{"title":"Real-Time Schedule Optimization in Shared Electric Vehicle Fleets","authors":"Falko Koetter, J. Ostermann","doi":"10.5220/0005754602530263","DOIUrl":null,"url":null,"abstract":"Use of electric vehicles in corporate carsharing has become a promising option. However, to make the use of electric vehicles economically feasible, a high degree of utilization is necessary. In the Shared E-Fleet project, solutions for shared car fleets are being researched, increasing utilization by sharing cars among different companies. In this work, we present a process and algorithms for real-time vehicle schedule optimization, aiming to minimize manual scheduling work, to optimize the schedule towards a goal function (e.g. minimizing emissions) and to compensate disruptions in real-time. We evaluate the approach using synthetic data and model trials, showing that schedule optimization increases utilization as well as quality-of-service.","PeriodicalId":218840,"journal":{"name":"International Conference on Vehicle Technology and Intelligent Transport Systems","volume":"23 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Vehicle Technology and Intelligent Transport Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0005754602530263","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
Use of electric vehicles in corporate carsharing has become a promising option. However, to make the use of electric vehicles economically feasible, a high degree of utilization is necessary. In the Shared E-Fleet project, solutions for shared car fleets are being researched, increasing utilization by sharing cars among different companies. In this work, we present a process and algorithms for real-time vehicle schedule optimization, aiming to minimize manual scheduling work, to optimize the schedule towards a goal function (e.g. minimizing emissions) and to compensate disruptions in real-time. We evaluate the approach using synthetic data and model trials, showing that schedule optimization increases utilization as well as quality-of-service.