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.

The soft expert set (SES) is a useful mathematical tool for addressing uncertainty, with significant applications in decision-making. This study identifies some inconsistencies in the original SES framework. The study demonstrates that the majority of SES operations are in conflict with the foundational definition of an SES. Consequently, the definition of an SES is revised to not only resolve the existing complications but also enhance its applicability in real-world problems. In addition, a novel multi-criteria decision-making (MCDM) approach is proposed based on the revised SES framework, along with its concrete algorithm. The proposed algorithm is applied to a supply chain problem, and its results are compared with two existing SES-based decision-making approaches. The results reveal that the proposed algorithm offers a more precise ranking of decision alternatives and has greater applicability as compared to the other two methods. The findings of this study advance the theoretical understanding of the SES and provide a more robust tool for decision-makers in MCDM environments.

The increasing global population and rapid technological advancements are driving a surge in energy demand. Meanwhile, fossil fuel dependence as a primary energy source is contributing to environmental issues, including greenhouse gas emissions and climate change. These challenges underscore the urgent necessity of transitioning to sustainable energy solutions. Renewable energy systems (RES) are critical alternatives that can facilitate sustainable development goals. Nevertheless, the selection of the most suitable RES for specific geography, particularly within Industry 4.0, poses substantial decision-making complexities due to the intricate criteria involved. This paper introduces a robust method for evaluating RES. The proposed approach integrates f, g, h-fractional fuzzy sets (f, g, h-FrFS) into multi-criteria decision-making (MCDM), enhancing the decision-making process. The f, g, h-FrFS framework effectively captures decision-makers’ preferences by considering membership degrees (MB), non-membership degrees (NMB), and indeterminacy degrees (ID). Initially, the importance of evaluation criteria is determined through the improved Criteria Importance through Inter-Criteria Correlation (CRITIC) method. Following this, the WASPAS model is used to provide an accurate ranking of the RES. Geothermal energy is recognized as the most reliable RES option. To test the robustness of the proposed approach, a sensitivity analysis is conducted, demonstrating the stability of the model under varying conditions. The effectiveness of this framework is further validated through a comparative study analysis. Ultimately, this technique offers significant value for policymakers, energy managers, and organizations involved in renewable energy systems. In Industry 4.0, energy planning and development is complicated, so this tool can assist them in navigating the complexities.

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As each day passes by the world’s NT requirements increase due to increasing population and technological advancements. Currently, traditional technologies are inadequate to support the requirement. It is vital to investigate cost-effective and suitable green environmental technologies as a response. Future connectivity(5G, 6G), programming, artificial intelligence and new technologies might be a resolution to this resource crisis in this setting. Now, choosing amongst the most suitable option present itself as a Multi-Criteria Decision Making (MCDM) challenge in which a judgment must be made in terms of a wide variety of characteristics. In this paper, the extended MCDM strategies are proposed to optimizing new technologies implementation. The novelty of the Fuzzy Hypersoft (FHS) set is discussed, which can deal with uncertainties, vagueness, and unclear data. This framework is more flexible than the structures found in literature as it can deal with the information where the attributes can be further sub-partitioned into attribute values for a better understanding. It may not always be possible to analyze these criteria using precise figures; instead, an assessment must be made using human and expert judgments for a more adaptable and sensitive review. The adaptive MCDM design with fuzzy edges incorporates Entropy (EN), Similarity Measure (SIM), and TOPSIS techniques rely on FHS. The conveyed frameworks are better for probing NT issues because they analyze a more expansive range of attributes, which can handle a component with multiple different sub-attribute values. Expert ratings are used to demonstrate a practical application to highlight the relevance of the proposed approach. In addition, a sensitivity analysis is done to investigate the impact of primary criterion weights in sorting.