{"title":"通过地铁系统时刻表优化最大化可再生能源利用","authors":"Hongjie Liu, B. Ning, T. Tang, Xiwang Guo","doi":"10.1109/ICIRT.2018.8641588","DOIUrl":null,"url":null,"abstract":"Maximizing regenerative energy utilization (REU) has become a hot topic in subway systems recently. This paper proposes a timetable optimization problem to maximize REU with the headway and dwell time control, which coordinates the traction and braking trains in each substation. The mathematical model are formulated, and some realistic constraints are considered. An improved artificial bee colony algorithm (IABCA) is designed to solve our problem, the pseudo-code of its main process and the generation of a feasible solution are presented. Numerical experiments based on the data from a subway line in China are conducted, and IABCA is compared with a genetic algorithm. Experimental results prove the correctness of the mathematical model and the effectiveness of IABCA.","PeriodicalId":202415,"journal":{"name":"2018 International Conference on Intelligent Rail Transportation (ICIRT)","volume":"307 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Maximize Regenerative Energy Utilization through Timetable Optimization in a Subway System\",\"authors\":\"Hongjie Liu, B. Ning, T. Tang, Xiwang Guo\",\"doi\":\"10.1109/ICIRT.2018.8641588\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Maximizing regenerative energy utilization (REU) has become a hot topic in subway systems recently. This paper proposes a timetable optimization problem to maximize REU with the headway and dwell time control, which coordinates the traction and braking trains in each substation. The mathematical model are formulated, and some realistic constraints are considered. An improved artificial bee colony algorithm (IABCA) is designed to solve our problem, the pseudo-code of its main process and the generation of a feasible solution are presented. Numerical experiments based on the data from a subway line in China are conducted, and IABCA is compared with a genetic algorithm. Experimental results prove the correctness of the mathematical model and the effectiveness of IABCA.\",\"PeriodicalId\":202415,\"journal\":{\"name\":\"2018 International Conference on Intelligent Rail Transportation (ICIRT)\",\"volume\":\"307 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Intelligent Rail Transportation (ICIRT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIRT.2018.8641588\",\"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 International Conference on Intelligent Rail Transportation (ICIRT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIRT.2018.8641588","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Maximize Regenerative Energy Utilization through Timetable Optimization in a Subway System
Maximizing regenerative energy utilization (REU) has become a hot topic in subway systems recently. This paper proposes a timetable optimization problem to maximize REU with the headway and dwell time control, which coordinates the traction and braking trains in each substation. The mathematical model are formulated, and some realistic constraints are considered. An improved artificial bee colony algorithm (IABCA) is designed to solve our problem, the pseudo-code of its main process and the generation of a feasible solution are presented. Numerical experiments based on the data from a subway line in China are conducted, and IABCA is compared with a genetic algorithm. Experimental results prove the correctness of the mathematical model and the effectiveness of IABCA.
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