Eleni Boumpa, Vasileios Tsoukas, Vasileios Chioktour, M. Kalafati, G. Spathoulas, Athanasios Kakarountas
{"title":"Smart Delivery for Goods Exploiting ML Algorithms","authors":"Eleni Boumpa, Vasileios Tsoukas, Vasileios Chioktour, M. Kalafati, G. Spathoulas, Athanasios Kakarountas","doi":"10.1145/3575879.3576009","DOIUrl":null,"url":null,"abstract":"This paper presents the work conducted on providing an efficient solution to the Vehicle Routing Problem considering all stages of a delivery process. The results are promising when compared to real-life data and depict the benefits of the application of ML algorithms in all stages of the process. A platform capable to monitor the various stages of the process was developed, and a number of ML models were incorporated to provide the best solution to a multiobjective problem. The results indicate on average a maximum of 25% distance saving, and a maximum of 14% regarding the total delivery time.","PeriodicalId":164036,"journal":{"name":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","volume":"104 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 26th Pan-Hellenic Conference on Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3575879.3576009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents the work conducted on providing an efficient solution to the Vehicle Routing Problem considering all stages of a delivery process. The results are promising when compared to real-life data and depict the benefits of the application of ML algorithms in all stages of the process. A platform capable to monitor the various stages of the process was developed, and a number of ML models were incorporated to provide the best solution to a multiobjective problem. The results indicate on average a maximum of 25% distance saving, and a maximum of 14% regarding the total delivery time.
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