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发布时间：2019-03-12

论文类型：期刊论文

发表时间：2017-01-01

发表刊物：SCIENTIFIC PROGRAMMING

收录刊物：Scopus、EI、SCIE

卷号：2017

ISSN号：1058-9244

摘要：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.