A robust design for a closed-loop supply chain network under an uncertain environment
Tóm tắt
This paper presents a robust design for a multi-product, multi-echelon, closed-loop logistic network model in an uncertain environment. The model includes a general network structure considering both forward and reverse processes that can be used in various industries, such as electronics, digital equipment, and vehicles. Because logistic network design is a time consuming and costly project as well as a strategic and sensitive decision (i.e. the change of such decision is difficult in the future), a robust optimisation approach is adopted to cope with the uncertainty of demand and the return rate described by a finite set of possible scenarios. Hence, to obtain robust solutions with better time, the scenario relaxation algorithm is employed for the proposed model. Numerical examples and a sensitivity analysis are presented to demonstrate the significance and applicability of the presented model. It is shown that solutions resulted from the suggested approach insure more situations, especially in worst case ones. The results show that although the profit values of the robust configuration are less than the deterministic configuration, the robust configuration is more reliable than the deterministic one because the deterministic configuration is infeasible under some demand and return rates (i.e. in the worst cases). Moreover, the results show the computing time superiority of the algorithm compared to the extensive form model as well as optimality of the resulted solutions.
Tài liệu tham khảo
Melo MT, Nickel S, Saldanha-da-Gama F (2009) Facility location and supply chain management—a review. Eur J Oper Res 196:401–412
Shen ZM (2007) Integrated supply chain design models: a survey and future research directions. J Ind Manag Optim 3(1):1–27
Manzini R, Gamberi M, Gebennini E, Regattieri A (2008) An integrated approach to the design and management of a supply chain system. Int J Adv Manuf Technol 37:625–640
El-Sayed M, Afia N, El-Kharbotly A (2010) A stochastic model for forward–reverse logistics network design under risk. Comput Ind Eng 58:423–431
Selim H, Ozkarahan I (2008) A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. Int J Adv Manuf Technol 36:401–418
Yeh WC (2005) A hybrid heuristic algorithm for the multistage supply chain network problem. Int J Adv Manuf Technol 26:675–685
Üster H, Easwaran G, Akçali E, Çetinkaya S (2007) Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model. Nav Res Logist 54:890–907
Fleischmann M, Beullens P, Bloemhof-ruwaard JM, Wassenhove L (2001) The impact of product recovery on logistics network design. Prod Oper Manag 10:156–173
Lee D, Dong M (2008) A heuristic approach to logistics network design for end-of-lease computer products recovery. Transp Res E 44:455–474
Verstrepen S, Cruijssen F, de Brito M, Dullaert W (2007) An exploratory analysis of reverse logistics in Flanders. Eur J Transp Infrastruct Res 7(4):301–316
Wang HF, Hsu HW (2010) A closed-loop logistic model with a spanning-tree based genetic algorithm. Comput Oper Res 37:376–389
Dekker R, Fleischmann M, Inderfurth K, van Wassenhove LN (eds) (2004) Reverse logistics: Quantitative models for closed-loop supply chains. Springer, New York
Georgiadis P, Besiou M (2010) Environmental and economical sustainability of WEEE closed-loop supply chains with recycling: a system dynamics analysis. Int J Adv Manuf Technol 47(5):475–493
Georgiadis P, Vlachos D, Tagaras G (2006) The impact of product lifecycle on capacity planning of closed-loop supply chains with remanufacturing. Prod Oper Manag 15:514–527
Georgiadis P, Athanasiou E (2010) The impact of two-product joint lifecycles on capacity planning of remanufacturing networks. Eur J Oper Res 202:420–433
Sabri EH, Beamon BM (2000) A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega 28:581–598
Salema MIG, Barbosa-Povoa AP, Novais AQ (2007) An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. Eur J Oper Res 179:1063–1077
Ko HJ, Evans GW (2007) A genetic-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Comput Oper Res 34:346–366
Min H, Ko HJ (2008) The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. Int J Prod Econ 113:176–192
Lee DH, Dong M (2009) Dynamic network design for reverse logistics operations under uncertainty. Transp Res E 45:61–71
Pishvaee MR, Farahani RZ, Dullaert W (2010) A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Comput Oper Res 37(6):1100–1112
Ramezani M, Bashiri M, Tavakkoli-Moghaddam R (2012) A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Math Model. doi:10.1016/j.apm.2012.02.032
Baohua W, Shiwei H (2009) Robust optimization model and algorithm for logistics center location and allocation under uncertain environment. J Transp Syst Eng Inf Technol 9(2):69–74
Kouvelis P, Yu G (1997) Robust discrete optimization and its applications. Kluwer, Dordrecht
Aissi H, Bazgan C, Vanderpooten D (2009) Min–max and min–max regret versions of combinatorial optimization problems: a survey. Eur J Oper Res 197:427–438
Ben-Tal A, Nemirovski A (1999) Robust solutions to uncertain programs. Oper Res Lett 25:1–13
Ben-Tal A, Nemirovski A (2000) Robust solutions of linear programming problems contaminated with uncertain data. Math Program 88:411–424
Ben-Tal A, El-Ghaoui L, Nemirovski A (2000) Robust semidefinite programming. In: Saigal R, Vandenberghe L, Wolkowicz H (eds) Handbook on semidefinite programming. Kluwer, New York, pp 139–162
Bertsimas D, Sim M (2003) Robust discrete optimization and network flows. Math Program Ser B 98:49–71
Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53
Zakovic S, Rustem B (2002) Semi-infinite programming and applications to minimax problems. Ann Oper Res 124:81–110
Rustem B, Howe M (2002) Algorithms for worst-case design and applications to risk management. Princeton University Press Publishers, Princeton
Daskin MS, Hesse SM, Re Velle CS (1997) a-reliable p-minimax regret: a new model for strategic facility location modeling. Locat Sci 5(4):227–246
Kalai R, Aloulou MA, Vallin P, Vanderpooten D (2005) Robust 1-median location problem on a tree. In: Proceedings of Third Edition of the Operational Research Peripatetic Postgraduate Programme (ORP3 2005), Valencia, Spain. pp. 201–212
Assavapokee T, Realff MJ, Ammons JC, Hong I (2008) Scenario relaxation algorithm for finite scenario-based min–max regret and min–max relative regret robust optimization. Comput Oper Res 35:2093–2102
Mulvey JM, Vanderbei RJ, Zenios SA (1995) Robust optimization of large-scale systems. Oper Res 43(2):264–28
Fulya Altiparmak F, Gen M, Lin L, Karaoglan I (2009) A steady-state genetic algorithm for multi-product supply chain network design. Comput Ind Eng 56:521–537
Salema MI, Po´voa APB, Novais AQ (2006) A warehouse-based design model for reverse logistics. J Oper Res Soc 57(6):615–629
Lu Z, Bostel N (2007) A facility location model for logistics systems including reverse flows: the case of remanufacturing activities. Comput Oper Res 34:299–323