Case-based myopic reinforcement learning for satisfying target service level in supply chain

Authors:
Ick-Hyun Kwon;Chang Ouk Kim;Jin Jun;Jung Hoon Lee
Affiliations:
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;Department of Information and Industrial Engineering, Yonsei University, Seoul 120-749, Republic of Korea;Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA;Graduate School of Information, Yonsei University, Seoul 120-749, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 4
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Abstract

In the last decade, driven by global competition in the marketplace, many companies have taken initiatives to revamp their supply chains in order to increase responsiveness to changes in the marketplace. The renovation of inventory control system is central to such an effort. However, experiences in industry have shown that the control of inventory in supply chain is not an easy task because of uncertainties inherent in customer demand. In this paper, we propose a reinforcement learning algorithm appropriate for the nonstationary inventory control problem of supply chain that has a large state space. Traditional reinforcement learning algorithms such as learning automata and Q-learning have the difficulty of slow convergence when applied to the situations with large state spaces. To resolve the problems of nonstationary customer demand and large state space, we develop a case-based myopic reinforcement learning (CMRL) algorithm. A simulation-based experiment was performed to show good performance of CMRL.