tactical and operational decisions in multi-national logistics networks: A review and discussion of modeling issues. International Journal of Production Research, 39(7), 1501–1523.

    Article  Google Scholar 

  4. Goetschalckx, M., Vidal, C. J., & Dogan, K. (2002). Modeling and design of global logistic systems: A review of integrated strategic and tactical models and design algorithms. European Journal of Operational Research, 143, 1–18.

    Article  MATH  Google Scholar 

  5. Meixell, M. J., & Gargeya, V. B. (2005). Global supply chain design: A literature review and critique. Transportation Research Part E, 41, 531–550.

    Article  Google Scholar 

  6. Klose, A., & Drexel, A. (2005). Facility location models for distribution system design. European Journal of Operational Research, 162, 4–29.

    Article  MathSciNet  MATH  Google Scholar 

  7. ReVelle, C. S., & Eiselt, H. A. (2005). Location analysis: A synthesis and survey. European Journal of Operational Research, 165, 1–19.

    Article  MathSciNet  MATH  Google Scholar 

  8. Jain, V., Wadhwa, S., & Deshmukh, S. G. (2006). Modeling and analysis of supply chain dynamics: a high intelligent time petri net based approach. International Journal of Industrial and Systems Engineering, 1(1/2), 59–86.

    Article  Google Scholar 

  9. Zarandi, M. H. F., Turksen, I. B., & Saghiri, S. (2002). Supply chain: Crisp and fuzzy aspects. International Journal of Applied Mathematics and Computer Science, 12(3), 423–435.

    Google Scholar 

  10. Swaminathan, M. J., Smith, S. F., & Sadeh, N. M. (1998). Modeling supply chain dynamics: A multiagent approach. Decision Sciences, 29(3), 607–632.

    Article  Google Scholar 

  11. Melo, M. T., Nickel, S., & Saldanha da Gama, F. (2006). Dynamic multi-commodity capacitated facility location: A mathematical modeling framework for strategic supply chain planning. Computers and Operations Research, 33, 181–208.

    Article  MATH  Google Scholar 

  12. Geoffrion, A. M., & Graves, G. W. (1974). Multi-commodity distribution system design by Bender’s decomposition. Management Science, 20, 822–844.

    Article  MathSciNet  MATH  Google Scholar 

  13. Cohen, M. A., & Lee, H. L. (1985). Manufacturing strategy: concepts and methods. In P. R. Kleindorfer (Ed.), The Management of Productivity and Technology in Manufacturing (pp. 153–188). New York: Plenum.

    Google Scholar 

  14. Cohen, M. A., & Lee, H. L. (1989). Resource deployment analysis of global manufacturing and distribution networks. Journal of Manufacturing and Operations Management, 2, 81–104.

    Google Scholar 

  15. Arntzen, B. C., Brown, G. G., Harrison, T. P., & Trafton, L. L. (1995). Global supply chain management at digital equipment corporation. Interfaces, 25, 69–93.

    Article  Google Scholar 

  16. Pirkul, H., & Jayaraman, V. (1996). Production, transportation, and distribution planning in a multi-commodity tri-echelon system. Transportation Sciences, 30(4), 291–302.

    Article  MATH  Google Scholar 

  17. Pirkul, H., & Jayaraman, V. (1998). A multi-commodity, multi-plant, capacitated facility location problem: formulation and efficient heuristic solution. Computers and Operations Research, 25(10), 869–878.

    Article  MathSciNet  MATH  Google Scholar 

  18. Pirkul, H., & Jayaraman, V. (2001). Planning and coordination of production and distribution facilities for multiple commodities. European Journal of Operational Research, 133, 394–408.

    Article  MATH  Google Scholar 

  19. Vila, D., Martel, A., & Beauregard, R. (2006). Designing logistics networks in divergent process industries: A methodology and its application to the lumber industry. International Journal of Production Economics, 102(2), 358–378.

    Article  Google Scholar 

  20. Martel, A. (2006). The design of production-distribution networks: A mathematical programming approach. In J. Geunes & P. M. Pardalos (Eds.), Supply chain optimization 98 (pp. 265–305). Springer series: applied optimization. Berlin: Springer

    Google Scholar 

  21. Snyder, L. V. (2006). Facility location under uncertainty: A review. IIE Transactions, 38(7), 547–564.

    Article  Google Scholar 

  22. Louveaux, F. V. (1986). Discrete stochastic location models. Annals of Operations Research, 6, 23–34.

    Article  Google Scholar 

  23. Ricciardi, N., Tadei, R., & Grosso, A. (2002). Optimal facility location with random throughput costs. Computers and Operations Research, 29, 593–607.

    Article  MathSciNet  MATH  Google Scholar 

  24. Erlebacher, S. J., & Meller, R. D. (2000). The interaction of location and inventory in designing distribution systems. IIE Transactions, 32, 155–166.

    Google Scholar 

  25. Daskin, M. S., Coullard, C. R., & Shen, Z.-J. M. (2002). An inventory-location model: formulation, solution algorithm and computational results. Annals of Operations Research, 110, 83–106.

    Article  MathSciNet  MATH  Google Scholar 

  26. Shen, Z.-J. M., Coullard, C. R., & Daskin, M. S. (2003). A joint location-inventory model. Transportation Science, 37(1), 40–55.

    Article  Google Scholar 

  27. Snyder, L. V. (2004). Supply chain robustness and reliability: Models and algorithms. Ph.D. Dissertation, Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA.

    Google Scholar 

  28. Shen, Z.-J. M. (2005). A multi-commodity supply chain design problem. IIE Transactions, 37, 753–762.

    Article  Google Scholar 

  29. Tanonkou, G. A., Benyoucef, L., & Xie, X. (2006), Integrated facility location and supplier selection decisions in a distribution network design. Proceedings of the 2nd IEEE International Conference on Service Operations and Logistics, and Informatics (pp. 399–404). June 21–23, Shanghai (China).

    Google Scholar 

  30. Tanonkou, G. A., Benyoucef, L., & Xie, X. (2007). Design of multi-commodity distribution network with random demands and supply lead-times. Proceedings of the 3rd IEEE International Conference on Automation Science and Engineering (pp. 698–703). September 22–25, Scottsdale, AZ, USA.

    Google Scholar 

  31. Tanonkou, G. A., Benyoucef, L., & Xie, X. (2008). Design of stochastic distribution networks using Lagrangian relaxation. IEEE Transactions on Automation Science and Engineering (TASE), 5(4), 597–608.

    Article  Google Scholar 

  32. França, P. M., & Luna, H. P. L. (1982). Solving stochastic transportation-location problems by generalized Benders decomposition. Transportation Science, 16(2), 113–126.

    Article  MathSciNet  Google Scholar 

  33. Moinzadeh, K., & Nahmias, S. (1988). A continuous review model for an inventory system with two supply modes. Management Science, 34, 761–773.

    Article  MathSciNet  MATH  Google Scholar 

  34. Sculli, D., & Shum, Y. W. (1990). Analysis of a continuous review stock-control model with multiple suppliers. Journal of Operational Research Society, 41, 873–877.

    Google Scholar 

  35. Ramasesh, R. V., Ord, J. K., Hayya, J. C., & Pan, A. (1991). Sole versus dual sourcing in stochastic lead-time (s, Q) inventory models. Management Science, 37, 428–443.

    Article  Google Scholar 

  36. Lau, H. S., & Zhao, L. G. (1994). Dual sourcing cost-optimization with unrestricted lead-time distributions and order-split proportions. IIE Transactions, 26, 66–75.

    Article  Google Scholar 

  37. Ganeshan, R., Boone, T., & Stenger, A. J. (2001). The impact of inventory and flow planning parameters on supply chain performance: An exploratory study. International Journal of Production Economics, 71, 111–118.

    Article  Google Scholar 

  38. Sedarage, D., Fujiwara, O., & Luong, H. T. (1999). Determining optimal order splitting and reorder level for N-supplier inventory systems. European Journal of Operational Research, 116, 389–404.

    Article  MATH  Google Scholar 

  39. Ghodyspour, S. H., & O’Brien, C. (2001). The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. International Journal of Production Economics, 73, 15–27.

    Article  Google Scholar 

  40. Qi, X. (2007). Order splitting with multiple capacitated suppliers. European Journal of Operational Research, 178, 421–432.

    Article  MATH  Google Scholar 

  41. Wang, G., Jiang, Z., Li, Z., & Liu, W. (2008). Supplier selection and order splitting in multiple-sourcing inventory systems. Frontiers of Mechanical Engineering in China, 3(1), 23–27.

    Article  Google Scholar 

  42. Ding, H., Benyoucef, L., & Xie, X. (2008). Simulation-based evolutionary multi-objective optimization approach for integrated decision-making in supplier selection. International Journal of Computer Applications in Technology, 31(3/4), 144–157.

    Article  Google Scholar 

  43. Slats, P. A., Bhola, B., Evers, J. J. M., & Dijkhuizen, G. (1995). Logistic chain modeling. European Journal of Operational Research, 87, 1–20.

    Article  MATH  Google Scholar 

  44. Beamon, B. M. (1998). Supply chain design and analysis: models and methods. International Journal of Production Economics, 55, 281–294.

    Article  Google Scholar 

  45. Sarmiento, A. M., & Nagi, R. (1999). A review of integrated analysis of production-distribution systems. IIE Transactions, 31, 1061–1074.

    Google Scholar 

  46. Azadivar, F. (1999). Simulation optimization methodologies. Proceedings of the 1999 Winter Simulation Conference, 1 (pp. 93–100).

    Google Scholar 

  47. Lacksonen, T. (2001). Empirical comparison of search algorithms for discrete event simulation. Computers and Industrial Engineering, 40(1/2), 133–148.

    Article  Google Scholar 

  48. Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithm for multiobjective optimization: Formulation, discussion and generalization. Proceedings of the 5th Internationa Confernce on Genetic Algorithms (pp. 416–423). Morgan Kaufmann: San Mateo, CA.

    Google Scholar 

  49. Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the 1st IEEE Internationa Conference on Evolutionary Computation (pp. 82–87). Piscataway, NJ.

    Google Scholar 

  50. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.

    Article  Google Scholar 

  51. Coello, C. A. C. (2000). An updated survey of ga-based multiobjective optimization techniques. ACM Computing Surveys, 32, 109–143.

    Article  Google Scholar 

  52. Goldberg, D. E. (1986). Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.

    Google Scholar 

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Author information

Authors and Affiliations

  1. INRIA, COSTEAM Project, ISGMP Bat. A, Ile Du Saulcy, 57000, Metz, France

    Lyes Benyoucef

  2. ENSM.SE, 158 Cours Fauriel, 42023, Saint-Etienne Cedex 2, France

    Xiaolan Xie

Authors
  1. Lyes Benyoucef
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  2. Xiaolan Xie
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Correspondence to Lyes Benyoucef .

Editor information

Editors and Affiliations

  1. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Lihui Wang

  2. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Amos H. C. Ng

  3. , Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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Benyoucef, L., Xie, X. (2011). Supply Chain Design Using Simulation-Based NSGA-II Approach. In: Wang, L., Ng, A., Deb, K. (eds) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-0-85729-652-8_17

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  • DOI: https://doi.org/10.1007/978-0-85729-652-8_17

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-617-7

  • Online ISBN: 978-0-85729-652-8

  • eBook Packages: EngineeringEngineering (R0)

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