Optimizing COVID-19 medical waste management using goal and robust possibilistic programming

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dc.authoridTavakkoli-Moghaddam, Reza/0000-0002-6757-926X
dc.authoridkarimi, hamed/0000-0001-8940-1211
dc.authorwosidTavakkoli-Moghaddam, Reza/P-1948-2015
dc.contributor.authorKarimi, Hamed
dc.contributor.authorWassan, Niaz
dc.contributor.authorEhsani, Behdad
dc.contributor.authorTavakkoli-Moghaddam, Reza
dc.contributor.authorGhodratnama, Ali
dc.date.accessioned2024-05-19T14:45:47Z
dc.date.available2024-05-19T14:45:47Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractDuring the global Coronavirus Disease (COVID-19) pandemic, the exponential rise in Hazardous Medical Waste (HMW) due to increased demand for personal protective equipment and heightened medical requirements posed significant threats to public health. This study proposes an innovative approach using a reverse logistics supply chain network that comprehensively integrates sustainability factors (e.g., cost, working conditions, exposure risks, and environmental impact) to manage the risks associated with medical waste effectively amid the pandemic. This research focuses on employing a guideline -based allocation of medical waste to specific technologies, leveraging the Torabi-Hassini (TH), Lp-metric (Lebesgue metric), and Goal Attainment (GA) approaches and robust possibilistic programming to address uncertainties. A real -case study validates the proposed model, demonstrating its ability to balance multiple objectives by optimizing the flow among treatment centers and introducing new Temporary Treatment Centers (TTCs). Also, we analyze broad sensitivity through weights assigned to the objective functions to obtain Pareto solutions. The convexity of the Pareto front confirms the conflict among the objective functions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach specifies that the Lp-metric approach outperforms the others, and the TH approach is regarded as the second rank. The study's findings highlight the model's efficacy and provide crucial managerial insights for health organization administrators in efficiently managing the HMW supply chain network.en_US
dc.identifier.doi10.1016/j.engappai.2023.107838
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85181837057en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.engappai.2023.107838
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5342
dc.identifier.volume131en_US
dc.identifier.wosWOS:001153645600001en_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.subjectMedical Waste Managementen_US
dc.subjectReverse Supply Chain Networken_US
dc.subjectRobust Possibilistic Programmingen_US
dc.titleOptimizing COVID-19 medical waste management using goal and robust possibilistic programmingen_US
dc.typeArticleen_US

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