A similarity measure-based Pythagorean fuzzy additive ratio assessment approach and its application to multi-criteria sustainable biomass crop selection

Decision-making algorithm with complex hesitant fuzzy partitioned maclaurin symmetric mean aggregation operators and SWARA method

Complex hesitant fuzzy sets (CHFSs) have emerged as a powerful tool for addressing uncertainty, especially in representing two-dimensional information through multiple possible values. This study addresses the limitations in existing methodologies by introducing two novel operators: the complex hesitant fuzzy partitioned Maclaurin symmetric mean (CHFPMSM) and the complex hesitant fuzzy weighted partitioned Maclaurin symmetric mean (CHFWPMSM). These operators enable effective aggregation of criteria by organizing them into independent partitions based on inherent properties, enhancing their applicability to real-world multi-criteria decision-making (MCDM) problems. To validate the reliability of these operators, essential properties such as idempotency, monotonicity, and boundedness are verified. The study further extends the stepwise weight assessment ratio analysis (SWARA) method to the CHFS framework, facilitating the derivation of attribute weights under uncertain and complex conditions. A robust MCDM methodology is then proposed, integrating the newly developed operators and the extended SWARA approach to address two-dimensional decision-making challenges effectively. The proposed methodology is applied to a practical case study of selecting the best supplier for electronic goods among five alternatives. Comprehensive sensitivity analysis is conducted to examine the stability of the decision-making process against variations in criteria weights. Additionally, a comparative analysis underscores the novelty and efficiency of the proposed methodology by benchmarking its results against existing approaches.

Assessing the adoption barriers for the AI in food supply chain finance applying a hybrid interval-valued Fermatean fuzzy CRITIC-ARAS model

The identification and evaluation of barriers to artificial intelligence (AI) adoption in food supply chain finance (FSCF) can be addressed as a multiattribute decision-making problem. However, only a few studies have reported the application of decision models for evaluating barriers to the implementation of AI in FSCF, especially within an uncertain context. Hence, this work explores the evaluation issue of implementation barriers via an integrated decision model. In this model, the conventional additive ratio assessment (ARAS) model integrated with the Choquet integral and criteria importance through intercriteria correlation (CRITIC) is extended into the interval-valued Fermatean fuzzy (IVFF) setting for ranking the barriers. The IVFF weighted average operator based on the Choquet integral is introduced to form a group decision matrix. Then, the developed ARAS model with the IVFF-CRITIC method is proposed to evaluate the implementation barriers for AI in FSCF, which can depict the interactions between the barriers. Finally, a case of an FSCF, including four participants, is presented to illustrate the application of the reported model and demonstrate its reliability. The result shows that "Data privacy" ([Formula: see text]) is the main barrier impeding AI adoption in FSCF, and the participant "small and medium-sized processing enterprises" ([Formula: see text]) has the highest barrier level to AI adoption.

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.