基于遗传算法的作战任务分配和资源调度问题研究

在作战行动中出现多任务与多种保障资源合理调度困难的问题十分常见。笔者以任务完成的总时间最短为目标函数构建数学模型，使用遗传算法进行迭代优化得到保障资源调度的全局最优解。对遗传算法进行自适应改进、移民交叉算子操作和迭代条件优化，解决了过早收敛无法求出全局最优解的问题，并使算法运行效率提高了76.4%。仿真实验结果表明：该方法切实可行，可以快速准确地完成多种保障资源调度并形成任务分配方案，满足现阶段作战部队资源保障的现实要求。研究成果在高效完成保障资源调度的同时不产生冗余负担，具有较好的应用价值和发展前景。

Research on Combat Task Assignment and Resource Scheduling Based on Genetic Algorithm

In combat operations, it is very common to have difficulties in the rational scheduling of multi-task and multi-support resources. In this paper, a mathematical model is constructed with the objective function of the shortest total time of task completion, and the global optimal solution of support resource scheduling is obtained by using genetic algorithm for iterative optimization. The genetic algorithm is improved by self-adaptation, the operation of emigration and crossover, and the optimization of iterative conditions, which solves the problem that the global optimal solution can not be obtained by premature convergence, and improves the efficiency of the algorithm by 76. 4%. The simulation results show that the method is feasible, and it can quickly and accurately complete a variety of support resource scheduling and form a task allocation scheme to meet the realistic requirements of combat force resource support at this stage. The research results have good application value and development prospects, which can efficiently complete the support resource scheduling without generating redundant burden.