Tang, L., Ma, Y., Wang, J., Ouyang, L., Byun, J. (2018). Robust parameter design of supply chain inventory policy considering the uncertainty of demand and lead time. Scientia Iranica, (), -. doi: 10.24200/sci.2018.5205.1217
Lina Tang; Yizhong Ma; Jianjun Wang; Linhan Ouyang; Jai-Hyun Byun. "Robust parameter design of supply chain inventory policy considering the uncertainty of demand and lead time". Scientia Iranica, , , 2018, -. doi: 10.24200/sci.2018.5205.1217
Tang, L., Ma, Y., Wang, J., Ouyang, L., Byun, J. (2018). 'Robust parameter design of supply chain inventory policy considering the uncertainty of demand and lead time', Scientia Iranica, (), pp. -. doi: 10.24200/sci.2018.5205.1217
Tang, L., Ma, Y., Wang, J., Ouyang, L., Byun, J. Robust parameter design of supply chain inventory policy considering the uncertainty of demand and lead time. Scientia Iranica, 2018; (): -. doi: 10.24200/sci.2018.5205.1217
Robust parameter design of supply chain inventory policy considering the uncertainty of demand and lead time
1School of Economics and Management, Nanjing University of Science and Technology
2School of Economics and Management, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
3College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
4Department of Industrial and System Engineering, Gyeongsang National University, Jinju, Korea
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 profit and customer service level of supply chain by robust parameter design of inventory policies. This paper proposes to use 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 and dispersion modeling method is adopted to locate the optimum area quickly, which is very efficient to optimize the responses in discrete levels of parameters. Then, fractional factorial design and full factorial design are used to recognize significant factors. Finally, RSM is used to find the optimal combinations of factors for profit maximization and customer service level maximization in continuous levels of parameters. Furthermore, a discussion of multi-response optimization is addressed with different weight of each response. Confirmation experiment results have shown the effectiveness of the proposed method.
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