A data-driven hybrid scenario-based robust optimization method for relief logistics network design

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dc.authorscopusidReza Tavakkoli Moghaddam / 57207533714
dc.authorwosidReza Tavakkoli Moghaddam / P-1948-2015
dc.contributor.authorAmin Amani, Mohammad
dc.contributor.authorAsumadu Sarkodie, Samuel
dc.contributor.authorSheu, Jiuh Biing
dc.contributor.authorMahdi Nasiri, Mohammad
dc.contributor.authorTavakkoli Moghaddam, Reza
dc.date.accessioned2025-04-18T08:23:10Z
dc.date.available2025-04-18T08:23:10Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThe incorporation of artificial intelligence (AI) and robust optimization methods for the planning and design of relief logistics networks under relief demand–supply uncertainty appears promising for intelligent disaster management (IDM). This research proposes a data-driven hybrid scenario-based robust (SBR) method for a mixed integer second-order cone programming (MISOCP) model that integrates machine learning with a hybrid robust optimization approach to address the above issue. A machine learning technique is utilized to cluster the casualties based on location coordinates and injury severity score. Moreover, the hybrid SBR optimization method and robust optimization based on the uncertainty sets technique are utilized to cope with uncertain parameters such as the probability of facility disruption, the number of wounded individuals, transportation time, and relief demand. Additionally, the epsilon-constraint technique is applied to seek the solution for the bi-objective model. Focusing on a real case (the Kermanshah disaster), our analytical results have demonstrated not only the validity but also the relative merits of the proposed methodology against typical stochastic and robust optimization approaches. Besides, the proposed method shows all casualties can be efficiently transported to receive medical services at a fair cost, which is crucial for disaster management. © 2024 Elsevier Ltd
dc.description.sponsorshipThis research is supported partly by grant NSC 112-2410-H-002-046-MY3 from the National Science Council of Taiwan, Taiwan, R.O.C. The author would like to thank the handling editor and referees for their helpful comments and suggestions. Any errors or omissions remain the sole responsibility of the authors.
dc.identifier.citationAmani, M. A., Sarkodie, S. A., Sheu, J. B., Nasiri, M. M., & Tavakkoli-Moghaddam, R. (2025). A data-driven hybrid scenario-based robust optimization method for relief logistics network design. Transportation Research Part E: Logistics and Transportation Review, 194, 103931.
dc.identifier.doi10.1016/j.tre.2024.103931
dc.identifier.issn13665545
dc.identifier.scopus2-s2.0-85211593285
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.tre.2024.103931
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6553
dc.identifier.volume194
dc.indekslendigikaynakScopus
dc.institutionauthorTavakkoli Moghaddam, Reza
dc.institutionauthoridReza Tavakkoli Moghaddam / 0000-0002-6757-926X
dc.language.isoen
dc.publisherElsevier Ltd.
dc.relation.ispartofTransportation Research Part E: Logistics and Transportation Review
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFacility Disruption
dc.subjectHumanitarian Relief Logistics
dc.subjectIntelligent Disaster Management
dc.subjectMachine Learning
dc.subjectRobust Optimization
dc.titleA data-driven hybrid scenario-based robust optimization method for relief logistics network design
dc.typeArticle

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