{"title":"物流配送车辆路径规划研究","authors":"Changhao Piao, Hao Hu, Yan Zhang","doi":"10.1109/ICAIIS49377.2020.9194800","DOIUrl":null,"url":null,"abstract":"Aiming at the problem of vehicle routing in logistics distribution, an improved ant colony optimization algorithm was proposed. In the distribution process, shortening the delivery mileage minimizes the path Introduction. By establishing a corresponding matlab distribution model, an improved ant colony algorithm is used to solve the optimal path. The improved ant colony algorithm sets the volatility factor according to the search stage, and considers the starting point, the end point and the distance between each node in the heuristic factor. The experimental results show that compared with the traditional manual scheme, the logistics distribution path planning model and the improved ant colony algorithm are adopted to effectively reduce the logistics distribution operation path.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Logistics distribution vehicle path planning research\",\"authors\":\"Changhao Piao, Hao Hu, Yan Zhang\",\"doi\":\"10.1109/ICAIIS49377.2020.9194800\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problem of vehicle routing in logistics distribution, an improved ant colony optimization algorithm was proposed. In the distribution process, shortening the delivery mileage minimizes the path Introduction. By establishing a corresponding matlab distribution model, an improved ant colony algorithm is used to solve the optimal path. The improved ant colony algorithm sets the volatility factor according to the search stage, and considers the starting point, the end point and the distance between each node in the heuristic factor. The experimental results show that compared with the traditional manual scheme, the logistics distribution path planning model and the improved ant colony algorithm are adopted to effectively reduce the logistics distribution operation path.\",\"PeriodicalId\":416002,\"journal\":{\"name\":\"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIIS49377.2020.9194800\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIIS49377.2020.9194800","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Logistics distribution vehicle path planning research
Aiming at the problem of vehicle routing in logistics distribution, an improved ant colony optimization algorithm was proposed. In the distribution process, shortening the delivery mileage minimizes the path Introduction. By establishing a corresponding matlab distribution model, an improved ant colony algorithm is used to solve the optimal path. The improved ant colony algorithm sets the volatility factor according to the search stage, and considers the starting point, the end point and the distance between each node in the heuristic factor. The experimental results show that compared with the traditional manual scheme, the logistics distribution path planning model and the improved ant colony algorithm are adopted to effectively reduce the logistics distribution operation path.