Dyna, an integrated architecture for learning, planning, and reacting
ACM SIGART Bulletin
Technical Note: \cal Q-Learning
Machine Learning
Competitive and Cooperative Inventory Policies in a Two-Stage Supply Chain
Management Science
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
A Single-Item Inventory Model for a Nonstationary Demand Process
Manufacturing & Service Operations Management
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Uncertainties inherent in customer demands make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunities or keeping excessive chain-wide inventories. In this paper, two adaptive inventory-control models, a centralized model and a decentralized one, are proposed for a supply chain consisting of one supplier and one retailers. The objective of the two models is to satisfy a target service level predefined for each retailer and to minimize the whole inventory cost. The inventory-control parameters of the supplier and retailers are safety lead time and safety stocks, respectively. Unlike most extant inventory-control approaches, modelling the uncertainty of customer demand as a statistical distribution is not a prerequisite in the two models. Instead, using a reinforcement learning technique called action-reward method, the control parameters are designed to adaptively change as customer demand patterns changes. A simulation-based experiment was performed to compare the performance of the two inventory control models.