An Adaptive Inventory Control for a Supply Chain
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 prede-fined 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.
Inventory Control Supply Chain Adaptive Forecast Reinforcement Learning Safety Stock
Junqin Xu Jihui Zhang Yushuang Liu
School of Mathematical Science, Qingdao University, Qingdao 266071, China Institute of Complexity Science, Qingdao University, Qingdao 266071, China College of Science,Qingdao University of Science & Technology, Qingdao 266071, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5714-5719
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）