面向服务协调的重大灾害多部门应急服务分布式调度优化研究

重大灾害多部门应急服务之间的协调配合是影响救灾效果的关键因素。针对强时间窗和能力约束及强干扰下的复杂动态和不确定性服务依赖和服务冲突情况，基于免疫MAS（Immune Multi-Agent System）分布式最优化理论，围绕面向服务协调的重大灾害多部门应急服务分布式调度优化展开研究，包括：①不同服务关系、优先级和协调者模式下的，面向服务承诺一致的多Agent应急服务需求分配问题和免疫MAS最优化方法；②不同服务行动目标生成、行动偏差和行动信息获取模式下的，面向服务行动协作的多Agent应急服务行动调度问题和免疫MAS最优化方法；③支持服务过程复杂性/可靠性/协调性评估、多时空尺度情景推演和参数自学习的，面向服务过程协调的多Agent应急服务绩效评估和免疫MAS仿真优化方法。力图实现重大灾害应急服务调度与免疫MAS最优化理论与方法拓展，为分布协作式应急服务调度实践提供科学依据和理论指导。

The coordination of multi-department emergency services for major disaster is the critical factor to influence the emergency response performance. According to the complex, dynamic and uncertain service environments with service interdependences and conflicts, as well as with strong time-window, capacity constraints and disruptions, the immune multi-agent system (MAS) based distributed scheduling optimization of multi-department emergency services for major disaster aiming to the service coordination will be investigated..Firstly, considering the different modes of service relationship, service priority and service coordinator, the multi-agent emergency service demand allocation problem and its immune MAS based optimization approach aiming to the service commitment coordination will be addressed. .Secondly, considering the different modes of action goal generation, action deviation and action information acquisition, the multi-agent emergency service action scheduling problem and its immune MAS based optimization approach aiming to the service action cooperation will be explored. .Thirdly, considering the complexity, reliability and coordination assessment of service process, the discrete event simulation deduction with multiple space-time scale, the system parameter self-learning, the assessment of multi-agent emergency service performance and its immune MAS based simulation optimization approach aiming to the service process coordination will be addressed..These research works will make effort to extend the theories and methods of the emergency service scheduling of major disaster and the immune MAS based optimization, as well as to provide the scientific basis and theoretical guidance for the distributed and collaborative emergency service scheduling practice.