【摘要】 航空公司进行航班延误恢复时，各种资源之间会通过航班计划产生间接关联，此时各决策单元若独立地考虑本领域内的资源恢复问题，将难以保证恢复方案的整体可行性和全局优化性。为探究航空公司航班恢复过程中各决策部门的决策模型及其协同关系，本文提出了基于多智能体技术的航班恢复协同决策仿真方法。首先，基于航空公司实际组织架构构建了航班恢复多智能体决策系统框架；其次，对部门间协同决策的动态过程进行了分析，将延误恢复的全过程分为了预恢复、可行解协商、均衡解协商3个阶段，构建了三阶段协同决策机制；最后，根据不同资源的恢复特性建立各决策部门的核心决策模型与部门间自动协商模型，并基于多智能体系统进行仿真。仿真结果显示，基于多智能体的协同决策方法能够在3.8 s的极短时间内针对1天中包含3架飞机和12个航班的航班计划做出完整的延误恢复方案，并且能够在保障航空公司整体效益的情况下一定程度地平衡各决策主体的自身利益。更多还原

【Abstract】 In the case of airlines recovering from flight delays, resources are indirectly linked to each other through the flight plan, and it is difficult to ensure the overall feasibility and global optimization of the recovery solution if each decision-making department considers the recovery of resources in its own area. This paper proposes a multi-agent simulation method for flight recovery in order to investigate the decision models of the departments and their collaborative relationship in the airline flight recovery process. Firstly, the framework of flight recovery multi-agent system is constructed based on the actual organizational structure of the airline. Then, the whole process of delay recovery is divided into three stages: Pre-recovery, feasible solution negotiation and balanced solution negotiation, and a three-stage collaborative decision-making mechanism is constructed. Finally, the core decision model of each department agent is developed according to the recovery characteristics of different resources, and the multi-agent auto-negotiation model is established based on the collaborative decision mechanism. The simulation results show that the multi-agent based collaborative decision-making method can produce a complete delay recovery plan in 3.8 s for a flight schedule consisting of three aircrafts and twelve flights in one day, and it can balance the local interests of each decision maker to a certain extent while safeguarding the overall benefits of the airline.更多还原