Simulation and Optimization for Supply Chain Based on Multi-agent Reinforcement Learning: A Case Study on a Large-scale Refinery

摘要：

A case study on a large-scale refinery is implemented in this paper.Considering the plenty of stochastic factors existing in real life,general-purpose simulation platform ARENA is employed to model the complex supply chain of this refinery and obtain the systems performance indices.With VBA and Object Oriented Programming technology,a kind of architecture is proposed to integrate simulation with optimization.Then a Multi-agent Reinforcement Learning algorithm is designed to optimize the ordering and distributing policies of the refinery.Research results show that the methodology proposed can effectively solve the optimization problems existing in real-life and complicated supply chain.

关键词： Supply Chain Simulation Optimization Reinforcement Learning

作者: PAN Yanchun

作者单位: College of Management,Shenzhen University,P.R.China,518060

会议类型: 国际会议

会议名称: 2008 International Conference on Lofistics Engineering and Supply Chain(2008物流与供应链管理国际研讨会)

会议地点: 长沙

会议语种:英文

页码: 433-439

在线出版日期: 2008-08-20（万方平台首次上网日期，不代表论文的发表时间）