{"title":"A distributionally robust optimisation with joint chance constraints approach for location-routing problem in urban search and rescue operations","authors":"Kamran Sarmadi , Mehdi Amiri-Aref","doi":"10.1016/j.cor.2025.107051","DOIUrl":null,"url":null,"abstract":"<div><div>This paper examines a multi-period location-routing problem with uncertain demand and travel times in the context of disaster management. We propose an optimisation model that integrates strategic location decisions with multi-period routing decisions to navigate search-and-rescue teams in the aftermath of a disaster within an uncertain environment. To model this problem, we apply a distributionally robust optimisation approach with joint chance constraints. We enhance computational tractability by reformulating the problem using Bonferroni’s inequality and approximating the chance constraints. The proposed methodology is evaluated in a hypothetical case study of Santa Cruz County, California, USA, a region highly susceptible to earthquakes. We tested multiple instances of this case study to demonstrate the effectiveness of the proposed method compared to the sample average approximation approach. Numerical experiments reveal that the methodology developed in this paper aids decision-makers in strategically locating facilities to deploy search-and-rescue teams and efficiently directing them towards affected sites, achieving a maximal rescue rate.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107051"},"PeriodicalIF":4.1000,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Operations Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305054825000796","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper examines a multi-period location-routing problem with uncertain demand and travel times in the context of disaster management. We propose an optimisation model that integrates strategic location decisions with multi-period routing decisions to navigate search-and-rescue teams in the aftermath of a disaster within an uncertain environment. To model this problem, we apply a distributionally robust optimisation approach with joint chance constraints. We enhance computational tractability by reformulating the problem using Bonferroni’s inequality and approximating the chance constraints. The proposed methodology is evaluated in a hypothetical case study of Santa Cruz County, California, USA, a region highly susceptible to earthquakes. We tested multiple instances of this case study to demonstrate the effectiveness of the proposed method compared to the sample average approximation approach. Numerical experiments reveal that the methodology developed in this paper aids decision-makers in strategically locating facilities to deploy search-and-rescue teams and efficiently directing them towards affected sites, achieving a maximal rescue rate.
期刊介绍:
Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.