{"title":"Routing Optimization of Regional Logistics Vehicles Based on Distribution Information Matching","authors":"W. Lu, Chen Youling","doi":"10.1145/3366194.3366204","DOIUrl":null,"url":null,"abstract":"Based on the new perspective of customer location and distribution demand of regional logistics distribution center, this paper integrates the similarity of customer attributes to optimize the matching of vehicle and customer information, and effectively avoids the unreasonable distribution of the number of vehicles and the frequency of delivery. At the same time, when the influence of vehicle speed, load weight and node distance on vehicle energy consumption are considered, a distribution time model with soft time window constraints is established, and the ant colony algorithm for node reversed processing is designed to solve the problem. The results show that the proposed method can reasonably plan the distribution of vehicles and achieve path optimization.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3366194.3366204","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Based on the new perspective of customer location and distribution demand of regional logistics distribution center, this paper integrates the similarity of customer attributes to optimize the matching of vehicle and customer information, and effectively avoids the unreasonable distribution of the number of vehicles and the frequency of delivery. At the same time, when the influence of vehicle speed, load weight and node distance on vehicle energy consumption are considered, a distribution time model with soft time window constraints is established, and the ant colony algorithm for node reversed processing is designed to solve the problem. The results show that the proposed method can reasonably plan the distribution of vehicles and achieve path optimization.