A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system

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dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridRouhani-Tazangi, Mohammad Reza/0000-0001-6343-5077
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.contributor.authorNayeri, Sina
dc.contributor.authorKhoei, Mohammad Amin
dc.contributor.authorRouhani-Tazangi, Mohammad Reza
dc.contributor.authorGhanavatiNejad, Mohssen
dc.contributor.authorRahmani, Mohammad
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2024-05-19T14:46:50Z
dc.date.available2024-05-19T14:46:50Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis research attempts to study the Supplier Selection and Order Allocation Problem (SSOAP) considering three crucial concepts, namely responsiveness, sustainability, and resilience. To do so, the current research develops a Multi-Stage Decision-Making Framework (MSDMF) to select potential suppliers and determine the quantity of orders. The first stage aims at computing the scores of the suppliers based on several indicators. To do this, a novel decision-making approach named the Stochastic Fuzzy Best-Worst Method (SFBWM) is developed. Then, in the second stage, a Multi-Objective Model (MOM) is suggested to deal with supplier selection and order allocation decisions. In the next step, a data-driven Fuzzy Robust Stochastic (FRS) optimization approach, based on the fuzzy robust stochastic method and the Seasonal Autoregressive Integrated Moving Average (SARIMA) methods, is employed to efficiently treat the hybrid uncertainty of the problem. Afterwards, a novel solution method named the developed Chebyshev Multi-Choice Goal Programming with Utility Function (CMCGP-UF) is developed to obtain the optimal solution. Moreover, given the crucial role of the Medical Equipment (ME) industry in society's health, especially during the recent Coronavirus disease, this important industry is taken into account. The outcomes of the first stage demonstrate that agility, cost, GHG emission, quality, robustness, and Waste Management (WM), respectively, are the most important criteria. The outcomes of the second stage determine the selected suppliers, utilized transportation systems, and established sites. It is also revealed that demand directly affects all the objective functions while increasing the rate of disruptions has a negative effect on the sustainability measures.en_US
dc.identifier.doi10.1016/j.engappai.2023.106511
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85161554009en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.engappai.2023.106511
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5603
dc.identifier.volume124en_US
dc.identifier.wosWOS:001019171900001en_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.subjectSupplier Selectionen_US
dc.subjectSustainabilityen_US
dc.subjectResilienceen_US
dc.subjectStochastic Fuzzy Best-Worst Methoden_US
dc.subjectData-Driven Fuzzy Robust Stochasticen_US
dc.subjectOptimizationen_US
dc.titleA data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare systemen_US
dc.typeArticleen_US

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