基于改进遗传算法的应急车辆调度研究  

Scheduling Optimization of Emergency Vehicle Based on Improved Genetic Algorithm

作　　者：吴凡[1] 杨冰 洪思 WU Fan;YANG Bing;HONG Si(School of Management Science and Engineering,Anhui University of Technology,Maanshan 243032,China)

机构地区：[1]安徽工业大学管理科学与工程学院,安徽马鞍山243032

出　　处：《数学的实践与认识》2021年第18期10-23,共14页Mathematics in Practice and Theory

基　　金：国家自然科学基金(61702006)。

摘　　要：近年来,突发事件频发,给人类生命财产安全带来了巨大挑战.如何实现及时高效的应急车辆调度,保证应急资源的合理分配,成为亟待解决的问题.本研究在综合考虑新冠肺炎疫情这类特殊突发事件特点的前提下,以配送成本最低、时间惩罚最少、配送员被感染风险最小为优化目标,构建了一类多目标优化调度模型.并针对模型设计了一种改进遗传算法,在一定程度上克服了传统遗传算法的早熟以及局部搜索能力较弱等问题,在相同数量级的运行时间内提高了算法的寻优性能.多次算例仿真结果对比表明,该算法相较于其他三类遗传算法寻优精度更高、收敛速度更快、搜索稳定性更好.In recent years,emergencies occur frequently,which are great challenges for the safety of human life and property.How to handle vehicle scheduling timely and efficiently and ensure the emergency resource allocation properly has become an important problem to be solved.Considering the characteristics of a special type of emergencies,such as COVID-19 epidemic situation,this research constructs a multi-objective scheduling model with the lowest logistic cost,the least time punishment and the minimum infection risk of dispatchers.Then an improved genetic algorithm is proposed,which can solve the premature and weak local search capacity problem of the traditional genetic algorithm to some extent and improve the optimization performance of the algorithm in the same order of running time.Finally,the simulation results of comparative experiments show that compared with the other three types of genetic algorithms,the algorithm proposed has higher optimization accuracy,faster convergence speed and better search stability.

关 键 词：应急车辆调度 新冠肺炎疫情 遗传算法 多目标优化 软时间窗 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]