Sustainable Medical Waste Management Using an Intuitionistic Fuzzy-Based Decision Support System

Konstantinos Kokkinos, Evangelia Lakioti (), Konstantinos Moustakas, Constantinos Tsanaktsidis and Vayos Karayannis
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Konstantinos Kokkinos: Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
Evangelia Lakioti: School of Health Sciences, University of Thessaly, 42100 Larissa, Greece
Konstantinos Moustakas: School of Chemical Engineering, National Technical University of Athens, 15780 Athens, Greece
Constantinos Tsanaktsidis: Department of Chemical Engineering, University of Western Macedonia, 50100 Kozani, Greece
Vayos Karayannis: Department of Chemical Engineering, University of Western Macedonia, 50100 Kozani, Greece

Sustainability, 2023, vol. 16, issue 1, 1-26

Abstract: The growing urban population and increased use of healthcare services have brought significant attention to the safe and sustainable management of medical waste. Selecting the proper technology in medical waste management (MWM) represents one of the most critical challenges for decision-makers to ensure public health. In order to evaluate and choose the best MWM methodology, the current research provides a novel multi-criteria decision-making (MCDM) strategy for a variety of social stakeholders, to compute criteria weights, decision-making weights, and alternative ranking algorithms. The suggested structure addresses uncertain assessments of alternatives by extending weighting and ranking methods to acquire the decision-making weight and rank the MWM alternatives based on uncertain conditions. It also uses ‘intuitionistic fuzzy’ linguistic variables to indicate criteria weights. To assess all the factors pertaining to the sustainability of MWM actions, this study suggests the creation of a decision support system (DSS). Our DSS is built upon a novel strategy that utilizes a collection of MCDM models that are grounded on contemporary intuitionistic fuzzy logic methodologies. Alternative scenarios have been assessed for the instance of Greece, after specialists in the healthcare management field imposed 17 criteria and sub-criteria. The IF-MCDM methodologies used were the Intuitionistic Fuzzy DEMATEL, TOPSIS, and CORPAS. The alternative scenarios ranged from the prioritizing of safety laws and regulations to public acceptance and awareness, with the handling of hazardous risks and transportation playing a crucial part in the process. All ensemble methods produced the same ranking of the alternatives, demonstrating that safety and risk avoidance is the most significant scenario for sustainable urban development and public health.

Keywords: medical (healthcare) waste; sustainable management; decision support system; intuitionistic fuzzy logic; urban development; public health (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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