Publication:
Emergency facility location under random network damage: insights from the Istanbul case

dc.contributor.departmentDepartment of Industrial Engineering
dc.contributor.departmentN/A
dc.contributor.kuauthorSalman, Fatma Sibel
dc.contributor.kuauthorYücel, Eda
dc.contributor.kuprofileFaculty Member
dc.contributor.kuprofilePhD Student
dc.contributor.otherDepartment of Industrial Engineering
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGraduate School of Sciences and Engineering
dc.contributor.yokid178838
dc.contributor.yokid235501
dc.date.accessioned2024-11-09T23:54:48Z
dc.date.issued2015
dc.description.abstractDamage to infrastructure, especially to highways and roads, adversely affects accessibility to disaster areas. Predicting accessibility to demand points from the supply points by a systematic model would lead to more effective emergency facility location decisions. To this effect, we model the spatial impact of the disaster on network links by random failures with dependency such that failure of a link induces failure of nearby links that are structurally more vulnerable. For each demand point, a set of alternative paths is generated from each potential supply point so that the shortest surviving path will be used for relief transportation after the disaster. The objective is to maximize the expected demand coverage within a specified distance over all possible network realizations. To overcome the computational difficulty caused by extremely large number of possible outcomes, we propose a tabu search heuristic that evaluates candidate solutions over a sample of network scenarios. The scenario generation algorithm that represents the proposed distance and vulnerability based failure model is the main contribution of our study. The tabu search algorithm is applied to Istanbul earthquake preparedness case with a detailed analysis comparing solutions found in no link failure, independent link failure, and dependent link failure cases. The results show that incorporating dependent link failures to the model improves the covered demand percentages significantly.
dc.description.indexedbyWoS
dc.description.indexedbyScopus
dc.description.openaccessNO
dc.description.sponsoredbyTubitakEuTÜBİTAK
dc.description.sponsorshipTubitak This study was financially supported by a Tubitak grant. Assistance of I. Arsik, O. C. Binatli, A. Kibar, S. Ozdinc and O. Saracoglu in data preparation, and the comments and suggestions of the reviewers that helped to improve the presentation of the article are gratefully appreciated.
dc.description.volume62
dc.identifier.doi10.1016/j.cor.2014.07.015
dc.identifier.eissn1873-765X
dc.identifier.issn0305-0548
dc.identifier.scopus2-s2.0-84937966705
dc.identifier.urihttp://dx.doi.org/10.1016/j.cor.2014.07.015
dc.identifier.urihttps://hdl.handle.net/20.500.14288/15260
dc.identifier.wos357224000022
dc.keywordsFacilities planning and design
dc.keywordsHumanitarian logistics
dc.keywordsDisaster preparedness
dc.keywordsFacility location
dc.keywordsLink failures
dc.keywordsSpatial and structural correlation
dc.keywordsSearch procedure
dc.keywordsModel
dc.keywordsReliability
dc.keywordsRisk
dc.languageEnglish
dc.publisherPergamon-Elsevier Science Ltd
dc.sourceComputers and Operations Research
dc.subjectComputer Science
dc.subjectArtificial intelligence
dc.subjectIndustrial engineering
dc.subjectOperations research
dc.subjectManagement science
dc.titleEmergency facility location under random network damage: insights from the Istanbul case
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.authorid0000-0001-6833-2552
local.contributor.authorid0000-0002-3448-1522
local.contributor.kuauthorSalman, Fatma Sibel
local.contributor.kuauthorYücel, Eda
relation.isOrgUnitOfPublicationd6d00f52-d22d-4653-99e7-863efcd47b4a
relation.isOrgUnitOfPublication.latestForDiscoveryd6d00f52-d22d-4653-99e7-863efcd47b4a

Files