考虑服务水平的逆向物流网络优化决策

运筹与管理 ›› 2013, Vol. 22 ›› Issue (5): 90-97.

考虑服务水平的逆向物流网络优化决策

李爽^1,2, 王能民^1,2, 何正文^1,2, 马云高^1,2

1.西安交通大学管理学院,陕西西安 710049;
2.过程控制与效率工程教育部重点实验室,陕西西安 710049

收稿日期:2012-04-13 出版日期:2013-10-25
作者简介:李爽(1987-),女,辽宁鞍山人,博士研究生;王能民(1974-),男,湖南双峰人,管理学博士、博士后,副教授,研究方向:工业工程与管理。
基金资助:
国家自然科学基金项目(70602017,70971105,71071126);国家社会科学基金重大项目(1282D070);陕西省自然科学基金项目(2010JM9003);中央高校基本科研业务费专项资金资助

Decision-making for Reverse Logistics Network Optimization Considering Service Level

LI Shuang^1,2, WANG Neng-min^1,2, HE Zheng-wen^1,2, MA Yun-gao^1,2

1. School of Management,Xi'an Jiaotong University, Xi'an 710049, China;
2. Key Lab of the Ministry of Education for Process Control &Efficiency Engineering, Xi'an 710049, China

Received:2012-04-13 Online:2013-10-25

摘要/Abstract

摘要： 针对电子产品的售后维修服务问题,建立了一个同时考虑成本和服务质量的多目标逆向物流网络优化模型;该问题是多目标的NP-hard问题,应用NSGA-II算法和多目标模拟退火算法(MOSA)两种多目标进化算法,对模型进行求解并对其求解的效果进行比较分析;多组算例测试结果表明,NSGA-II相比MOSA更具优势。

关键词: 逆向物流, 物流网络设计, 多目标进化算法, 选址-分配

Abstract: This paper proposes a multi-objective reverse logistics network optimization model considering both cost and service level to help manufacturers of electronic products provide quality post-sale repair service for their consumers. Due to the NP-hard nature of the problem, two multi-objective evolutionary algorithms are employed including the Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Simulated Annealing(MOSA). The performance of the two heuristic algorithms is compared using numerical examples. The computational results show that NSGA-II outperforms MOSA.

Key words: reverse logistics, logistics network design, multi-objective evolutionary algorithms, location-allocation

中图分类号:

F272.3

李爽, 王能民, 何正文, 马云高. 考虑服务水平的逆向物流网络优化决策[J]. 运筹与管理, 2013, 22(5): 90-97.

LI Shuang, WANG Neng-min, HE Zheng-wen, MA Yun-gao. Decision-making for Reverse Logistics Network Optimization Considering Service Level[J]. Operations Research and Management Science, 2013, 22(5): 90-97.

参考文献

[1] De Brito M P, Dekker R. Reverse logistics—a framework[R]. Econometric Institute Report EI 2002-38. Netherlands: Erasmus University Rotterdam, 2002.1-19.
[2] Stock J, Speh T, Shear H. Many happy(product)returns[J]. Harvard Business Review, 2002, 80(7): 16-17.
[3] Ko H J, Evans G W. A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs[J]. Computers & Operations Research, 2007, 34(2): 346-366.
[4] Amini M M, Retzlaff-Roberts D, Bienstock C C. Designing a reverse logistics operation for short cycle time repair services[J]. International Journal of Production Economics, 2005, 96(3): 367-380.
[5] Barros A I, Dekker R, Scholten V. A two-level network for recycling sand: a case study[J]. European Journal of Operational Research, 1998, 110(2): 199-214.
[6] Min H, Jeung Ko H, Seong Ko C. A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns[J]. Omega, 2006, 34(1): 56-69.
[7] Cruz-Rivera R, Ertel J. Reverse logistics network design for the collection of end-of-life vehicles in Mexico[J]. European Journal of Operational Research, 2009, 196(3): 930-939.
[8] Salema M I G, Barbosa-Povoa A P, Novais A Q. An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty[J]. European Journal of Operational Research, 2007, 179(3): 1063-1077.
[9] Qin Z, Ji X. Logistics network design for product recovery in fuzzy environment[J]. European Journal of Operational Research, 2010, 202(2) 479-490.
[10] 周根贵,曹振宇.遗传算法在逆向物流网络选址问题中的应用研究[J].中国管理科学, 2005,13(1):42-47.
[11] 李波,曾成培.一种逆向物流网络的多期动态选址方法[J].管理科学学报, 2008,11(5):76-84.
[12] 赵宜,尹传忠,蒲云.回收物流设施多层选址模型及其算法[J].西南交通大学学报, 2005,40(4):530-534.
[13] Srivastava S K. Network design for reverse logistics[J]. Omega, 2008, 36(4): 535-548.
[14] Daskin, M S. Network and discrete location: models, algorithms, and applications[M]. New York: John Wiley & Sons, 1995.
[15] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[16] Altiparmak F, Gen M, Lin L, Paksoy T. A genetic algorithm approach for multi-objective optimization of supply chain networks[J]. Computers & Industrial Engineering, 2006, 51(1): 196-215.