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.
| Original language | English |
|---|---|
| Pages (from-to) | 389-397 |
| Number of pages | 9 |
| Journal | Expert Systems with Applications |
| Volume | 35 |
| Issue number | 1-2 |
| DOIs | |
| Publication status | Published - 2008 Jul 1 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
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Case-based myopic reinforcement learning for satisfying target service level in supply chain. / Kwon, Ick Hyun; Kim, Chang Ouk; Jun, Jin; Lee, Jung Hoon.
In: Expert Systems with Applications, Vol. 35, No. 1-2, 01.07.2008, p. 389-397.Research output: Contribution to journal › Article
TY - JOUR
T1 - Case-based myopic reinforcement learning for satisfying target service level in supply chain
AU - Kwon, Ick Hyun
AU - Kim, Chang Ouk
AU - Jun, Jin
AU - Lee, Jung Hoon
PY - 2008/7/1
Y1 - 2008/7/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=44949146357&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2007.07.002
DO - 10.1016/j.eswa.2007.07.002
M3 - Article
VL - 35
SP - 389
EP - 397
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 1-2
ER -