^{}Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

In this study, an efficient logistics network was designed to optimize both time and cost as the most effective factors using a mathematical model (two-objective fuzzy optimization) in a reverse logistics system. This paper attempted to determine value of goods sent between return products processing centers in any time period, in such a way to minimize total cost and time of delay within supply chain. The fuzzy approach was adopted in order to consider uncertainty in reverse logistics network. The validity of model was measured through a model proposed by Azar Resin Co and then implemented and solved by GAMS software. According to previous studies and implementation of model at smaller scale, the problem revolved around designing NP-hard logistics network. Hence, exact methods cannot solve these problems on large scale, for which Cuckoo algorithm was considered. In order to validate the newly proposed algorithm, results were compared against the exact solution. The results suggested that the proposed Cuckoo algorithm was sufficiently accurate to solve the problem and achieve values similar to exact solution.

Altiparmak, F., Gen, M., Lin, L., and Paksoy, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering, Vol. 51(1). pp. 196-215.

Bagheri-Neghad Z., Kazemzadeh R., and Asadi R. (2013). Identifying and ranking of success factors in automotive reverse logistics through interpretive structural modeling (ISM). Journal of Management Research in Iran, Vol. 17(1), pp. 21-40.

Balin, S. (2011). Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation. Information Sciences, Vol. 181(17), pp. 3551-3569.

Behnamian, J., and Ghomi, S. M. T. F. (2014). Multi-objective fuzzy multiprocessor flowshop scheduling. Applied Soft Computing, Vol. 21(4), pp. 139-148.

Barros, A. I., Dekker, R., and Scholten, V. (1998). A two-level network for recycling sand: A case study. European Journal of Operational Research, Vol. 110(2), pp. 199-214.

Blackburn,J.,Guide,V.,Souza,G.,and Van Wassenhove,L.(2004).Reverse Supply chains for commercial Returns. California Management Review, Vol.46 (2), pp.6-22.

De Koster, R. (2002). How to organize return handling an exploratory study with nine retailer warehouses. International Journal of Retail and Distribution Management, Vol. 30(8). pp. 407-421.

Del Castillo, E., and Cochran, J.K. (1996).Optimal short horizon distribution operations in reusable container systems. Journal of the Operational Research Society, Vol. 47(1), pp. 48-60.

Cruz-Rivera, R., and Ertel, J. (2009). Reverse logistics network design for the collection of End-of-Life Vehicles in Mexico.European Journal of Operational Research, Vol. 196(3). pp. 930-939.

Chopra S. (2003). Designing the distribution network in a supply chain. Transportation Research Part E, Vol. 39(2), pp. 123–140.

Dat, L. Q., Truc Linh, D. T., Chou, S.-Y., and Yu, V. F. (2012).Optimizing reverse logistic costs for recycling end-of-life electrical and electronic products. Expert Systems with Applications, Vol. 39(7), pp. 6380-6387.

Dubois D, Nguyen HT, Prade H. (2000). Possibility theory, probability and fuzzy sets misunderstandings. Bridges and gaps. Fundamentals of fuzzy sets, Springer.pp. 343–438.

E. Ozceylan, T. Paksoy, T. Bektas.(2014). Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing. Transportation Research Part E: Logistics and Transportation Review, Vol.61 (3), pp.142–164.

M. Eskandarpour, E. Masehian, R. Soltani, A. Khosrojerdi.(2014). A reverse logistics network for recovery systems and a robust meta heuristic solution approach. International Journal Advanced Manufacturing Technology. Vol.74 (9), pp.1393–406.

Faizul, H., Stafford, F., Bhutta,S.M.Khurrum,K,S.(2010).Examination of the differential effects of transportation in supply An chain optimization modeling. Journal of Manufacturing Technology Management, Vol. 21(2), pp. 269-286.

Farahani, R.Z., and Elahipanah, M. (2008).A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. International Journal of Production Economics, Vol. 111(2), pp. 229-243.

Frota Neto, J. Q., Bloemhof-Ruwaard, J. M., van Nunen, J. A. E. E., and van Heck, E. (2008). Designing and evaluating sustainable logistics networks. International Journal of Production Economics, Vol. 111(2), pp. 195-208.

Fleischmann, M., J. M. Bloemhof-Ruwaard, R. Dekker, E. van der Laan, J. A. E. E. van Nunen, L. Van Wassenhove.(1997).Quantitative models for reverse logistics: A review. European Journal of Operational Research, Vol. 103(1), pp. 1-17.

Fleischmann, M., Krikke, H.R., Dekker, R., and Flapper, S. D. P. (2000). A characterization of logistics networks for product recovery. Omega, Vol. 28(6), pp. 653-666.

Fleischmann, M., Beullens, P., Bloemhof-Ruwaard, J. M., and Wassenhove, L. N. V. (2001). The impact of product recovery on logistics network design. Production and Operations Management, Vol. 10(2), pp. 156-173.

Govindan, K.(2015). Green sourcing: taking steps to achieve sustainability management and conservation of resources. Resources, Conservation and Recycling. Vol.104, part B, pp.329–333.

Jayaraman, V., Guide, V. D. R., and Srivastava, R. (1999). A closed-loop logistics model for remanufacturing. Journal of the Operational Research Society, Vol. 50(5), pp. 497-508.

Jayaraman, V., Patterson, R.A., and Rolland, E.(2003).The design of reverse distribution networks: Models and solution procedures. European Journal of Operational Research, Vol. 150(1), pp. 128-149.

Kerr, W., and Ryan, C. (2001). Eco-efficiency gains from remanufacturing: A case study of photocopier remanufacturing at Fuji Xerox Australia. Journal of Cleaner Production, Vol. 9(1), pp. 75-81.

Ko, H.J., and Evans, G.W. (2007). A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers & Operations Research, Vol. 34(2), pp. 346-366.

Krikke, H. R., van Harten, A., and Schuur, P.C. (1999). Business case Océ: Reverse logistic network re-design for copiers. OR Spectrum, Vol. 21(3), pp. 381-409.

Keskin B. B. and Uster H. (2007). Meta-heuristic approaches with memory and evolution for a multi-product production/distribution system design problem. European Journal of Operational Research, Vol. 182(2), pp. 663-682.

Kannan, G. (2009). Fuzzy approach for the selection of third party reverse logistics provider. Asia Pacific Journal of Marketing and Logistics, Vol. 21(3), pp. 397-416.

Kroon L., and Vrijens, G.(1995). Returnable containers: An example of reverse logistics. International Journal of Physical Distribution and Logistics Management, Vol. 25(2), pp.56-68.

Kumar, S., and Putnam,V.(2008).Cradle to cradle: reverse logistics strategies and opportunities across three industry sectors. International Journal of Production Economics, Vol.115 (2), pp.305-315.

Lee, J.-E., Gen, M., and Rhee, K.-G. (2009). Network model and optimization of reverse logistics by hybrid genetic algorithm. Computers & Industrial Engineering, Vol. 56(3), pp. 951-964.

Liao,Tsai,Y.(2018). Reverse Logistics Network Design for Product Recovery and Remanufacturing, Applied Mathematical Modelling. Article in press, doi: 10.1016/j.apm.2018.03.003

Liu, Z. F., Liu, X.P., Wang, S.W., and Liu, G. F. (2002). Recycling strategy and a recyclability assessment model based on an artificial neural network. Journal of Materials Processing Technology, Vol. 129(3), pp. 500-506.

Louwers, D., Kip, B. J., Peters, E., Souren, F., and Flapper, S. D. P. (1999).A facility location allocation model for reusing carpet materials. Computers & Industrial Engineering, Vol. 36(4), pp. 855-869.

Meade L., Sarkis J., and Presley A. (2007). The theory and practice of reverse logistics. International Journal of Logistics Systems and Management, Vol. 3(1), pp. 1742-7975.

Min, H.(1989).A bicriterion reverse distribution model for product recall. Omega, Vol. 17(5), pp. 483-490.

Min, H., Ko, H.J., and Park, B.-I. (2005). A Lagrangian relaxation heuristic for solving the multi-echelon, multi-commodity, close-loop supply chain network design problem. International Journal of Logistics Systems and Management, Vol. 1(4), pp. 382-404.

Murphy, P. (1986). A preliminary study of transportation and warehousing aspects of reverse distribution. Transportation Journal, Vol. 25(4), pp. 12-21.

Pati, R. K., Vrat, P., and Kumar, P. (2008).A goal programming model for paper recycling system. Omega, Vol. 36(3), pp. 405-417.

Pishvaee M. S., and Torabi S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Set and Systems, Vol. 161(20), pp. 2668-2683.

Pishvaee, M. S., Farahani, R.Z., and Dullaert, W. (2010). A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers & Operations Research, Vol. 37(6), pp. 1100-1112.

Prahinski, C. and Kocabasoglu, C.(2006). Empirical research opportunities in reverse supply chain. OMEGA: The International Journal of Management Science, Vol.34 (6), pp.519–32.

Rajabioun, R. (2011).Cuckoo optimization algorithm. Applied Soft Computing, Vol. 11(8), pp. 5508-5518.

Üster, H., Easwaran, G., Akçali, E., and Çetinkaya, S. (2007). Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model. Naval Research Logistics, Vol. 54(8), pp. 890-907.

Xin-she Yang , Suash Deb.(2009). Cuckoo search via Lévy flights. World Congress on Nature & Biologically Inspired Computing. IEEE Publications, pp. 210–214.