A novel Fractional fuzzy approach for multi-criteria decision-making in medical waste management

Urban populations, coupled with increased healthcare service usage, highlight the need for safe and sustainable medical waste management (MWM). Choosing the right technology for MWM is a crucial challenge for decision-makers aiming to protect public health. Multi-criteria decision making (MCDM) techniques are often used to address uncertainty and complexity inherent in such decisions. MCDM techniques based on traditional fuzzy sets (such as spherical and t-spherical fuzzy sets) leave significant membership value. In this response, a f, g, h-fractional fuzzy set (f, g, h-FrFS) based MCDM model is introduced. This study introduces the f, g, h-FrFS based Hamming and normalized Hamming distances. Additionally, we propose an improved Criteria Importance through Inter-Criteria Correlation (CRITIC) method to assess criteria weights and a novel distance-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to evaluate and rank MWM technologies. To test the robustness of the proposed approach, a sensitivity analysis is conducted, demonstrating the stability of the model under varying conditions. The result is the development of a comprehensive MCDM framework, referred to as f, g, h-FrF-CRITIC-TOPSIS, which incorporates relevant criteria for evaluating MWM technologies. The effectiveness of this framework is further validated through a comparative study. The results align with the actual situation and offer valuable insights into the implementation of suitable treatment technologies for MWM. This methodology proves to be highly effective in addressing the complex decision-making challenges associated with MWM, particularly in uncertain environments. Ultimately, this technique offers significant value for policymakers and organizations involved in medical systems. In medical premises, MWM is complicated, so this tool can assist them in navigating the complexities.