Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 928620, 21 pages
Research Article

Design of a Multiobjective Reverse Logistics Network Considering the Cost and Service Level

1School of Management, Xi’an Jiaotong University, NO.28 Xiannin West Road, Xian, Shaanxi 710049, China
2The Key Lab of the Ministry of Education for Process Control and Efficiency Engineering, NO.28 Xiannin West Road, Xian, Shaanxi 710049, China
3School of Management, Northwestern Polytechnical University, NO.127 Youyi Road, Xian, Shaanxi 710072, China

Received 18 April 2012; Accepted 31 July 2012

Academic Editor: Cristian Toma

Copyright © 2012 Shuang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Reverse logistics, which is induced by various forms of used products and materials, has received growing attention throughout this decade. In a highly competitive environment, the service level is an important criterion for reverse logistics network design. However, most previous studies about product returns only focused on the total cost of the reverse logistics and neglected the service level. To help a manufacturer of electronic products provide quality postsale repair service for their consumer, this paper proposes a multiobjective reverse logistics network optimisation model that considers the objectives of the cost, the total tardiness of the cycle time, and the coverage of customer zones. The Nondominated Sorting Genetic Algorithm II (NSGA-II) is employed for solving this multiobjective optimisation model. To evaluate the performance of NSGA-II, a genetic algorithm based on weighted sum approach and Multiobjective Simulated Annealing (MOSA) are also applied. The performance of these three heuristic algorithms is compared using numerical examples. The computational results show that NSGA-II outperforms MOSA and the genetic algorithm based on weighted sum approach. Furthermore, the key parameters of the model are tested, and some conclusions are drawn.