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An Adaptive Vendor Managed Inventory Model Using Action-Reward Learning Method  

Kim Chang-Ouk (연세대학교 정보산업공학과)
Baek Jun-Geol (인덕대학 산업시스템경영과)
Choi Jin-Sung (연세대학교 정보산업공학과)
Kwon Ick-Hyun (고려대학교 산업시스템정보공학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.31, no.3, 2006 , pp. 27-40 More about this Journal
Abstract
Today's customer demands in supply chains tend to change quickly, variously even in a short time Interval. The uncertainties of customer demands make it difficult for supply chains to achieve efficient inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. Un this paper, we propose an adaptive vendor managed inventory (VMI) model for a two-echelon supply chain with non-stationary customer demands using the action-reward learning method. The Purpose of this model is to decrease the inventory cost adaptively. The control Parameter, a compensation factor, is designed to adaptively change as customer demand pattern changes. A simulation-based experiment was performed to compare the performance of the adaptive VMI model.
Keywords
Adaptive VMI Model; Non-Stationary Customer Demand; Action-Reward learning; Inventory Cost; Compensation Factor;
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