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Scientific Programming
Volume 2017 (2017), Article ID 1080468, 14 pages
Research Article

A Network Optimization Research for Product Returns Using Modified Plant Growth Simulation Algorithm

1Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, China
2School of Business, Dalian University of Technology, Panjin 124221, China
3College of Economics and Management, Northwest A&F University, Yangling 712100, China

Correspondence should be addressed to Xuping Wang

Received 15 September 2016; Revised 4 November 2016; Accepted 25 December 2016; Published 9 February 2017

Academic Editor: Lu Zhen

Copyright © 2017 Xuping Wang 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.


As product returns are eroding Internet retail profit, managers are continuously striving for a more scientific and efficient network layout to arrange the returned goods. Based on a three-echelon product returns network, this paper proposes a mixed integer nonlinear programming model with the aim of minimizing total cost and creates a high-efficiency method, the Modified Plant Growth Simulation Algorithm (MPGSA), to optimize the problem. The algorithm handles the objective function and the constraints, respectively, requiring no extrinsic parameters and provides a guiding search direction generated from the assessment of the current solving state. Above all, MPGSA keeps a great balance between concentrating growth opportunities on the outstanding growth points and expanding the searching scope. The improvements give the revaluating and reselecting chances to all growth points in each iteration, enhancing the optimization efficiency. A case study illustrates the effectiveness and robustness of MPGSA compared to its original version, Plant Growth Simulation Algorithm, and other approaches, namely, Genetic Algorithm, Artificial Immune System, and Simulated Annealing.