A DEA-based simulation-optimisation approach to design a resilience plasma supply chain network: a case study of the COVID-19 outbreak

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dc.authoridSimic, Vladimir/0000-0001-5709-3744
dc.authoridGhasemi, Peiman/0000-0002-1085-0776
dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridGoodarzian, Fariba/0000-0001-9295-1839
dc.authorwosidSimic, Vladimir/B-8837-2011
dc.authorwosidGhasemi, Peiman/E-8340-2016
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.contributor.authorGhasemi, Peiman
dc.contributor.authorGoodarzian, Fariba
dc.contributor.authorSimic, Vladimir
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2024-05-19T14:46:13Z
dc.date.available2024-05-19T14:46:13Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis study develops a novel multi-objective mathematical model for a Plasma Supply Chain Network (PSCN) in order to maximise the coverage of blood donors during periods and minimise the blood transportation costs between different nodes, relocation cost of temporary mobile facilities, inventory holding cost of the blood, and the costs of newly established blood centres. Therefore, the major contribution of this work is the simultaneous consideration of resiliency and efficiency in the proposed PCN during the COVID-19 outbreak. To address the uncertain parameters, Stochastic Chance-Constrained Programming (SCCP) method is applied to the model. Additionally, to solve the PSCN model, the & epsilon;-constraint method is employed for small- and medium-sized problems and then a multi-objective invasive weed optimisation (MOIWO) algorithm is implemented for large-sized problems. To validate the suggested methodology, a variety of problem instances is designed and solved using the solution techniques, considering two assessment metrics of Hyper Volume (HV) and Min Ideal Distance (MID). Moreover, a real case study and sensitivity analyses on significant parameters are conducted to configure the optimal network. Eventually, the obtained results are examined and useful decision aids are suggested.en_US
dc.description.sponsorshipFWF Austrian Science Fund [I 5908-G]en_US
dc.description.sponsorshipThis work was partially supported by the FWF Austrian Science Fund (Peiman Ghasemi) [I 5908-G].en_US
dc.identifier.doi10.1080/23302674.2023.2224105
dc.identifier.issn2330-2674
dc.identifier.issn2330-2682
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85164486743en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1080/23302674.2023.2224105
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5471
dc.identifier.volume10en_US
dc.identifier.wosWOS:001026198300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofInternational Journal of Systems Science-Operations & Logisticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectPlasma Supply Chain Networken_US
dc.subjectResilienceen_US
dc.subjectEfficiencyen_US
dc.subjectCovid-19 Pandemicen_US
dc.subjectStochastic Chance-Constrained Programmingen_US
dc.subjectSimulation-Optimisation Modelen_US
dc.subject>en_US
dc.titleA DEA-based simulation-optimisation approach to design a resilience plasma supply chain network: a case study of the COVID-19 outbreaken_US
dc.typeArticleen_US

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