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
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