储备仓库多目标多约束下的运输车辆调度算法

Transportation Vehicle Scheduling Algorithm under Multi-Objective and Multi-Constraint in Storage Warehouse

考虑防汛救灾紧迫性、公平性和道路受损拥挤等情况,提出一种储备仓库多目标多约束的运输车辆调度算法(TVSA)。上述算法提出储备仓库车辆需求约束和物流中心车辆拥有上限约束,计算道路组合的拥挤因子,预测车辆总预测行驶时间,考虑多个储备仓库之间的竞争,建立每一个储备仓库的车辆调度优化模型。采用多目标遗传算法求解,即通过Pareto支配策略计算染色体之间的拥挤度和非支配等级,确定染色体被选择进行交叉变异的概率,采用精英保留策略和非支配等级进行筛选。通过迭代获得车辆最优调度方案。实验结果表明,TVSA算法可获得较优车辆运输调度方案,降低车辆平均调度预测时间,离规定救援时间的偏离度和偏离度方差,比SGA算法和MOPSO算法更优。

Considering the urgency, fairness, road damage and congestion of flood prevention and relief, a transportation vehicle scheduling algorithm(TVSA) based on multi-objective and multi-constraint of reserve warehouses is proposed. The TVSA proposed the vehicle demand constraint of the storage warehouse and the upper limit constraint of the vehicles in the logistics center, calculated the congestion factor of the road combination, and predicted total predicted travel time of vehicle. Considering the competition among multiple reserve warehouses, the vehicle scheduling optimization model for each reserve warehouse was established. Then the multi-objective genetic algorithm based on Pareto dominated strategy was used to solve the model, that is, the congestion degree and non dominance level between chromosomes were calculated through Pareto dominance strategy, and the probability of chromosomes being selected for cross mutation was determined. The elite retention strategy and non dominance level were used for screening. The optimal vehicle scheduling scheme was obtained by iteration. The experimental results show that TVSA can obtain a better vehicle transportation scheduling scheme, which reduces average vehicle scheduling forecast time, deviation degree from prescribed rescue time and deviation variance. TVSA is better than SGA and MOPSO.