{"title":"Optimization Algorithm for Urban Rail Transit Operation Scheduling based on Linear Programming","authors":"Shuang Wu, Jinlong Wu, Yifeng Sun, Tong Yao","doi":"10.12694/scpe.v24i3.2245","DOIUrl":null,"url":null,"abstract":"At present, the traditional urban public transportation system cannot meet people’s daily travel needs. Urban Rail Transit (URT) has been rapidly promoted in major cities due to its advantages such as low energy consumption, high frequency, and large traffic volume. To achieve a more excellent and energy-saving operation scheduling strategy, the research first combines the train dynamics model and the energy consumption model. Since the optimization problem of URT is a linear problem, the attraction model of the Firefly algorithm can determine the calculation time consumed by the algorithm, which is very suitable for the complex optimization problem of URT. Therefore, the FA based optimization algorithm for urban rail transit operation scheduling (FURTOSO) based on the Firefly algorithm is studied and designed. Therefore, based on the study of the four working conditions of traction, cruise, coasting, and braking, a Firefly Algorithm for Urban Rail Transit Operation Scheduling (FURTOSO) was designed. Finally, the study optimizes the operation scheduling of Chengdu Metro Line 8 from two aspects: driving strategy and train schedule. The research demonstrates that the FURTOSO algorithm only needs 76 iterations to reach a stable state, with a fitness value of 0.6827. In practical applications, the utilization rate of train RBE is 30.1%, the total energy consumption (TEC) is 2.661 * 1011J, and the energy saving rate is 13.03%. In summary, the FURTOSO algorithm proposed in the study has excellent performance and has better energy-saving effects in Chengdu Metro Line 8.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"30 1","pages":"0"},"PeriodicalIF":0.9000,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scalable Computing-Practice and Experience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12694/scpe.v24i3.2245","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
At present, the traditional urban public transportation system cannot meet people’s daily travel needs. Urban Rail Transit (URT) has been rapidly promoted in major cities due to its advantages such as low energy consumption, high frequency, and large traffic volume. To achieve a more excellent and energy-saving operation scheduling strategy, the research first combines the train dynamics model and the energy consumption model. Since the optimization problem of URT is a linear problem, the attraction model of the Firefly algorithm can determine the calculation time consumed by the algorithm, which is very suitable for the complex optimization problem of URT. Therefore, the FA based optimization algorithm for urban rail transit operation scheduling (FURTOSO) based on the Firefly algorithm is studied and designed. Therefore, based on the study of the four working conditions of traction, cruise, coasting, and braking, a Firefly Algorithm for Urban Rail Transit Operation Scheduling (FURTOSO) was designed. Finally, the study optimizes the operation scheduling of Chengdu Metro Line 8 from two aspects: driving strategy and train schedule. The research demonstrates that the FURTOSO algorithm only needs 76 iterations to reach a stable state, with a fitness value of 0.6827. In practical applications, the utilization rate of train RBE is 30.1%, the total energy consumption (TEC) is 2.661 * 1011J, and the energy saving rate is 13.03%. In summary, the FURTOSO algorithm proposed in the study has excellent performance and has better energy-saving effects in Chengdu Metro Line 8.
期刊介绍:
The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.