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Adaptive Inventory Control Models in a Supply Chain with Nonstationary Customer Demand  

Baek, Jun-Geol (Department of Industrial System Engineering, Induk Institute of Technology)
Kim, Chang Ouk (Department of Information and Industrial Engineering, Yonsei University)
Jun, Jin (Research Institute for Information and Communication Technology, Korea University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.31, no.2, 2005 , pp. 106-119 More about this Journal
Abstract
Uncertainties inherent in customer demand patterns make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. In this paper, we propose two intelligent adaptive inventory control models for a supply chain consisting of one supplier and multiple retailers, with the assumption of information sharing. The inventory control parameters of the supplier and retailers are order placement time to an outside source and reorder points in terms of inventory position, respectively. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a stationary statistical distribution is not necessary in these models. Instead, using a reinforcement learning technique, the control parameters are designed to adaptively change as customer demand patterns change. A simulation based experiment was performed to compare the performance of the inventory control models.
Keywords
supply chain; adaptive inventory control; reinforcement learning;
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