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

Ick Hyun Kwon, Chang Ouk Kim, Jin Jun, Jung Hoon Lee

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)389-397
Number of pages9
JournalExpert Systems with Applications
Issue number1-2
Publication statusPublished - 2008 Jul

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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