换电模式下电动车货运路径优化模型与算法  

Freight routing optimization model and algorithm of battery-swapping electric vehicle

作　　者：李进 王凤[1] 杨沈宇 LI Jin;WANG Feng;YANG Shenyu(School of Management and E-Business,Zhejiang Gongshang University,Hangzhou Zhejiang 310018,China;Center of Modern Business Research,Zhejiang Gongshang University,Hangzhou Zhejiang 310018,China)

机构地区：[1]浙江工商大学管理工程与电子商务学院,杭州310018 [2]浙江工商大学现代商贸研究中心,杭州310018

出　　处：《计算机应用》2021年第6期1792-1798,共7页journal of Computer Applications

基　　金：浙江省社科规划课题(18NDJC180YB);浙江省高校重大人文社科攻关计划项目(2018QN007);浙江省自然科学基金资助项目(LY20G020006);国家社会科学基金资助项目(19BGL194)。

摘　　要：针对考虑电池续航能力和换电站约束的电动车货运路径优化问题,提出考虑速度、载重和距离等多因素的电动车碳排放计算方法。首先,以耗电量和旅行时间费用最小化为目标,建立混合整数规划模型;然后,在爬山优化和换电邻域搜索的基础上提出一种自适应遗传算法,并设计随种群适应度变化而自适应调整的交叉和变异概率;最后,采用爬山搜索加强算法的局部搜索能力,并设计电动车换电邻域搜索策略对最优解进行进一步的改进,以满足电池续航能力和换电站约束,得到最优可行解。实验结果表明:相较于传统的遗传算法,自适应遗传算法能够更快速有效地找到满意解;考虑耗电量和旅行时间的路径安排能够减少货运配送的碳排放和总费用;与固定的交叉和变异概率参数设置相比,自适应参数调节方法能够更有效防止局部优化问题,提高算法的全局搜索能力。To address the electric vehicle freight routing optimization problem considering the constrains of battery life and battery-swapping stations,a calculation method of electric vehicle carbon emissions considering multiple factors such as speed,load and distance was proposed.Firstly,with the goal of minimizing power consumption and travel time cost,a mixed integer programming model was established.Then,an adaptive genetic algorithm was proposed based on the mountain-climb optimization and batter-swapping neighborhood searching,and the crossover and mutation probabilities adaptively adjusting with the change of the population fitness were designed.Finally,the mountain-climb searching was used to enhance the local search capability of the algorithm.And the battery-swapping neighborhood searching strategy for the electric vehicle was designed to further improve the optimal solution,so as to meet the constraints of battery life and battery-swapping stations and obtain the final optimal feasible solution.The experimental results show that,the adaptive genetic algorithm can find satisfactory solution more quickly and effectively compared to the traditional genetic algorithm;the route arrangement considering power consumption and travel time can reduce the carbon emissions and total freight distribution costs;compared with the fixed parameter setting of the crossover and mutation probabilities,the adaptive parameter adjustment method can more effectively avoid the local optimum and improve the global search ability of the algorithm.

关 键 词：车辆路径问题 换电式电动车 碳排放 遗传算法 计算机仿真 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]