A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: an interactive possibilistic programming approach for a real case study

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210en_US
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874en_US
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorGolpîra, Hêris
dc.contributor.authorJavanmardan, Ahvan
dc.contributor.authorMaihami, Reza
dc.date.accessioned2022-11-08T13:12:50Z
dc.date.available2022-11-08T13:12:50Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractIn uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.en_US
dc.identifier.citationTirkolaee, E. B., Golpîra, H., Javanmardan, A., & Maihami, R. (2022). A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study. Socio-Economic Planning Sciences, doi:10.1016/j.seps.2022.101439en_US
dc.identifier.doi10.1016/j.seps.2022.101439en_US
dc.identifier.scopus2-s2.0-85139054794en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.seps.2022.101439
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3257
dc.identifier.wosWOS:000926439700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherElsevier Ltd.en_US
dc.relation.ispartofSocio-Economic Planning Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBlood Supply Chain Network Designen_US
dc.subjectCOVID-19 Pandemicen_US
dc.subjectInteractive Possibilistic Programmingen_US
dc.subjectJob Opportunityen_US
dc.subjectSocio-economic Optimizationen_US
dc.subjectUncertaintyen_US
dc.titleA socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: an interactive possibilistic programming approach for a real case studyen_US
dc.typeArticleen_US

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