{"title":"Selecting vehicle dispatching plan for typhoon emergency evacuation based on fault-tolerance analysis","authors":"Zhao-ge Liu , Xiang-yang Li","doi":"10.1016/j.jnlssr.2025.01.005","DOIUrl":null,"url":null,"abstract":"<div><div>Unexpected scenarios often occur during typhoon response, which is likely to cause the failure of evacuation vehicle dispatching and other preparedness plans. To solve this problem, a vehicle dispatching plan selecting method based on fault-tolerance analysis is proposed, which considers the bounded rationality of emergency decision-makers. The method improves the capability of responding to unexpected scenarios by increasing backup resources. First, under the expected scenarios, a bi-level programming model for arranging the quantities of each type of vehicle and their routes is established, with the goal of minimizing the expected total evacuation time. A corresponding solving algorithm is designed. Second, possible unexpected scenarios are preset by integrating local and non-local historical experiences, and the scenario influences on vehicle dispatching constraints are analyzed. Third, under unexpected scenarios, a fault-tolerance plan set is established considering the failure risk of vehicle dispatching and fault-tolerant cost. The optimal plan is selected by calculating and ranking fault-tolerant rates. Finally, a case study in Shenzhen, China is provided to verify the reasonability and effectiveness of the method. The results show that the proposed method can help discover and address the ‘fault’ of vehicle dispatching plans during emergency preparedness and thus improve evacuation capabilities in emergency response. The proposed method can be used to develop evacuation vehicle dispatching planning methods with comprehensive scenario adaptability and a precisely improved capability.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100198"},"PeriodicalIF":3.7000,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"安全科学与韧性(英文)","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666449625000258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
Unexpected scenarios often occur during typhoon response, which is likely to cause the failure of evacuation vehicle dispatching and other preparedness plans. To solve this problem, a vehicle dispatching plan selecting method based on fault-tolerance analysis is proposed, which considers the bounded rationality of emergency decision-makers. The method improves the capability of responding to unexpected scenarios by increasing backup resources. First, under the expected scenarios, a bi-level programming model for arranging the quantities of each type of vehicle and their routes is established, with the goal of minimizing the expected total evacuation time. A corresponding solving algorithm is designed. Second, possible unexpected scenarios are preset by integrating local and non-local historical experiences, and the scenario influences on vehicle dispatching constraints are analyzed. Third, under unexpected scenarios, a fault-tolerance plan set is established considering the failure risk of vehicle dispatching and fault-tolerant cost. The optimal plan is selected by calculating and ranking fault-tolerant rates. Finally, a case study in Shenzhen, China is provided to verify the reasonability and effectiveness of the method. The results show that the proposed method can help discover and address the ‘fault’ of vehicle dispatching plans during emergency preparedness and thus improve evacuation capabilities in emergency response. The proposed method can be used to develop evacuation vehicle dispatching planning methods with comprehensive scenario adaptability and a precisely improved capability.