Offsetting Inventory Cycles using Mixed Integer Programming and Genetic Algorithm

Authors

  • Ilkyeong K Moon Pusan National University
  • Byung Chul Cha Postal Technology Research Center
  • Sun Kwon Kim S&T Daewoo Co Ltd

DOI:

https://doi.org/10.23055/ijietap.2008.15.3.137

Keywords:

Offsetting cycles, Inventory, Mixed integer programming model, Genetic algorithm

Abstract

We propose a mixed integer programming model to minimize the maximum storage space requirement over an infinite time horizon by offsetting the inventory cycles of items. We also develop a genetic algorithm to find the near-optimal solution. The mixed integer programming model and the genetic algorithm produce better results than the existing heuristic. We also develop a mixed integer programming model for the finite time horizon; this model is more general and realistic than that for the infinite time horizon. A warehouse management system is designed based on the algorithms we developed.

Author Biographies

Ilkyeong K Moon, Pusan National University

I

Byung Chul Cha, Postal Technology Research Center

Byung-Chul Cha is currently a senior member of Postal Technology Research Center in Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea. He received the B.S., M.S., and Ph.D. degrees in industrial engineering from Pusan National University, Busan, Korea, in 1995, 1997, and 2005, respectively. He has been a CPIM since 2004. His research interests include SCM and system analysis and design for Korea Post.

Sun Kwon Kim, S&T Daewoo Co Ltd

Sun-Kwon Kim is currently a research and development engineer of Technical Center in S&T Daewoo Co., Ltd, Busan, Korea. He received his B.S. in Industrial Engineering from Kyungsung University, and M.S. in Industrial Engineering from Pusan National University, Korea. His research interests include SCM and inventory management and design for dampers.

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Published

2022-02-26

How to Cite

Moon, I. K., Cha, B. C., & Kim, S. K. (2022). Offsetting Inventory Cycles using Mixed Integer Programming and Genetic Algorithm. International Journal of Industrial Engineering: Theory, Applications and Practice, 15(3), 245–256. https://doi.org/10.23055/ijietap.2008.15.3.137

Issue

Vol. 15 No. 3 (2008)

Section

Supply Chain Management

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