文献类型：期刊文献

中文题名：Supply Chain Production-distribution Cost Optimization under Grey Fuzzy Uncertainty

英文题名：Supply chain production-distribution cost optimization under grey fuzzy uncertainty

作者：Liu, Dong-Bo[1]; Chen, Yu-Juan[1]; Huang, Dao[2]; Tian, Yu[2]

机构：[1] College of Mechanical and Electronic Engineering, Shanghai Normal University, Shanghai 201418, China; [2] Research Institute of Automation, East China University of Science and Technology, Shanghai 200237, China

年份：2008

卷号：25

期号：1

起止页码：41

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：20083311457024);Scopus

基金：The Science and Research Foundation of Shanghai Municipal Education Commission (No06DZ033);the Doctoral Science and Research Foundation of Shanghai Nor mal University ( No PL719);the Science and Research Foundation of Shanghai Nor mal University (NoSK200741)

语种：英文

中文关键词：supply chain optimization; grey fuzzy uncertainty; neural netwok ; particle swarm optimization algorithm; differential evolution algorithm

外文关键词：Stochastic systems - Particle swarm optimization (PSO)

摘要：Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.
Most supply chain programming problems are restricted to the deterministic situations or stochastic environments. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization (PSO) algorithm based on the Differential Evolution (DE) algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.