Skip to main content

Advertisement

Log in

Supply chain optimization in context of production flow network

  • Published:
Journal of Systems Science and Systems Engineering Aims and scope Submit manuscript

Abstract

For large multinational companies, the complex production process of their finished goods usually contains plenty of stages, which constitute a production flow network. Each production stage in the production flow network can be undertaken by one or more suppliers. This study proposes a stochastic programming model for the production flow network oriented supply chain network design problem, which optimizes the decision of allocating stages to suppliers with the objective of minimizing the total expected costs of production and transportation among suppliers under uncertain demands of customers. A local branching based solution method is developed to solve the model. A case study on applying this model to a large automobile company is performed. In addition, some numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Ahmadi-Javid, A. & Hoseinpour, P. (2015). Incorporating location, inventory and price decisions into a supply chain distribution network design problem. Computers & Operations Research, 56: 110–119.

    Article  MathSciNet  Google Scholar 

  2. Akeb, H., Hifi, M. & Mounir, M. E. O. A. (2011). Local branching-based algorithms for the disjunctively constrained knapsack problem. Computers & Industrial Engineering, 60: 811–820.

    Article  Google Scholar 

  3. Altiparmak, F., Gen, M., Lin, L. & Karaoglan, I. (2009). A steady-state genetic algorithm for multi-product supply chain network design. Computers & Industrial Engineering, 56(2): 521–537.

    Article  Google Scholar 

  4. Asian, S. & Nie, X. (2014). Coordination in supply chains with uncertain demand and disruption risks: existence, analysis, and insights. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(9): 1139–1154.

    Article  Google Scholar 

  5. Baghalian, A., Rezapour, S. & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. European Journal of Operational Research, 227(1): 199–215.

    Article  MathSciNet  MATH  Google Scholar 

  6. Costa, A., Celano, G., Fichera, S. & Trovato, E.(2010). A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms. Computers & Industrial Engineering, 59(4): 986–999.

    Article  Google Scholar 

  7. Danna, E., Rothberg, E. & Pape, C. Le. (2005). Exploring relaxation induced neighborhoods to improve MIP solutions. Mathematical Programming, 102: 71–90.

    Article  MathSciNet  MATH  Google Scholar 

  8. Dotoli, M., Fanti, M. P., Meloni, C. & Zhou, M. (2006). Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 36(1): 62–75.

    Article  Google Scholar 

  9. Eskigun, E., Uzsoy, R., Preckel, P. V., Beaujon, G., Krishnan, S. & Tew, J. D. (2005). Outbound supply chain network design with mode selection, lead times and capacitated vehicle distribution centers. European Journal of Operational Research, 165(1): 182–206.

    Article  MathSciNet  MATH  Google Scholar 

  10. Fischetti, M. & Lodi, A. (2003). Local branching. Mathematical Programming, 98: 23–47.

    Article  MathSciNet  MATH  Google Scholar 

  11. Gedik, R., Medal, H., Rainwater, C., Pohl, E. A. & Mason, S. J. (2014). Vulnerability assessment and re-routing of freight trains under disruptions: a coal supply chain network application. Transportation Research, Part E, 71: 45–57.

    Article  Google Scholar 

  12. Georgiadis, M. C., Tsiakis, P., Longinidis, P. & Sofioglou, M. K. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega, 39(3): 254–272.

    Article  Google Scholar 

  13. Govindan, K., Jafarian, A., Khodaverdi, R. & Devika, K. (2014). Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152: 9–28.

    Article  Google Scholar 

  14. Hasani, A., Zegordi, S. H. & Nikbakhsh, E. (2015). Robust closed-loop global supply chain network design under uncertainty: the case of the medical device industry. International Journal of Production Research, 53(5): 1596–1624.

    Article  Google Scholar 

  15. Klibi, W. & Martel, A. (2012). Scenario-based supply chain network risk modeling. European Journal of Operational Research, 223(3): 644–658.

    Article  Google Scholar 

  16. Lin, C.-C. & Wang, T.-H. (2011). Build-to-order supply chain network design under supply and demand uncertainties. Transportation Research, Part B, 45(8): 1162–1176.

    Article  Google Scholar 

  17. Mahnam, M., Yadollahpour, M. R., Famil-Dardashti, V & Hejazi, S. R. (2009). Supply chain modeling in uncertain environment with bi-objective approach. Computers & Industrial Engineering, 56(4): 1535–1544.

    Article  Google Scholar 

  18. Marufuzzaman, M., Eksioglu, S. D. & Huang, Y. (2014). Two-stage stochastic programming supply chain model for biodiesel production via wastewater treatment. Computers & Operations Research, 49: 1–17.

    Article  MathSciNet  Google Scholar 

  19. 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 & Operations Research, 33(1): 181–208.

    Article  MATH  Google Scholar 

  20. Nagurney, A., Dong, J. & Zhang, D.(2002). A supply chain network equilibrium model. Transportation Research, Part E, 38(5): 281–303.

    Article  Google Scholar 

  21. Nagurney, A. & Matsypura, D. (2005). Global supply chain network dynamics with multicriteria decision-making under risk and uncertainty. Transportation Research, Part B, 41(6): 585–612.

    Article  Google Scholar 

  22. Nagurney, A., Yu, M., Floden, J. & Nagurney, L. S. (2014). Supply chain network competition in time-sensitive markets. Transportation Research, Part E, 70: 112–127.

    Article  Google Scholar 

  23. Nickel, S., Saldanha-da-Gama, F. & Ziegler, H.-P. (2012). A multi-stage stochastic supply network design problem with financial decisions and risk management. Omega, 40(5): 511–524.

    Article  Google Scholar 

  24. Park, S., Lee, T.-E. & Sung, C. S. (2010). A three-level supply chain network design model with risk-pooling and lead times. Transportation Research, Part E, 46(5): 563–581.

    Article  Google Scholar 

  25. Peidro, D., Mula, J., Jiménez, M. & Botella, M. D. M. (2010). A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. European Journal of Operational Research, 205(1): 65–80.

    Article  MATH  Google Scholar 

  26. Sadjady, H. & Davoudpour, H. (2012). Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs. Computers & Operations Research, 39(7): 1345–1354.

    Article  MathSciNet  MATH  Google Scholar 

  27. Santoso, T., Ahmed, S., Goetschalckx, M. & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167: 96–115.

    Article  MathSciNet  MATH  Google Scholar 

  28. Sniedovich, M. & Voß, S. (2006). The corridor method: a dynamic programming inspired metaheuristic. Control and Cybernetics, 35: 551–578.

    MathSciNet  MATH  Google Scholar 

  29. Taillard, E. & Voß, S. (2002). POPMUSIC-A partial optimization metaheuristic under special intensification conditions. In C.C. Ribeiro and P. Hansen (eds), Essays and Surveys in Metaheuristics, pp. 613–629. Kluwer, Boston.

    Chapter  Google Scholar 

  30. Tiwari, A., Chang, P.C. & Tiwari, M.K. (2012). A highly optimised tolerance-based approach for multi-stage, multi-product supply chain network design. International Journal of Production Research, 50(19): 5430–5444.

    Article  Google Scholar 

  31. Wang, S. & Meng, Q. (2014). Liner shipping network design with deadlines. Computers & Operations Research, 41: 140–149.

    Article  MathSciNet  Google Scholar 

  32. Wang, S., Liu, Z. & Bell, M. G. H.(2015). Profit-based maritime container assignment models for liner shipping networks. Transportation Research, Part B, 72: 59–76.

    Article  Google Scholar 

  33. Wu, Y. (2010). A time staged linear programming model for production loading problems with import quota limit in a global supply chain. Computers & Industrial Engineering, 59(4): 520–529.

    Article  Google Scholar 

  34. Wu, Y., Dong, M., Fan, T. & Liu, S. (2012). Performance evaluation of supply chain networks with assembly structure under system disruptions. Computers & Operations Research, 39(12): 3229–3243.

    Article  MathSciNet  Google Scholar 

  35. Zhang, G., Shang, J. & Li, W. (2011). Collaborative production planning of supply chain under price and demand uncertainty. European Journal of Operational Research, 215(3): 590–603.

    Article  MathSciNet  MATH  Google Scholar 

  36. Zhen, L. & Wang, K. (2015). A stochastic programming model for multi-product oriented multi-channel component replenishment. Computers & Operations Research, 60: 79–90.

    Article  MathSciNet  Google Scholar 

  37. Zhen, L. & Wang, K., Liu, H. (2015). Disaster relief facility network design in metropolises. IEEE Transactions on Systems Man and Cybernetics: Systems, 45: 751–761.

    Article  Google Scholar 

  38. Zhen, L. (2015). Tactical berth allocation under uncertainty. European Journal of Operational Research, 247:928–944.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lu Zhen.

Additional information

Lu Zhen is a professor in School of Management, Shanghai University, Shanghai, China. He obtained both the B.E. and the Ph.D. degrees from Shanghai Jiao Tong University. His research interests focus on logistics and supply chain management. He has published over thirty papers on international journals such as Transportation Science, European Journal of Operational Research.

Dan Zhuge is a graduate student in School of Management, Shanghai University, Shanghai, China. Her major is management science and engineering, and her research interest focuses on logistics and supply chain management.

Jingqi Lei is a graduate student in School of Management, Shanghai University, Shanghai, China. Her major is logistic engineering, and her research interest focuses on logistics and supply chain management.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhen, L., Zhuge, D. & Lei, J. Supply chain optimization in context of production flow network. J. Syst. Sci. Syst. Eng. 25, 351–369 (2016). https://doi.org/10.1007/s11518-016-5304-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11518-016-5304-6

Keywords

Navigation