基于GA-PSO算法的物流纯电动汽车路径选择

IF 0.6 4区 工程技术 Q4 MECHANICS Mechanika Pub Date : 2023-06-17 DOI:10.5755/j02.mech.31954
M. Wang, Qiyue Xie
{"title":"基于GA-PSO算法的物流纯电动汽车路径选择","authors":"M. Wang, Qiyue Xie","doi":"10.5755/j02.mech.31954","DOIUrl":null,"url":null,"abstract":" Abstract:Based on current energy storage technologies such as batteries and fuel cells, the inherent battery capacity of electric vehicles puts constraints on their driving range and requires charging in the process of completing driving tasks. In this paper, with the current practical application in logistics industry as the background, from electric vehicle charging scheduling and path planning, a hybrid algorithm combining genetic-particle swarm algorithm is proposed to plan the best driving route for a group of electric logistics vehicles with vehicle load, vehicle battery life, charging facility location and customer time window as constraints and the total cost as the objective function. Based on the single distribution center, a more complex multi-distribution center electric vehicle path planning problem is considered. In this paper, multiple sets of Solomon VRPTW data sets are selected to test the prepared algorithm, and the results show that the algorithm can effectively plan the best distribution scheme.","PeriodicalId":54741,"journal":{"name":"Mechanika","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Logistics Pure Electric Vehicle Routing Based on GA-PSO Algorithm\",\"authors\":\"M. Wang, Qiyue Xie\",\"doi\":\"10.5755/j02.mech.31954\",\"DOIUrl\":null,\"url\":null,\"abstract\":\" Abstract:Based on current energy storage technologies such as batteries and fuel cells, the inherent battery capacity of electric vehicles puts constraints on their driving range and requires charging in the process of completing driving tasks. In this paper, with the current practical application in logistics industry as the background, from electric vehicle charging scheduling and path planning, a hybrid algorithm combining genetic-particle swarm algorithm is proposed to plan the best driving route for a group of electric logistics vehicles with vehicle load, vehicle battery life, charging facility location and customer time window as constraints and the total cost as the objective function. Based on the single distribution center, a more complex multi-distribution center electric vehicle path planning problem is considered. In this paper, multiple sets of Solomon VRPTW data sets are selected to test the prepared algorithm, and the results show that the algorithm can effectively plan the best distribution scheme.\",\"PeriodicalId\":54741,\"journal\":{\"name\":\"Mechanika\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mechanika\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.5755/j02.mech.31954\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanika","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5755/j02.mech.31954","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MECHANICS","Score":null,"Total":0}
引用次数: 0

摘要

摘要:基于当前电池和燃料电池等储能技术,电动汽车固有的电池容量限制了其续航里程,并需要在完成驾驶任务的过程中充电。本文以当前物流行业的实际应用为背景,从电动汽车充电调度和路径规划两个方面,提出了一种结合遗传粒子群算法的混合算法,以规划一组电动物流车在车辆负载、车辆电池寿命、,以充电设施位置和客户时间窗口为约束条件,以总成本为目标函数。在单个配送中心的基础上,考虑了一个更复杂的多配送中心电动汽车路径规划问题。本文选取了多组Solomon VRPTW数据集对所编制的算法进行了测试,结果表明该算法能够有效地规划最佳分配方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Logistics Pure Electric Vehicle Routing Based on GA-PSO Algorithm
 Abstract:Based on current energy storage technologies such as batteries and fuel cells, the inherent battery capacity of electric vehicles puts constraints on their driving range and requires charging in the process of completing driving tasks. In this paper, with the current practical application in logistics industry as the background, from electric vehicle charging scheduling and path planning, a hybrid algorithm combining genetic-particle swarm algorithm is proposed to plan the best driving route for a group of electric logistics vehicles with vehicle load, vehicle battery life, charging facility location and customer time window as constraints and the total cost as the objective function. Based on the single distribution center, a more complex multi-distribution center electric vehicle path planning problem is considered. In this paper, multiple sets of Solomon VRPTW data sets are selected to test the prepared algorithm, and the results show that the algorithm can effectively plan the best distribution scheme.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Mechanika
Mechanika 物理-力学
CiteScore
1.30
自引率
0.00%
发文量
50
审稿时长
3 months
期刊介绍: The journal is publishing scientific papers dealing with the following problems: Mechanics of Solid Bodies; Mechanics of Fluids and Gases; Dynamics of Mechanical Systems; Design and Optimization of Mechanical Systems; Mechanical Technologies.
期刊最新文献
Nonlinear vibration characteristics and bifurcation control of a class of piecewise constrained systems with dynamic clearances Model Updating Based on Bayesian Theory and Improved Objective Function Design and FEM Analysis of Plastic Parts of a Tie-Rod Composite Hydraulic Cylinder Real-Time Energy Consumption Sensing System in SMT Intelligent Workshop Research on Bionic Hierarchical Optimization of Wing Based on PLSR and PSO
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1