A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor

  • Authors:

  • Jiuping Xu;Qiang Liu;Rui Wang

  • Affiliations:

  • Uncertainty Decision-Making Laboratory, School of Business and Administration, Sichuan University, Chengdu 610064, PR China;Uncertainty Decision-Making Laboratory, School of Business and Administration, Sichuan University, Chengdu 610064, PR China;Uncertainty Decision-Making Laboratory, School of Business and Administration, Sichuan University, Chengdu 610064, PR China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

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Abstract

An important issue, when shipping cost and customers demand are random fuzzy variables in supply chain network (SCN) design problem, is to find the network strategy that can simultaneously achieve the objectives of minimization total cost comprised of fixed costs of plants and distribution centers (DCs), inbound and outbound distribution costs, and maximization customer services that can be rendered to customers in terms of acceptable delivery time. In this paper, we propose a random fuzzy multi-objective mixed-integer non-linear programming model for the SCN design problem of Luzhou Co., Ltd. which is representative in the industry of Chinese liquor. By the expected value operator and chance constraint operator, the model has been transformed into a deterministic multi-objective mixed-integer non-linear programming model. Then, we use spanning tree-based genetic algorithms (st-GA) by the Prufer number representation to find the SCN to satisfy the demand imposed by customers with minimum total cost and maximum customer services for multi-objective SCN design problem of this company under condition of random fuzzy customers demand and transportation cost between facilities. Furthermore, the efficacy and the efficiency of this method are demonstrated by the comparison between its numerical experiment results and those of tradition matrix-based genetic algorithm.