Interval-Valued Pythagorean Fuzzy Similarity Measure-Based Complex Proportional Assessment Method for Waste-to-Energy Technology Selection

A novel approach based on similarity measure for the multiple attribute group decision-making problem in selecting a sustainable cryptocurrency

Environmental impact and sustainability challenges in the cryptocurrencies has become increasingly examined in the literature. However, studies of the multiple attribute group decision making (MAGDM) method for major selection of cryptocurrencies in advancing sustainability are still at an early stage. In particular, research on the fuzzy-MAGDM method in the evaluation of sustainability in cryptocurrencies is scarce. This paper adds contributions by developing a novel MAGDM approach to evaluate the sustainability development of major cryptocurrencies. It proposes a similarity measure for interval-valued Pythagorean fuzzy numbers (IVPFNs) based on whitenisation weight function and membership function in grey systems theory for IVPFNs. It further developed a novel generalised interval-valued Pythagorean fuzzy weighted grey similarity (GIPFWGS) measure approach to provide a more rigorous evaluation in complex decision marking problem with embedding ideal solution and membership degree. It also conducts a sustainability evaluation model of major cryptocurrencies as a numerical application and performs a robustness assessment with different variations of the expert's weight to test how different values of parameter θ can affect the ranking results of alternatives. The results suggest that Stellar is the most sustainable cryptocurrency, while Bitcoin with its intensive energy consumption, high mining cost and high computing power provides the least effective support for its sustainable development. A comparative analysis with the average value method and Euclidean distance method was performed to validate the reliability of the proposed decision-making model and provides evidence that the GIPFWGS has better fault tolerance.

Assessment of healthcare waste treatment methods using an interval-valued intuitionistic fuzzy double normalization-based multiple aggregation approach

Healthcare waste management has been an extensively attractive topic recently since it is one of the key concerns regarding both environment and public health, predominantly in developing nations. The optimization of the treatment procedure for healthcare waste is indeed a complex "multi-criteria decision-making (MCDM)" problem that involves contradictory and interweaved critical criteria. To successfully handle this issue, this study extends the original method, named the "double normalization-based multi-aggregation (DNMA)" approach, with "interval-valued intuitionistic fuzzy sets (IVIFSs)" for decision-making problems taking criteria in terms of benefit or cost types. This method involves two target-based normalizations and three subordinate utility models. To estimate the criteria weights, we propose a new parametric divergence measure and discuss the feasibility of the developed divergence measure based on existing divergence measures for IVIFSs. Further, the developed framework is implemented to elucidate the "healthcare waste treatment (HCWT)" problem. The comparative and sensitivity analyses of the outcomes indicate that the proposed approach efficiently tackles the problem of HCWT selection. The outcomes show that steam sterilization (0.462) is the optimal one for HCWT. The prioritization options, obtained by presented approach, are dependable and suitable, which are steam sterilization ≻ microwave ≻ incineration ≻ landfilling.

Digital transformation challenges in sustainable financial service systems using novel interval-valued Pythagorean fuzzy double normalization-based multiple aggregation approach

Recently, financial service systems have had essential impacts on public policies, the economic performance of firms, and all forms of industry and commerce. These systems play an important role in determining whether a society (which includes a wide range of members, from governmental institutions to individual consumers) has been successfully considered an environmentally sustainable path. The literature shows that the people who work in the financial sector are mostly unaware of the pressure and rationale behind sustainable development and its bearing on their work; however, those who work in the relevant research and policy areas generally ignore the vitality of the role of the financial sector in such a development. The study of interval-valued Pythagorean fuzzy sets (IVPFSs) indicates an urge for a decision approach to implementing the available information for rational decisions properly. Inspired by the advantage of IVPFSs, an extended decision methodology called the IVPF rank-sum weighting method (RSWM)-double normalization-based multi-aggregation (DNMA) is discussed. In this line, the IVPF-RSWM is applied to find the subjective weights of digital transformation challenges of sustainable financial service systems (SFSS), and the DNMA framework is developed to obtain the preferences of SFSSs in the banking sector. A case study to assess the main digital transformation challenges in SFSSs of the banking sector is undertaken. Further, comparison and sensitivity investigations are taken to illustrate the advantage of the presented approach.