{"title":"在线车辆调度:从分配到调度","authors":"Kangjia Zhao, Wenqing Chen, Kong-wei Lye","doi":"10.1109/ICDMW.2018.00094","DOIUrl":null,"url":null,"abstract":"The available prior demand data will make it possible for the ride hailing platform to make the central control strategies, which plan a sequence of trips for drivers in a certain future time period, so that a system optimal vehicle dispatch could be achieved. However, handling a large scale booking requests within the restrictive computing time to achieve an optimal vehicle dispatch is a big challenge. This paper proposes an optimization framework for the online vehicle dispatch problem by adopting vehicle scheduling methodology. A novel approach is introduced to solve the optimization challenges of the large problem size and the limited computing time. The designed optimization framework is validated by the real-world demand data in Singapore.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Online Vehicle Dispatch: from Assignment to Scheduling\",\"authors\":\"Kangjia Zhao, Wenqing Chen, Kong-wei Lye\",\"doi\":\"10.1109/ICDMW.2018.00094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The available prior demand data will make it possible for the ride hailing platform to make the central control strategies, which plan a sequence of trips for drivers in a certain future time period, so that a system optimal vehicle dispatch could be achieved. However, handling a large scale booking requests within the restrictive computing time to achieve an optimal vehicle dispatch is a big challenge. This paper proposes an optimization framework for the online vehicle dispatch problem by adopting vehicle scheduling methodology. A novel approach is introduced to solve the optimization challenges of the large problem size and the limited computing time. The designed optimization framework is validated by the real-world demand data in Singapore.\",\"PeriodicalId\":259600,\"journal\":{\"name\":\"2018 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2018.00094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2018.00094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Online Vehicle Dispatch: from Assignment to Scheduling
The available prior demand data will make it possible for the ride hailing platform to make the central control strategies, which plan a sequence of trips for drivers in a certain future time period, so that a system optimal vehicle dispatch could be achieved. However, handling a large scale booking requests within the restrictive computing time to achieve an optimal vehicle dispatch is a big challenge. This paper proposes an optimization framework for the online vehicle dispatch problem by adopting vehicle scheduling methodology. A novel approach is introduced to solve the optimization challenges of the large problem size and the limited computing time. The designed optimization framework is validated by the real-world demand data in Singapore.