An adaptive inventory control model for a supply chain with nonstationary customer demands

  • Authors:

  • Jun-Geol Baek;Chang Ouk Kim;Ick-Hyun Kwon

  • Affiliations:

  • Department of Industrial Systems Engineering, Induk Institute of Technology, Seoul, Republic of Korea;Department of Information and Industrial Engineering, Yonsei University, Seoul, Korea;Department of Industrial Systems and Information Engineering, Korea University, Seoul, Korea

  • Venue:
  • PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we propose an adaptive inventory control model for a supply chain consisting of one supplier and multiple retailers with nonstationary customer demands. The objective of the adaptive inventory control model is to minimize inventory related cost. The inventory control parameter is safety lead time. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a statistical distribution is not a prerequisite in this model. Instead, using a reinforcement learning technique called action-reward based learning, the control parameter is designed to adaptively change as customer demand pattern changes. A simulation based experiment was performed to compare the performance of the adaptive inventory control model.