基于CRL的分布式供应链采购计划细化研究

Xia Zhanguo, Guan Hongjie, Wang Ke
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引用次数: 1

摘要

本文针对供应链采购问题,提出了一种协调强化学习(CRL)的方法来改进由一组代理使用双拍卖市场机制(DAMM)生成的分布式运输计划。通过分析DAMM严格的市场规则所带来的弊端,我们提出了一种基于强化学习的改进方法,即CRL。数值结果表明,CRL有效地改进了由分布式交通规划模型生成的分布式交通规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Refining the Distributed Supply Chain Procurement Plans Based on CRL
In this paper, we propose a method of coordinated reinforcement learning (CRL) to refine the distributed transportation plans generated by a group of agents using double auction market mechanism (DAMM) in the context of supply chain procurement problem. Through analyzing the drawbacks of DAMM due to its strict market rules, we develop a refinement method based on reinforcement learning, namely CRL. Numerical results demonstrate that CRL effectively refines the distributed transportation plans generated by the DAMM.
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