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Supply Chain Production-distribution Cost Optimization under Grey Fuzzy Uncertainty

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【作者】 刘东波陈玉娟黄道添玉

【Author】 LIU Dong-bo1,CHEN Yu-juan1,HUANG Dao2,TIAN Yu21 College of Mechanical and Electronic Engineering,Shanghai Normal University,Shanghai201418,China2 Research Institute of Automation,East China University of Science and Technology,Shanghai200237,China

【机构】 College of Mechanical and Electronic Engineering Shanghai Normal UniversityCollege of Mechanical and Electronic EngineeringShanghai Normal UniversityResearch Institute of AutomationEast China University of Science and TechnologyShanghai201418ChinaShanghai200237

【摘要】 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.

【Abstract】 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.

【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)
  • 【文献出处】 Journal of Donghua University(English Edition) ,东华大学学报(英文版) , 编辑部邮箱 ,2008年01期
  • 【分类号】O159;O224
  • 【下载频次】109
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