Health care waste recycling concerning circular economy: a Fermatean fuzzy aggregation operator-based SWARA–MABAC approach

Increasing production of waste materials from the healthcare sector is a burning problem, causing health hazards for living beings and the environment. Thus, it is pertinent to identify a proper healthcare waste treatment technology for public health and environmental safety. Healthcare waste recycling (HCWR) is a kind of HCW management that could reduce waste volume and, at the same time, help to produce value-added products. A production and consumption paradigm, known as "circular economy" (CE), emphasizes sharing, renting, reusing, repairing and recycling old goods for as long as possible through which the life cycle of items can be extended. In the present treatise, the problem of HCWR technology (HCWRT) selection is considered a multi-criteria group decision-making (MCGDM) problem concerning CE, where four recycling options with nine selection criteria under Fermatean fuzzy environment (FFE) have been considered with a panel of experts involved in the decision-making process. To carry out the research, a novel FF score function (FFSF) has been devised in such a way that irrespective of membership degree (MD) and non-MD (NMD), the SF could easily rank any type of FF numbers (FFNs). The salient properties of the proposed FFSF have been validated through theoretical justifications. To aggregate decision maker (DM’s) assessments, two aggregation operators, namely FF Dual Hamy Mean Operator (FFDHMO) and FF Weighted Dual Hamy Mean Operator (FFWDHMO), are introduced and their properties are analysed. Finally, the notions of traditional step-wise weight assessment ratio analysis (SWARA) and multi-attribute border approximation area comparison (MABAC) methods have been extended under FFE with the aid of proposed FFWDHMO to deal with uncertainty as well as group decision-making (GDM) issues. These extended methods are then integrated to identify the best HCWRT concerning CE. Various comparative and sensitivity analyses have been carried out to check the consistency, reliability and robustness of the results obtained by the proposed model. The proposed integrated technique has identified Red2Green (R2G) as the best HCWRT concerning CE. The comparative and sensitivity analyses have shown consistency and influence of parameters in ranking results, respectively. The results of this study demonstrate that the proposed model has a broader range of applications since it can anticipate performance more realistically while considering a wide range of influencing factors and input uncertainties.