战时不确定性运输路径优化

基于战时运输随机时间与模糊损耗，建立了路径优化的相关机会规划模型和遗传算法；针对运输中的“必经点”问题，设计了特殊染色体编码、交叉与变异规则；提出通过对初始种群染色体的预处理来提高算法性能；给出了获取时间与损耗的模拟步骤。最后，还进行了算法比较实验和结果分析，验证了算法的有效性。

Optimizing the Transportation Path with Uncertain Factor in War

Based on stochastic time and fuzzy loss in war, the model of optimal path is constructed by dependent-chance programming and a genetic algorithm is designed. For solving the problems of the necessity - node, the special rules of coding, crossover and mutation are designed. Initial chromosomes are predisposed to improve the efficiency of algorithm. The steps of simulation to gain time and loss are presented. Finally, some experiments are performed and analyses are presented to verify the effectiveness of the algorithm.