Decision Making Algorithm Based on Fermatean Fuzzy Schweizer-Sklar Power Average Operator and Its Application in Selection of Sustainable Health Care Waste Management Technique

Hospitals and other health care facilities produce health care waste (HCW), which poses risks to health care personnel, patients, the public, and the environment. Its complex makeup, including infectious pathogens and dangerous compounds, necessitates expert treatment to reduce health and environmental concerns. In numerous developing nations, healthcare waste disposal management has emerged as one of the most rapidly escalating concerns for urban towns and health care providers. Therefore, identifying the most sustainable HCW management technique (HCWMT) is a challenging endeavor due to the multitude of possibilities, criteria, and stringent governmental regulations governing HCW disposal. Therefore, this paper presents a multiattribute decision making (MADM) algorithm under the Fermatean fuzzy numbers (FFNs) environment to select the optimal HCWMT. To achieve this, we propose the Fermatean fuzzy Schweizer-Sklar power average (FFSSPA) aggregation operator (AO) and the Fermatean fuzzy Schweizer-Sklar power weighted average (FFSSPWA) AO for aggregating the FFNs by combining the features of power averaging AO and Schweizer-Sklar t-norm and t-conorm. The proposed FFSSPA AO and FFSSPA AO adjusts the influence of each input dynamically, taking into account its relative importance or reliability. However, based on the proposed FFSSPWA AO, we propose a MADM algorithm under the FFNs environment. Afterwards, we consider a mathematical case study for the assessment of sustainable HCWMTs and demonstrate the practical applicability of the proposed MADM algorithm. In this case study, five potential alternatives for sustainable health care waste management techniques (HCWMTs): “Mechanical Biological Treatment”, “Hydrothermal Carbonization”, “Incineration”, “Microwaving”, and “Chemical Disinfection”, which are evaluated based on seven attributes: “Environmental hazard”, “Health risk”, “Investment cost”, “Operation and maintenance cost”, “Revenue generation”, “Public acceptance”, and “Requirement of skilled labor”. The proposed algorithm identifies “Chemical Disinfection” as the most appropriate sustainable HCWMT for this case. Finally, we present two numerical examples to demonstrate the superiority and validity of the proposed MADM algorithm compared to existing MADM algorithms.