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Reverse Logistics Network Design with Incentive-Dependent Return
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 Title & Authors
Reverse Logistics Network Design with Incentive-Dependent Return
Asghari, Mohammad; Abrishami, Salman J.; Mahdavi, Faezeh;
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 Abstract
Reverse logistics network design issues have been popularly discussed in recent years. However, few papers in the past literature have been dedicated to incentive effect on return quantity of used products. The purpose of this study is to formulate a dynamic nonlinear programming model of reverse logistics network design with the aim of managing the used products allocation by coordinating the collection centers and recovery facilities to warrant economic efficiency. In the optimization model, a fuzzy approach is applied to interpret the relationship between the rate of return and the suggested incentives. Due to funding constraints in setting up the collection centers, this work considers these centers as multi-capacity levels, which can be opened or closed at different periods. In view of the fact that the problem is known as NP-hard, we propose a heuristic method based on tabu search procedure to solve the presented model. Finally, several dominance properties of optimal solutions are demonstrated in comparison with the results of a state-of-the-art commercial solver.
 Keywords
Reverse Logistics;Incentive-Dependent Return;Nonlinear Programming;Network Optimization;Heuristic Algorithm;
 Language
English
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Two-echelon supply chain model with manufacturing quality improvement and setup cost reduction, Journal of Industrial and Management Optimization, 2016, 13, 2, 63  crossref(new windwow)
 References
1.
Abdallah, T., Farhat, A., Diabat, A., and Kennedy, S. (2012), Green supply chains with carbon trading and environmental sourcing: formulation and life cycle assessment, Applied Mathematical Modelling, 36(9), 4271-4285. crossref(new window)

2.
Aksen, D., Aras, N., and Karaarslan, A. G. (2009), Design and analysis of government subsidized collection systems for incentive-dependent returns, International Journal of Production Economics, 119(2), 308-327. crossref(new window)

3.
Amin, S. H. and Zhang, G. (2012), An integrated model for closed-loop supply chain configuration and supplier selection: multi-objective approach, Expert Systems with Applications, 39(8), 6782-6791. crossref(new window)

4.
Amiri, A. (2006), Designing a distribution network in a supply chain system: formulation and efficient solution procedure, European Journal of Operational Research, 171(2), 567-576. crossref(new window)

5.
Aras, N. and Aksen, D. (2008), Locating collection centers for distance- and incentive-dependent returns, International Journal of Production Economics, 111(2), 316-333. crossref(new window)

6.
Cardoso, S. R., Barbosa-Povoa, A. P. F., and Relvas, S. (2013), Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty, European Journal of Operational Research, 226(3), 436-451. crossref(new window)

7.
Chaabane, A., Ramudhin, A., and Paquet, M. (2012), Design of sustainable supply chains under the emission trading scheme, International Journal of Production Economics, 135(1), 37-49. crossref(new window)

8.
Choi, T. M., Li, Y., and Xu, L. (2013), Channel leadership, performance and coordination in closed loop supply chains, International Journal of Production Economics, 146(1), 371-380. crossref(new window)

9.
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, 196(3), 930-939. crossref(new window)

10.
Das, K. and Chowdhury, A. H. (2012), Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning, International Journal of Production Economics, 135(1), 209-221. crossref(new window)

11.
de Figueiredo, J. N. and Mayerle, S. F. (2008), Designing minimum-cost recycling collection networks with required throughput, Transportation Research Part E: Logistics and Transportation Review, 44(5), 731-752. crossref(new window)

12.
Diabat, A., Kannan, D., Kaliyan, M., and Svetinovic, D. (2013), An optimization model for product returns using genetic algorithms and artificial immune system, Resources, Conservation and Recycling, 74, 156-169. crossref(new window)

13.
El-Sayed, M., Afia, N., and El-Kharbotly, A. (2010), A stochastic model for forward-reverse logistics network design under risk, Computers and Industrial Engineering, 58(3), 423-431. crossref(new window)

14.
Eskandarpour, M., Nikbakhsh, E., and Zegordi, S. H. (2013a), Variable neighborhood search for the biobjective post-sales network design problem: a fitness landscape analysis approach, Computers and Operations Research, 52B, 300-314.

15.
Eskandarpour, M., Zegordi, S. H., and Nikbakhsh, E. (2013b), A parallel variable neighborhood search for the multi-objective sustainable post-sales network design problem, International Journal of Production Economics, 145(1), 117-131. crossref(new window)

16.
Fleischmann, M., Krikke, H. R., Dekker, R., and Flapper, S. D. P. (2000), A characterisation of logistics networks for product recovery, Omega, 28(6), 653-666. crossref(new window)

17.
Govindan, K., Palaniappan, M., Zhu, Q., and Kannan, D. (2012), Analysis of third party reverse logistics provider using interpretive structural modeling, International Journal of Production Economics, 140(1), 204-211. crossref(new window)

18.
Guide Jr, V. D. R., Teunter, R. H., and Van Wassenhove, L. N. (2003), Matching demand and supply to maximize profits from remanufacturing, Manufacturing and Service Operations Management, 5(4), 303-316. crossref(new window)

19.
Hatefi, S. M. and Jolai, F. (2014), Robust and reliable forward-reverse logistics network design under demand uncertainty and facility disruptions, Applied Mathematical Modelling, 38(9), 2630-2647. crossref(new window)

20.
Ilgin, M. A. and Gupta, S. M. (2010), Environmentally conscious manufacturing and product recovery (EC MPRO): a review of the state of the art, Journal of Environmental Management, 91(3), 563-591. crossref(new window)

21.
Jayaraman, V., Patterson, R. A., and Rolland, E. (2003), The design of reverse distribution networks: models and solution procedures, European Journal of Operational Research, 150(1), 128-149. crossref(new window)

22.
Keyvanshokooh, E., Fattahi, M., Seyed-Hosseini, S. M., and Tavakkoli-Moghaddam, R. (2013), A dynamic pricing approach for returned products in integrated forward/reverse logistics network design, Applied Mathematical Modelling, 37(24), 10182-10202. crossref(new window)

23.
Ko, H. J. and Evans, G. W. (2007), A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs, Computers and Operations Research, 34(2), 346-366. crossref(new window)

24.
Krikke, H., le Blanc, I., van Krieken, M., and Fleuren, H. (2008), Low-frequency collection of materials disassembled from end-of-life vehicles: on the value of on-line monitoring in optimizing route planning, International Journal of Production Economics, 111(2), 209-228. crossref(new window)

25.
Lamsali, H. (2012), A mixed integer non-linear programming model for optimizing the collection methods of returned products, Proceedings of the 2011 9th International Conference on ICT and Knowledge Engineering, Bangkok, Thailand, 129-133.

26.
Lee, C., Realff, M., and Ammons, J. (2011), Integration of channel decisions in a decentralized reverse production system with retailer collection under deterministic non-stationary demands, Advanced Engineering Informatics, 25(1), 88-102. crossref(new window)

27.
Lee, D. H., Dong, M., and Bian, W. (2010), The design of sustainable logistics network under uncertainty, International Journal of Production Economics, 128(1), 159-166. crossref(new window)

28.
Min, H. and Ko, H. J. (2008), The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers, International Journal of Production Economics, 113(1), 176-192. crossref(new window)

29.
Min, H., Ko, C. S., and Ko, H. J. (2006), The spatial and temporal consolidation of returned products in a closed-loop supply chain network, Computers and Industrial Engineering, 51(2), 309-320. crossref(new window)

30.
Nativi, J. J. and Lee, S. (2012), Impact of RFID information-sharing strategies on a decentralized supply chain with reverse logistics operations, International Journal of Production Economics, 136(2), 366-377. crossref(new window)

31.
Ostlin, J., Sundin, E., and Bjorkman, M. (2008), Importance of closed-loop supply chain relationships for product remanufacturing, International Journal of Production Economics, 115(2), 336-348. crossref(new window)

32.
Pishvaee, M. S., Farahani, R. Z., and Dullaert, W. (2010a), A memetic algorithm for bi-objective integrated forward/reverse logistics network design, Computers and Operations Research, 37(6), 1100-1112. crossref(new window)

33.
Pishvaee, M. S., Jolai, F., and Razmi, J. (2009), A stochastic optimization model for integrated forward/reverse logistics network design, Journal of Manufacturing Systems, 28(4), 107-114. crossref(new window)

34.
Pishvaee, M. S., Kianfar, K., and Karimi, B. (2010b), Reverse logistics network design using simulated annealing, International Journal of Advanced Manufacturing Technology, 47(1-4), 269-281. crossref(new window)

35.
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 Modelling, 35(2), 637-649. crossref(new window)

36.
Pokharel, S. and Liang, Y. (2012), A model to evaluate acquisition price and quantity of used products for remanufacturing, International Journal of Production Economics, 138(1), 170-176. crossref(new window)

37.
Ramezani, M., Bashiri, M., and Tavakkoli-Moghaddam, R. (2013), A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level, Applied Mathematical Modelling, 37(1), 328-344. crossref(new window)

38.
Salema, M. I. G., Barbosa-Povoa, A. P., and Novais, A. Q. (2007), An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty, European Journal of Operational Research, 179(3), 1063-1077. crossref(new window)

39.
Schultmann, F., Zumkeller, M., and Rentz, O. (2006), Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry, European Journal of Operational Research, 171(3), 1033-1050. crossref(new window)

40.
Sharma, M., Ammons, J. C., and Hartman, J. C. (2007), Asset management with reverse product flows and environmental considerations, Computers and Operations Research, 34(2), 464-486. crossref(new window)

41.
Srivastava, S. K. (2008), Network design for reverse logistics, Omega, 36(4), 535-548. crossref(new window)

42.
Tagaras, G. and Zikopoulos, C. (2008), Optimal location and value of timely sorting of used items in a remanufacturing supply chain with multiple collection sites, International Journal of Production Economics, 115(2), 424-432. crossref(new window)

43.
Wojanowski, R., Verter, V., and Boyaci, T. (2007), Retail-collection network design under deposit-refund, Computers and Operations Research, 34(2), 324-345. crossref(new window)

44.
Yager, R. R. (1978), Ranking fuzzy subsets over the unit interval, Proceedings of IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, San Diego, CA, 1435-1437.

45.
Yager, R. R. (1981), A procedure for ordering fuzzy subsets of the unit interval, Information Sciences, 24(2), 143-161. crossref(new window)