Nomenclature.

This paper considers the generic problem of a central authority selecting an appropriate subset of operators in order to perform a process (i.e. mission or task) in an optimized manner. The subset is selected from a given and usually large set of ‘n’ candidate operators, with each operator having a certain resource availability and capability. This general mission performance optimization problem is considered in terms of Unmanned Aerial Vehicles (UAVs) acting as firefighting operators in a fire extinguishing mission and from a deterministic and a stochastic algorithmic point of view. Thus the applicability and performance of certain computationally efficient stochastic multistage optimization schemes is examined and compared to that produced by corresponding deterministic schemes. The simulation results show acceptable accuracy as well as useful computational efficiency of the proposed schemes when applied to the time critical resource allocation optimization problem. Distinguishing features of this work include development of a comprehensive UAV firefighting mission framework, development of deterministic as well as stochastic resource allocation optimization techniques for the mission and development of time-efficient search schemes. The work presented here is also useful for other UAV applications such as health care, surveillance and security operations as well as for other areas involving resource allocation such as wireless communications and smart grid.

本文研究一类通用问题：集中式决策主体为以优化方式执行某项流程（即任务或使命），需从给定的、通常规模较大的n个候选操作员集合中选取合适的操作员子集，每位操作员均具备特定的资源可用度与作业能力。本文将该通用任务性能优化问题具象为以无人机（Unmanned Aerial Vehicles, UAVs）为消防作业主体的灭火任务场景，并分别从确定性算法与随机性算法两个维度展开分析，据此对若干计算高效的随机多阶段优化方案的适用性与性能进行检验，并与对应的确定性优化方案所得结果展开对比。仿真结果表明，所提方案应用于时间紧迫型资源分配优化问题时，具备可接受的精度与良好的计算效率。本研究的核心特色包括构建了一套完整的无人机消防任务框架、针对该任务开发了确定性与随机性两类资源分配优化技术以及设计了高时效的搜索方案。本文所提出的研究方法同样适用于其他无人机应用场景，例如医疗保障、监视侦察与安保作业，同时也可推广至无线通信、智能电网等涉及资源分配的其他领域。