{"title":"紧急疏散的巴士路线:瓦尔帕莱索大火的案例","authors":"Javiera Loyola Vitali, M. Riff, Elizabeth Montero","doi":"10.1109/CEC.2017.7969589","DOIUrl":null,"url":null,"abstract":"The Bus Evacuation Problem is a route planning problem, in the context of an evacuation in an emergency situation. Considering that public transport is available to support the evacuation, the objective of the problem is to determine the best route for each vehicle, to move all the people from a risk zone to open shelters located in safe zones, such that the evacuation time is minimized. In this work we present a method based on the Greedy Randomized Adaptive Search Procedure metaheuristic to solve the problem, in order to apply the solution to a real-world scenario based on a recent wildfire on Valparaíso, Chile. In computational experiments we demonstrate that our approach is effective to solve real-world size problems, and able to outperform a commercial MIP solver.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Bus Routing for emergency evacuations: The case of the Great Fire of Valparaiso\",\"authors\":\"Javiera Loyola Vitali, M. Riff, Elizabeth Montero\",\"doi\":\"10.1109/CEC.2017.7969589\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Bus Evacuation Problem is a route planning problem, in the context of an evacuation in an emergency situation. Considering that public transport is available to support the evacuation, the objective of the problem is to determine the best route for each vehicle, to move all the people from a risk zone to open shelters located in safe zones, such that the evacuation time is minimized. In this work we present a method based on the Greedy Randomized Adaptive Search Procedure metaheuristic to solve the problem, in order to apply the solution to a real-world scenario based on a recent wildfire on Valparaíso, Chile. In computational experiments we demonstrate that our approach is effective to solve real-world size problems, and able to outperform a commercial MIP solver.\",\"PeriodicalId\":335123,\"journal\":{\"name\":\"2017 IEEE Congress on Evolutionary Computation (CEC)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Congress on Evolutionary Computation (CEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2017.7969589\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Congress on Evolutionary Computation (CEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2017.7969589","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bus Routing for emergency evacuations: The case of the Great Fire of Valparaiso
The Bus Evacuation Problem is a route planning problem, in the context of an evacuation in an emergency situation. Considering that public transport is available to support the evacuation, the objective of the problem is to determine the best route for each vehicle, to move all the people from a risk zone to open shelters located in safe zones, such that the evacuation time is minimized. In this work we present a method based on the Greedy Randomized Adaptive Search Procedure metaheuristic to solve the problem, in order to apply the solution to a real-world scenario based on a recent wildfire on Valparaíso, Chile. In computational experiments we demonstrate that our approach is effective to solve real-world size problems, and able to outperform a commercial MIP solver.