A novel Pythagorean fuzzy combined compromise solution framework for the assessment of medical waste treatment technology

Medical waste management (MWM) is extremely important to protect the general public and environment. Medical practitioners have used various medical waste treatment technologies (MWTTs) for MWM. In this study, a few treatment technologies (TTs) namely Incineration, Chemical disinfection, Microwave, Autoclaving and Reverse Polymerization are considered as alternatives. Further, each of the TTs is assessed based on the technology and its operation, social acceptance, environmental protection, total cost, and health hazards. Finally, the most suitable technology is selected. The process of selecting the most suitable TT can be regarded as a multi-criteria decision-making (MCDM) problem owing to the involvement of several factors. During this process, the decision experts have some uncertainty and ambiguity to measure the qualitative and quantitative opinions in the selection of best alternative over different criteria. To address this issue, a new Pythagorean fuzzy-based decision-making methodology is developed. To this end, we present a novel Pythagorean fuzzy combined compromise solution (PF-CoCoSo) framework to rank the alternatives. In addition, we propose new Pythagorean fuzzy similarity measures to develop the weight-determining procedure for the criteria. In addition, a comparison with extant measures is presented to verify the effectiveness of the introduced similarity measures. To demonstrate the effectiveness and feasibility of the proposed framework, a case study of MWTT selection is presented. From the assessment, the autoclaving technology is the most suitable alternative among the MWTTs. Further, a sensitivity analysis and comparative study are performed to highlight the strength and validity of the introduced methodology. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Medical waste management is crucial for protecting public health and the environment. This study evaluates various medical waste treatment technologies and proposes a Pythagorean fuzzy-based decision-making methodology for selecting the most suitable technology. The autoclaving technology is identified as the most suitable alternative, supported by sensitivity analysis and comparative study.