A cluster-based stratified hybrid decision support model under uncertainty: sustainable healthcare landfill location selection

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210en_US
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874en_US
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017en_US
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorTorkayesh, A.E.
dc.date.accessioned2022-03-14T06:21:32Z
dc.date.available2022-03-14T06:21:32Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractNowadays, healthcare waste management has become one of the significant environmental, health, and social problems. Due to population and urbanization growth and an increase in healthcare waste disposals according to the growing number of diseases and pandemics like COVID-19, disposal of healthcare waste has become a critical issue. Authorities in big cities require reliable decision support systems to empower them to make strategic decisions to provide safe disposal methods with a prospective vision. Since inappropriate healthcare waste management systems would definitely bring up dangerous environmental, social, health, and economic issues for every city. Therefore, this paper attempts to address the landfill location selection problem for healthcare waste using a novel decision support system. Novel decision support model integrates K-means algorithms with Stratified Best-Worst Method (SBWM) and a novel hybrid MARCOS-CoCoSo under grey interval numbers. The proposed decision support system considers waste generate rate in medical centers, future unforeseen but potential events, and uncertainty in experts’ opinion to optimally locate required landfills for safe and economical disposal of dangerous healthcare waste. To investigate the feasibility and applicability of the proposed methodology, a real case study is performed for Mazandaran province in Iran. Our proposed methodology could efficiently deal with 79 medical centers within 4 clusters addressing 9 criteria to prioritize candidate locations. Moreover, the sensitivity analysis of weight coefficients is carried out to evaluate the results. Finally, the efficiency of the methodology is compared with several well-known methods and its high efficiency is demonstrated. Results recommend adherence to local rules and regulations, and future expansion potential as the top two criteria with importance values of 0.173 and 0.164, respectively. Later, best location alternatives are determined for each cluster of medical centers. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.en_US
dc.identifier.citationTirkolaee, E. B., & Torkayesh, A. E. (2022). A cluster-based stratified hybrid decision support model under uncertainty: Sustainable healthcare landfill location selection. Applied Intelligence, doi:10.1007/s10489-022-03335-4en_US
dc.identifier.doi10.1007/s10489-022-03335-4en_US
dc.identifier.issn0924-669Xen_US
dc.identifier.pmid35280110en_US
dc.identifier.scopus2-s2.0-85125643420en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s10489-022-03335-4
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2547
dc.identifier.wosWOS:000765189600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofApplied Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCoCoSoen_US
dc.subjectGrey Numbersen_US
dc.subjectHealthcare Waste Managementen_US
dc.subjectK-mean Algorithmen_US
dc.subjectMARCOSen_US
dc.subjectStratified BWMen_US
dc.titleA cluster-based stratified hybrid decision support model under uncertainty: sustainable healthcare landfill location selectionen_US
dc.typeArticleen_US

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