DOI QR코드

DOI QR Code

Optimizing Bi-Objective Multi-Echelon Multi-Product Supply Chain Network Design Using New Pareto-Based Approaches

  • Jafari, Hamid Reza (Department of Industrial Engineering, Science and Research branch, Islamic Azad University) ;
  • Seifbarghy, Mehdi (Department of Industrial Engineering, Alzahra University)
  • Received : 2016.11.05
  • Accepted : 2016.11.13
  • Published : 2016.12.30

Abstract

The efficiency of a supply chain can be extremely affected by its design which includes determining the flow pattern of material from suppliers to costumers, selecting the suppliers, and defining the opened facilities in network. In this paper, a multi-objective multi-echelon multi-product supply chain design model is proposed in which several suppliers, several manufacturers, several distribution centers as different stages of supply chain cooperate with each other to satisfy various costumers' demands. The multi-objectives of this model which considered simultaneously are 1-minimize the total cost of supply chain including production cost, transportation cost, shortage cost, and costs of opening a facility, 2-minimize the transportation time from suppliers to costumers, and 3-maximize the service level of the system by minimizing the maximum level of shortages. To configure this model a graph theoretic approach is used by considering channels among each two facilities as links and each facility as the nodes in this configuration. Based on complexity of the proposed model a multi-objective Pareto-based vibration damping optimization (VDO) algorithm is applied to solve the model and finally non-dominated sorting genetic algorithm (NSGA-II) is also applied to evaluate the performance of MOVDO. The results indicated the effectiveness of the proposed MOVDO to solve the model.

Keywords

References

  1. Altiparmak, F., Gen, M., Lin, L., and Paksoy, T. (2006), A genetic algorithm approach for multi-objective optimization of supply chain networks, Computers and Industrial Engineering, 51, 196-215. https://doi.org/10.1016/j.cie.2006.07.011
  2. Cakravastia, A., Toha, I., and Nakamura, N. (2002), A two-stage model for the design chain networks, International Journal of Production Economics, 80, 231-248. https://doi.org/10.1016/S0925-5273(02)00260-8
  3. Chen, C., Wang, B., and Lee, W. (2008), Multi-objective optimization for a multi-enterprise supply chain network, Industrial and Engineering Chemistry Research, 42(6/7), 1879-1889.
  4. Chopra, S. and Meindl, P. (2004), Supply Chain Management: Strategy, Planning and Operation, Prentice Hall, Upper Saddle River, USA.
  5. Coello, C. A., Lamont G. B., and Van Veldhuizen, D. A. (2007), Evolutionary algorithms for solving multiobjective problems, 2nd ed., Springer, Berlin.
  6. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002), A fast and elitist multi objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 182-197. https://doi.org/10.1109/4235.996017
  7. Elhedhli, S. and Gzara, F. (2008), Integrated design of supply chain networks with three echelons, multiple commodities and technology selection, IIE Transactions, 40(1), 31-44. https://doi.org/10.1080/07408170701246641
  8. Guille'n, G., Mele, F., Bagajewicz, M., Espuna, A., and Puigjaner, L. (2005), Multi objective supply chain design under uncertainty, Chemical Engineering Science, 60, 1535-1553. https://doi.org/10.1016/j.ces.2004.10.023
  9. Gumus, A. T., Guneri, A. F., and Keles, S. (2009), Supply chain network design using an integrated neuro- fuzzy and MILP approach: A comparative design study, Expert Systems with Applications, 36, 12570-12577. https://doi.org/10.1016/j.eswa.2009.05.034
  10. Hajipour, V., Mehdizadeh, E., Tavakkoli-Moghaddam, R. (2013), A novel Pareto-based Multi-objective Vibration Damping Optimization Algorithm to Solve Multi-objective Optimization Problems, Published Online ScientiaIranica, Transaction E, http://www.scientiairanica.com/en/ManuscriptDetail?mid=228.
  11. MATLAB Version 7.10.0.499 (R2010a) (2010), The Math Works, Inc. Protected by U.S. and international patents.
  12. Mehdizadeh, E. and Tavakkoli-Moghaddam, R. (2009), Vibration damping optimization algorithm for an identical parallel machine scheduling problem, Conference of Iranian Operations Research Society, Babolsar, Iran.
  13. Moncayo-Martinez, L. A. and Zhang, D. Z. (2011), Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design, International Journal of Production Economics, 131, 407-420. https://doi.org/10.1016/j.ijpe.2010.11.026
  14. Mousavi, S. M., Niaki, S. T. A., Mehdizadeh, E., and Tavarroth, M. R. (2013), The capacitated multi-facility location-allocation problem with probabilistic customer location and demand: two hybrid meta-heuristic algorithms, International Journal of Systems Science, 44(10), 1897-1912. https://doi.org/10.1080/00207721.2012.670301
  15. Sabri, E. and Beamon, B. (2000), A multi-objective approach to simultaneous strategic and operational planning in supply chain design, The International Journal of Management Science, 28(5), 581-598.
  16. Santoso, T., Ahmed, S., Goetschalckx, M., and Shapiro, A. (2005), A stochastic programming approach for supply chain network design under uncertainty, European Journal of Operational Research, 167, 96-115. https://doi.org/10.1016/j.ejor.2004.01.046
  17. Simchi-Levi, D., Kaminsky, P., and Simchi-Levi, E. (2003), Designing and managing the supply chain: concepts, strategies and case studies, 2nd ed. New York, NY: McGraw-Hill.
  18. Srinivas, N. and Deb, K. (1995), Multi objective function optimization using non dominated sorting genetic algorithms, Evolutionary Computation, 2(3), 221-248. https://doi.org/10.1162/evco.1994.2.3.221
  19. Syam, S. S. (2002), A model and methodologies for the location problem with logistical components, Computers and Operations Research, 29, 1173-1193. https://doi.org/10.1016/S0305-0548(01)00023-5
  20. Syarif, A., Yun, Y., and Gen, M. (2002), Study on multi-stage logistics chain network: A spanning tree-based genetic algorithm approach, Computers and Industrial Engineering, 43, 299-314. https://doi.org/10.1016/S0360-8352(02)00076-1
  21. Jayaraman, V. and Ross, A. (2003), A simulated annealing methodology to distribution network design and management, European Journal of Operational Research, 144, 629-645. https://doi.org/10.1016/S0377-2217(02)00153-4
  22. Yeh, W. C. (2005), A hybrid heuristic algorithm for multistage supply chain network problem, International Journal of Advance Manufacturing Technology, 26(5/6), 675-685. https://doi.org/10.1007/s00170-003-2025-z
  23. Yeh, W. C. (2006), A efficient memetic algorithm for multi-stage supply chain network problem, International Journal of Advance Manufacturing Technology, 29(7/8), 803-813. https://doi.org/10.1007/s00170-005-2556-6
  24. Tsiakis, P., Shah, N., and Pantelides, C. (2001), Design of multi-echelon supply chain networks under demand uncertainty, Industrial and Engineering Chemistry Research, 40(16), 3585-3604. https://doi.org/10.1021/ie0100030
  25. Zitzler, E. and Thiele, T. (1998), Multi objective Optimization Using Evolutionary Algorithms-A Comparative Case Study, Conference on Parallel Problem Solving from Nature, Amsterdam, 292-301.

Cited by

  1. A distribution free newsvendor model with consignment policy and retailer’s royalty reduction vol.56, pp.15, 2018, https://doi.org/10.1080/00207543.2017.1399220