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dc.contributor.authorAkbari, Vahid
dc.contributor.authorShiri, Davood
dc.contributor.authorSalman, F. Sibel
dc.date.accessioned2021-06-25T06:30:07Z
dc.date.available2021-06-25T06:30:07Z
dc.date.issued2021en_US
dc.identifier.citationAkbari, V., Shiri, D. ve Salman, F. S. (2021). An online optimization approach to post-disaster road restoration. Transportation Research Part B: Methodological, 150, 1-25. https://dx.doi.org/10.1016/j.trb.2021.05.017en_US
dc.identifier.issn0191-2615
dc.identifier.issn1879-2367
dc.identifier.urihttps://dx.doi.org/10.1016/j.trb.2021.05.017
dc.identifier.urihttps://hdl.handle.net/20.500.12511/7313
dc.description.abstractNatural disasters impact transportation networks adversely and cause road sections to be damaged or blocked. The road network may even become disconnected, impeding accessibility between disaster-stricken areas and critical locations such as hospitals, relief aid depots and transportation hubs. In the immediate response phase, a set of blocked edges should be selected and restored to reconnect the transportation network. While locations of the disrupted roads can be identified using drone or satellite images, an accurate estimation of time to restore a road segment can be carried out only after expert observations on the field. In this article, we study a post-disaster road restoration problem modeled on an undirected edge-weighted graph with k blocked edges, where the unblocking time of a blocked edge is revealed online once the road restoration team visits an end-node of that blocked edge. The objective is to minimize the time at which the road network is reconnected. We first investigate the worst-case performance of online algorithms against offline optimal solutions by means of the competitive ratio. We prove that any online deterministic algorithm cannot achieve a competitive ratio better than 2k−1. We also provide an optimal online algorithm that is proven to achieve this lower bound. In addition, to achieve good performance on realistic instances, we implement an algorithm that solves a mixed integer programming model each time new information is revealed. Since model solution is prohibitively time-consuming, we also propose a novel polynomial time online algorithm. We compare these two algorithms with two other benchmark online algorithms on both Istanbul road network instances and several other city instances from the literature. Our experiments show that the proposed polynomial time online algorithm performs superior to the benchmark ones and obtains solutions close to the offline optimum on all the tested instances.en_US
dc.language.isoengen_US
dc.publisherElsevier Ltden_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectCompetitive Ratioen_US
dc.subjectDisaster Response Logisticsen_US
dc.subjectNetwork Restorationen_US
dc.subjectOnline Optimizationen_US
dc.subjectRoad Clearanceen_US
dc.titleAn online optimization approach to post-disaster road restorationen_US
dc.typearticleen_US
dc.relation.ispartofTransportation Research Part B: Methodologicalen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.identifier.volume150en_US
dc.identifier.startpage1en_US
dc.identifier.endpage25en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.trb.2021.05.017en_US
dc.identifier.wosqualityQ1en_US
dc.identifier.scopusqualityQ1en_US


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