文献类型：期刊文献

中文题名：Hybrid Intelligent Approach for the Selection of Third-Party Reverse Logistics Provider under Uncertainty

英文题名：Hybrid intelligent approach for the selection of third-party reverse logistics provider under uncertainty

作者：Gong, Yan-Xue[1,2]; Song, Jun-Dian[2]; Peng, Yi-Gong[3]; Tian, Yu[4]; Zheng, Shu-Quan[1,2]

机构：[1] Intelligent Product Innovation Center, Shanghai Industrial Technology Institute, Shanghai, China; [2] Shanghai Development Center of Computer Software Technology, Shanghai, China; [3] School of Information, East China University of Science and Technology, Shanghai, China; [4] College of Logistics Engineering, Shanghai Maritime University, Shanghai, China

年份：2014

卷号：31

期号：4

起止页码：484

中文期刊名：Journal of Donghua University(English Edition)

外文期刊名：Journal of Donghua University (English Edition)

收录：EI(收录号：20150200409215);Scopus

基金：Project of the Shanghai Committee of Science and Technology,China(No.12DZ1510000)

语种：英文

中文关键词：hybrid intelligent approach;third-party reverse logistics provider;uncertainty

外文关键词：Logistics - Decision making - Fuzzy sets - Particle swarm optimization (PSO)

摘要：A hybrid intelligent approach is proposed to help the decision maker to select the appropriate third-party reverse logistics provider. The following process is included: firstly,the evaluation team is established to determine the selection criteria and evaluate them by triangular fuzzy numbers; secondly,calculate the weight of criteria by the proposed hybrid algorithm integrating particle swarm optimization( PSO) and simulated annealing( SA); then, the performance evaluation for each supplier is predicted by the proposed self-feedback neural network( SFBNN) based on the historical data. A numerical example is also presented to interpret the methodology above.
A hybrid intelligent approach is proposed to help the decision maker to select the appropriate third-party reverse logistics provider. The following process is included: firstly, the evaluation team is established to determine the selection criteria and evaluate them by triangular fuzzy numbers; secondly, calculate the weight of criteria by the proposed hybrid algorithm integrating particle swarm optimization (PSO) and simulated annealing (SA); then, the performance evaluation for each supplier is predicted by the proposed self-feedback neural network (SFBNN) based on the historical data. A numerical example is also presented to interpret the methodology above.