An integrated decision support framework for resilient vaccine supply chain network design

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dc.authoridSimic, Vladimir/0000-0001-5709-3744
dc.authoridTorkayesh, Ali/0000-0002-1012-4213
dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridTavana, Madjid/0000-0003-2017-1723
dc.authorwosidSimic, Vladimir/B-8837-2011
dc.authorwosidTorkayesh, Ali/ABI-8024-2020
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorTorkayesh, Ali Ebadi
dc.contributor.authorTavana, Madjid
dc.contributor.authorGoli, Alireza
dc.contributor.authorSimic, Vladimir
dc.contributor.authorDing, Weiping
dc.date.accessioned2024-05-19T14:46:31Z
dc.date.available2024-05-19T14:46:31Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractDesigning resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Nondominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the & epsilon;-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines.en_US
dc.identifier.doi10.1016/j.engappai.2023.106945
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85167983184en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.engappai.2023.106945
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5539
dc.identifier.volume126en_US
dc.identifier.wosWOS:001059223300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectResilient Supply Chainen_US
dc.subjectVaccine Distributionen_US
dc.subjectNeutrosophic Seten_US
dc.subjectRobust Optimizationen_US
dc.subjectDecision Support Systemen_US
dc.subjectNsga-Iien_US
dc.titleAn integrated decision support framework for resilient vaccine supply chain network designen_US
dc.typeArticleen_US

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