Situation reactive approach to Vendor Managed Inventory problem

  • Authors:

  • Choonjong Kwak;Jin Sung Choi;Chang Ouk Kim;Ick-Hyun Kwon

  • Affiliations:

  • Research Evaluation and Planning Division, Korea Aerospace Research Institute, 45 Eoeun-Dong, Yuseong-Gu, Daejeon 305-333, Republic of Korea;Department of Information and Industrial Engineering, Yonsei University, Seoul 120-749, Republic of Korea;Department of Information and Industrial Engineering, Yonsei University, Seoul 120-749, Republic of Korea;Department of Systems Management Engineering, Inje University, Gimhae, Gyeongnam 621-749, Republic of Korea

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

In this research, we deal with VMI (Vendor Managed Inventory) problem where one supplier is responsible for managing a retailer's inventory under unstable customer demand situation. To cope with the nonstationary demand situation, we develop a retrospective action-reward learning model, a kind of reinforcement learning techniques, which is faster in learning than conventional action-reward learning and more suitable to apply to the control domain where rewards for actions vary over time. The learning model enables the inventory control to become situation reactive in the sense that replenishment quantity for the retailer is automatically adjusted at each period by adapting to the change in customer demand. The replenishment quantity is a function of compensation factor that has an effect of increasing or decreasing the replenishment amount. At each replenishment period, a cost-minimizing compensation factor value is chosen in the candidate set. A simulation based experiment gave us encouraging results for the new approach.