{"title":"基于遗传算法的公交调度优化","authors":"Jiamei Wang, Dongxiu Ou, Decun Dong, Lun Zhang","doi":"10.1109/ICIEA.2010.5514792","DOIUrl":null,"url":null,"abstract":"In this paper, genetic algorithm is used to optimize the bus dispatching problem, which coordinates with the arrival of the passengers and improves service level by reducing the average passenger waiting time. The arrival distribution of the transfer passenger associates with the former transport modes in hub, and the discrete stochastic arrival distribution can be depicted by simulation. Firstly, the initial scheme should be chosen considering the bus operational schedule and search speed to seek an optimal solution. Then reasonable fitness function is build to select the fitter solution and ameliorated genetic operators—crossover and mutation are used to generate a second generation population of solution from those selected. Finally, the optimized schedule can be generated by these procedures and an example will be used to analyze the effectiveness of GAs.","PeriodicalId":234296,"journal":{"name":"2010 5th IEEE Conference on Industrial Electronics and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Bus dispatching optimization based on genetic algorithm\",\"authors\":\"Jiamei Wang, Dongxiu Ou, Decun Dong, Lun Zhang\",\"doi\":\"10.1109/ICIEA.2010.5514792\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, genetic algorithm is used to optimize the bus dispatching problem, which coordinates with the arrival of the passengers and improves service level by reducing the average passenger waiting time. The arrival distribution of the transfer passenger associates with the former transport modes in hub, and the discrete stochastic arrival distribution can be depicted by simulation. Firstly, the initial scheme should be chosen considering the bus operational schedule and search speed to seek an optimal solution. Then reasonable fitness function is build to select the fitter solution and ameliorated genetic operators—crossover and mutation are used to generate a second generation population of solution from those selected. Finally, the optimized schedule can be generated by these procedures and an example will be used to analyze the effectiveness of GAs.\",\"PeriodicalId\":234296,\"journal\":{\"name\":\"2010 5th IEEE Conference on Industrial Electronics and Applications\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 5th IEEE Conference on Industrial Electronics and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2010.5514792\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 5th IEEE Conference on Industrial Electronics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2010.5514792","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bus dispatching optimization based on genetic algorithm
In this paper, genetic algorithm is used to optimize the bus dispatching problem, which coordinates with the arrival of the passengers and improves service level by reducing the average passenger waiting time. The arrival distribution of the transfer passenger associates with the former transport modes in hub, and the discrete stochastic arrival distribution can be depicted by simulation. Firstly, the initial scheme should be chosen considering the bus operational schedule and search speed to seek an optimal solution. Then reasonable fitness function is build to select the fitter solution and ameliorated genetic operators—crossover and mutation are used to generate a second generation population of solution from those selected. Finally, the optimized schedule can be generated by these procedures and an example will be used to analyze the effectiveness of GAs.