基于动态混合遗传算法的配送路径优化

Distribution path optimization based on dynamic hybrid genetic algorithm

随着越来越多的突发性卫生事件爆发,人们对应急医疗物资的需求量与日俱增,但应急医疗物资配送效率低下等问题依旧存在。文中研究建立多配送点配送模型,该模型对多个应急医疗物资配送中心到多个配送点的配送路径进行有效地优化,并在计算过程中考虑客户时间窗与交通拥堵情况,达到提高客户满意度、节约成本、减少配送过程中所产生的碳排放量的目标,如同此类问题都归类为VRP问题(车辆路径规划问题)。在该模型的计算过程中采用动态混合遗传算法(DHGA),在改进的遗传算法中加入大规模领域搜索算法,解决遗传算法求解精确度过低、易陷入局部最优循环的缺点。最后将计算结果与传统遗传算法(GA)、粒子群算法(ACO)以及蚁群算法(ACO)进行对比,结果表明该算法相对于其他算法的各项指标都得到明显提升。

With the outbreak of more and more sudden health events,people’s demand for emergency medical supplies is increasing,but the problems such as inefficient distribution of emergency medical supplies still exist.This paper considers the establishment of a multi-distribution point distribution model,which effectively optimizes the distribution path from multiple emergency medical materials distribution centers to multiple distribution points,and considers the customer time window and traffic congestion in the calculation process,so as to achieve the goals of improving customer satisfaction,saving costs and reducing carbon emissions generated in the distribution process.The dynamic hybrid genetic algorithm(DHGA)is used in the calculation process of the model,and the large-scale domain search algorithm is added to the improved genetic algorithm to solve the shortcomings of the genetic algorithm that the accuracy is too low and it is easy to fall into the local optimal cycle.Finally,the results are compared with the traditional genetic algorithm(GA),particle swarm optimization(ACO)and ant colony algorithm(ACO),and the final results show that the algorithm has improved significantly in various indexes compared with other algorithms.