• Title of article

    Robust parameter design of supply chain inventory policy considering the uncertainty of demand and lead time

  • Author/Authors

    Tang, L.N. School of Economics and Management - Nanjing University of Science and Technology, Nanjing, Jiangsu, China , Ma, Y.Z. School of Economics and Management - Nanjing University of Science and Technology, Nanjing, Jiangsu, China , Wang, J.J. School of Economics and Management - Nanjing University of Science and Technology, Nanjing, Jiangsu, China , Ouyang, L.H. College of Economics and Management - Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China , Byun, J.H. Department of Industrial and System Engineering - Gyeongsang National University, Jinju-daero, Jinju, Korea

  • Pages
    17
  • From page
    2971
  • To page
    2987
  • Abstract
    The uncertainty of demand and lead time in inventory management has posed challenges for the supply chain management. The purpose of this paper is to optimize the total prot and customer service level of supply chain by robust parameter design of inventory policies. This paper proposes system dynamics simulation, Taguchi method, and Response Surface Methodology (RSM) to model a multi-echelon supply chain. Based on the sequential experiment principle, Taguchi method combining location with dispersion modeling method is adopted to locate the optimum area quickly, which is very ecient to optimize the responses at discrete levels of parameters. Then, fractional factorial design and full factorial design are used to recognize signicant factors. Finally, RSM is used to nd the optimal combinations of factors for prot maximization and customer service level maximization at continuous levels of parameters. Furthermore, a discussion of multiresponse optimization is addressed with dierent weights of each response. Conrmation experiment results showed the eectiveness of the proposed method.
  • Keywords
    Supply chain , Inventory policy , Simulation , Taguchi method , Response surface methodology
  • Journal title
    Scientia Iranica(Transactions E: Industrial Engineering)
  • Serial Year
    2019
  • Record number

    2525076