基于多Agent强化学习的战时备件供应保障动态协调机制

Research on Multi Agent Reinforcement Learning Based Dynamic Coordination Mechanism for Wartime Spares Support

有效的备件供应保障是保证航空装备处于良好状态的重要因素。战时备件供应保障的不确定性特点突出。为了应对这些不确定,精确保障要求下战时备件供应保障更加强调备件资源在系统中的动态协调。鉴于战时备件供应保障系统与多Agent系统的相似性,采用基于A-gent的建模仿真技术研究多阶段供应保障过程中的动态协调机制。以Agent之间的供需关系为基础给出了多Agent系统模型结构中小组的定义。为了给出备件短缺情况下使军事效益最大的备件分配策略,设计出以小组为单位的多Agent强化学习方法。最后通过仿真实例验证了方法的有效性。

Spare parts support plays an import role during wartime. In order to meet the requirements of Precision Support, spare parts support must be planned deliberatively prewar and be executed flexibly to deal with various uncertainties. Based on the similarity between the wartime spares support system and the multi agent system, Agent based modeling and simulation methods are adopted to investigate the dynamic coordinate mechanism during the wartime. Groups in the multi - agent system＇s structure are described on the bases of the relationship between the Agents. To decide how to supply spares dynamically during the wartime, the new multi agent reinforcement learning method is designed and presented. A simulation example is illustrated in the end and the simulation result shows that the method is effective.