A green closed loop supply chain network design considering operational risks under uncertainty and solving the model with NSGA II algorithm

Document Type: Research Paper


School of Industrial Engineering, College of Engineering, University of Tehran, I.R. Iran


In today’s growing competitive environment, supply chain management has been widely focused by firms as a critical issue. Consequently, the organization’s activities are influenced in order to achieving quality improvement, cost reduction and customer satisfaction in production process. Recently, pollution and greenhouse effects have focused the researchers’ attention on planning and executing networks in which environmental problems and economical aspects are considered simultaneously. In this paper, a multi-layer, multi-product and multi-period supply chain network with product return will be studied. The operation risks are assumed as failures occurring in supplier and plant segments. The mathematical modeling will select the best suppliers according to selling price, transportation costs and the average of deficiency. Uncertainty is considered by means of fuzzy approach. The proposed multi-objective fuzzy model is first defuzzified using Jimenez technique and then it is solved by TH method. As the supply chain problems are determined as NP-Hard problems, in this study we took advantage of NSGA II in a large-scale case.


Main Subjects

  1. Daskin, M. S. (1995). “Network and Discrete Location: Models, Algorithms, and Applications.” Wiley, New York.
  2. Melo, M. T., Nickel, S., and Gama, F. S. (2009). “Facility location and supply chain management.” European Journal of Operation Research, Vol. 196, No. 2, 401-412.
  3. Melkote, S. and Daskin, M. S. (2001). “Capacitated facility location/network design problem.” European Journal of Operation Research, Vol. 129, No. 3, 481-495.
  4. Drezner, Z. and wesolowsky, G. O. (2003). “Network design: selection and design of links and facility location.”, Transportation Research, Vol. 37, No. 3, 241-256.
  5. Ambrosino, D. and Scutella, M. G. (2005). “Distribution Network Design: New Problems and Related Models.”, European Journal of Operational Research, Vol. 165, No. 3, 610-624.
  6. Nga Thanh, P., Bostel, N., and Peton, O. (2008). “A dynamic model for facility location in the design of Complex supply chains.”,International Journal of Production Economics, Vol. 113, No. 2, 678-693.
  7. Lu, Z. and Bostel, N. (2007). “A facility location model for logistics systems including reverse flows: the case of remanufacturing activities.” Computers & Operations Research, Vol. 34, No. 2, 299–323.
  8. Pishvaee, M. S. and Shakouri, H., (2009). “A System Dynamics Approach for Capacity Planning and Price Adjustment in a Closed-Loop Supply Chain.”, EMS: 435-439.
  9. Pishvaee, M. S., Rabbani, M., and Torabi, S. A. (2011). “A robust optimization approach to closed-loop supply chain network design under uncertainty.”, Applied Mathematical Modeling, Vol. 35, No. 2, 637-649.
10. Sayed, M., Afia, N., and Kharbotly, A. (2010). “A stochastic model for forward–reverse logistics network design under risk.” Computers & Industrial Engineering, Vol. 58, No. 3, 423-431.

11. Tarokh, M. J. and Naseri, A. (2012). “Genetic Algorithm and Hybrid Method to Minimize Total Distribution Cost in Multi-level Supply Chain.” Journal of Industrial Engineering, Vol. 46, No. 1, 15-26.

12. Aghaei, A. and Zandi, F. (2012). “Analyzing and Optimization a Two Echelon Supply Chain with Uncertainly Returned Product.” Journal of Industrial Engineering, Vol. 46, No. 2, 119-132.

13. Pourrousta, A., Tavakkoli Moghaddam, R., and Ebrahimnejad, S. (2012). “A Multi-product and Multi-period Model for a Procurement-production-distribution in Supply Chain with Fuzzy Parameters” Journal of Industrial Engineering, Vol. 46, No. 2, 147-156.

14. Tarokh, M. J., EsmaeiliGookeh, M., and Torabi, Sh. (2012). “A Model to Optimize the Design of a Reverse Logistic Network under Uncertainty” Journal of Industrial Engineering, Vol. 46, No. 2, 159-173.

15. Pishvaee, M. S. and Razmi, J. (2012). “Environmental supply chain network design using multi-objective fuzzy mathematical programming.” Applied Mathematical Modelling, Vol. 36, No. 8, 3433-3446.

16. Pishvaee, M. S., Razmi, J., and Torabi, S. A., (2012). “Robust possibilistic programming for socially responsible supply chain network design: A new approach.” Fuzzy Sets and Systems, Vol. 206, 1-20.

17. Pishvaee, M. S., Torabi, S. A., and Razmi, J. (2012). “Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty.” Computers & Industrial Engineering, Vol. 62, No. 2, 624-632.

18. Wang, R. -C. and Liang, T. -F. (2005). “Applying possibilistic linear programming to aggregate production planning.” International Journal of Production ECOnomics, Vol.98, No. 3, 328-341.

19. Arenas Parra, M, Bilbao Terol, A., Pérez Gladish, B., and Rodrı́guez Urı́a, M. V. (2005). “Solving a multiobjective possibilistic problem through Compromise programming.” European Journal of Operational Research, Vol. 164, No. 3, 748-759.

20. Yager, R. R., (1981). “A procedure for ordering fuzzy subsets of the unit interval.” Information Sciences, Vol. 24, No. 2, 143-161.

21. Torabi, S. A. and Hassini, E. (2008). “An interactive possibilistic programming approach for multiple objective supply chain master planning.” Fuzzy Sets and Systems, Vol. 159, No. 2, 193-214.

22. Jiménez, M., Arenas, M., Bilbao, A., and Rodriguez, M. V. (2007). “Linear programming with fuzzy parameters, An interactive method resolution” European Journal of Operational Research, Vol. 177 , 1599–1609.

23. Pishvaee, M. S. and Torabi, S. A. (2010). “A possibilistic programming approach for closed-loop supply chain network design under uncertainty.” Fuzzy Sets and Systems, Vol. 161, No. 20, 2668-2683.