Design and Optimization of Inverse Logistic Network under Uncertainty Using Genetic Algorithm

Document Type : Original Article

Authors

1 Professor, Shahid Beheshti University.

2 Assistant Professor, K. N. Toosi University of Technology.

3 M.A., Islamic Azad University of Arak.

Abstract

In the supply chain, return management is applied in reverse logistics. For various reasons, the flow of materials in the opposite direction of the chain is inevitable. It is essential to deal with the issue of reverse logistics network and efficient management and guidance. According to the reviews, one of the issues that has a great impact on reverse logistics network modeling is the uncertainty situation. In reverse logistics, parameters such as capacity of centers, demand, cost and quality, etc. are uncertain. In this regard, in this study, a possible mixed integer linear programming model for reverse logistics network design is presented. To solve this type of model, it must first become a definitive model. The model presented in this research is multi-product and multi-category that includes transportation costs and facility construction at the same time. The model is NP-Hard in terms of cost minimization (NP-Hard) in terms of cost minimization (facility deployment costs and shipping costs) and uncertainty in demand for return products. Exponentialism increases with respect to the dimensions of the problem. Therefore, in this study, an efficient method using Genetic Algorithm with priority-axis coding is proposed.

Keywords

References

1. 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, 521–537.
2. Altiparmak,F., Gen,M., Lin,L., Paksoy,T.( 2006), “A genetic algorithm for multi-objective optimization of supply chain networks”, Computers & Industrial Engineering, 51, 197–216.
3. Barros,A,I., Dekker,R., Scholten,V.( 1998), “ A two-level network for recycling sand: a case study”, European Journal of Operational Research,110, 199–214.
4. Frota Neto, j,Q., Bloemhof,j., van Nunen A,A,E.,van Heck, E.( 2008), “Designing and evaluating sustainable logistics network”, International journal of production Economics, 111, 195-208.
5. Gen,M., Altiparmak,F., Lin,L.( 2006), “A genetic algorithm for two-stage transportation problem using priority-based encoding”, OR Spectrum,28,(3) ,337–354.
6. Gen,M., Cheng, R.( 2000), “Genetic algorithms and engineering optimization”, New York, John Wiley and Sons.
7. Jayaraman,V., Patterson,R,A., Rolland ,E.( 2003), “The design of reverse distribution networks: Models and solution procedures”, European Journal of Operational Research, 150, 128–149.
8. Jayaraman,V.,Guide,V,D,R,J., Srivastava,R.( 1999), “A closed loop logistics model for remanufacturing”, Journal of the Operational Research Society ,50, (5), 497–508.
10. Kim,K., Song,I., Kim,J., Jeong,B.( 2006), “Supply planning model for remanufacturing system in reverse logistics environment”, Computers & Industrial Engineering, 51, (2), 279–287.
11. Kirkke, H,R., Harten,A,V., Schuur ,P,C.( 1999), “Business case Oce: reverse logistic network redesign for copiers”, OR Spectrum , 21, 381–409.
12. Kroon,L., Vrijens,G.( 1995), “Returnable containers: An example of reverse logistics, International Journal of Physical Distribution & Logistics Management”,25,(2), 56–68.
13. lee,J., Gen,M., Rhee,k. (2009), “Network model and optimization of reverse logistics by hybrid genetic algorithm”, computers & Industrial Engineering , 56, 951-946.
14. Lu,Z., Bostel,N.(2007) , “A facility location model for logistics systems including reverse flows: The case of remanufacturing activities”, Computers & Operations Research, 34, 299–323.
15. Michalewicz, Z., Vignaux ,G,A., Hobbs,M.( 1991), “A non-standard genetic algorithm for the nonlinear transportation problem”, ORSA Journal on Computing, 3, (4), 307–316.
16. Min, H., Ko,H,J., Park,B,I.( 2005), “A lagrangian relaxation heuristic for solving the multi-echelon, multi-commodity, closed-loop supply chain network design problem”, International Journal of Logistics Systems and Management, 1, (4), 382–404.
17. Min,H., Ko,H,J., Ko,C,S.( 2006), “A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns”, Omega, 34, 56 – 69.
18. Pati, R, K., Vrat,P., Kumar,P.( 2008), “A goal programming model for paper recycling system”, The International Journal of Management Science, Omega, 36,(3), 405–417.
19. Pishvaee, M ,S., Zanjirani, Farahani, R., Dullaert, W. (2010),”A memetic algorithm for bi-objective integrated forward/reverse logistics network design”, Computers & Operations Research, 37, 1100–1112.
20. Syarif,A.,Yun,Y,S.,Gen.M.(2002), “study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach”, Computers & Industrial Engineering, 43, 299-314.
21. Uster,H., Easwaran,G., Elif Akcali,E., Sila Cetinkaya,S.( 2007), “benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model”, Naval Research Logistics, 54, 890-907.
22. Xu,J., Liu,Q., Wang,R.( 2008), “A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor, Information Sciences”, 178, 2022–2043.

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