Analyses about Efficiency of Reinforcement Learning to Supply Chain Ordering Management

摘要：

The Reinforcement Learning (RL) is an efficient machine learning method for solving problems that an agent has no knowledge about the environment a priori. Improving efficiency of decision-making practices in a supply chain is a major competitive domain in today’s uncertain business environments. The bullwhip effect is an important phenomenon in the supply chain, in which the order variability increases as moving up along the supply chain. This paper proposes a multiagent coordination mechanism utilizing RL method to the supply chain ordering management. Further, the analyses about the efficiency of the method are discussed in detail based on some representative test data. Results show that the RL agent reduces the bullwhip effect efficiently in the stochastic supply chain.

关键词： supply chain ordering management reinforcement learning bullwhip stochastic

作者: Ruoying Sun Gang Zhao

作者单位: School of Information ManagementBeijing Information Science and Technology UniversityBeijing, China School of Information Management Beijing Information Science and Technology University Beijing, Chin

会议类型: 国际会议

会议名称: IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)

会议地点: 北京

会议语种:英文

页码: 124-127

在线出版日期: 2012-07-25（万方平台首次上网日期，不代表论文的发表时间）