作战任务和平台资源的合理匹配是作战体系发挥最大效能的重要条件, 由于平台资源之间协同水平存在差异, 若将协同水平高的平台组合到一起执行任务, 能够更好地提高作战效能。首先建立任务之间协同程度以及平台之间协同水平的量化模型; 然后建立以任务完成时间最小、任务内自协同度和任务间互协同度最大为目标函数的任务分配模型, 提出用于求解该模型的动态列表规划和混沌离散粒子群混合任务分配算法, 使用动态列表规划选择需处理的任务, 利用混沌离散粒子群算法为选定任务分配平台资源; 最后通过仿真实验验证了模型和方法的可行性与有效性。

The reasonable matching of the combat tasks with the platform resources is an important condition for maximizing the operational effectiveness of the combat systems. Considering the difference of cooperation ability between the platforms, the operational effectiveness can be improved greatly if the platforms with high cooperation ability are put together to execute a task. Firstly, the models of cooperation degree between tasks and cooperation ability between platforms are established. Then a task-allocation model is established taking the minimal task completion time, maximal self-cooperation degree within a task and maximal mutual-cooperation degree between the tasks as the objective functions. A hybrid approach is proposed based on Dynamic List Scheduling (DLS) and Chaos Discrete Particle Swarm Optimization (CDPSO) to solve the model, for which DLS is used to select the task needs to be disposed, then CDPSO is used to select the optimal platforms for the selected task. Finally, a simulation is provided to demonstrate the feasibility and validity of the model.