不确定信息条件下物资配送车辆调度问题研究

Research on the Vehicle Scheduling for Goods Distribution under Uncertain Information Condition

武警部队在遂行抢险救灾任务时，大量不确定的任务信息会对部队物资配送造成重要影响。建立以追求最少时间为目标的机会约束车辆调度规划模型，并运用改进蚁群算法进行求解。实例仿真结果表明，该算法收敛速度和解的精确度优于基本蚁群算法，能有效提升部队应急条件下的物资配送效率。

When carrying out rescue and relief missions, numerous uncertainties can cause significant impacts on goods transportation of CAPF. Focusing on this issue, this article aims at establishing the programming for chance constrained vehicle scheduling model. Meanwhile, we work out the formula according to the optimized ant colony algorithm. Experiment reveals that the convergence rate and accuracy of the optimized ant colony algorithm is better than the usual one. As a consequence, this algorithm can effectively improve the performance of transportation of the armed police under severe conditions.