In the creation of better multiple attribute decision-making (MADM) patterns to address the ambiguity in the expanding sophisticated of expert systems, the hypothesis of interval-valued intuitionistic fuzzy sets has proven to be an effective and advantageous technique. We employ Aczel–Alsina operations to remedy the MADM issue, wherein all data supplied by decision-makers is conveyed as interval-valued intuitionistic fuzzy (IVIF) decision matrices with all components described by an IVIF number (IVIFN). This allows us to satisfy much more demands from fuzzy decision-making concerns (IVIFN). In the framework of IVIFNs, we primarily describe several novel Aczel–Alsina operations. On the basis of these operations, we construct several novel IVIF aggregation operators, such as the IVIF Aczel–Alsina weighted averaging operator, the IVIF Aczel–Alsina order weighted averaging operator, and IVIF Aczel–Alsina hybrid averaging operator. We built up several features of such operators. We recommend an MADM technique dependent on the advanced IVIF aggregation operators. To demonstrate the effectiveness of the developed technique, we present an overview of research scientist selection. The experimental results show the viability and benefits of the created strategy by contrasting it with the different strategies. This paper reveals that some existing IVIF aggregation operators are particular instances of the operators induced in this paper.

This paper describes the new intuitionistic fuzzy aggregation operators in consequence of Aczel–Alsina operations that possess certain advantages in cases of solving real life problems. We first present some new operations of intuitionistic fuzzy sets (IFSs), for example, Aczel–Alsina sum, Aczel–Alsina product, and Aczel–Alsina scalar multiplication. At that point, we create some IF aggregation operators, for example, the IF Aczel–Alsina weighted averaging operator, the IF Aczel–Alsina ordered weighted averaging operator and IF Aczel–Alsina hybrid averaging operator. We set up different properties of these operators. It is demonstrated that suggested averaging operators have the properties of idempotency, boundary, monotonicity, and commutativity. Then, we design new techniques dependent on these operators to fix multiattribute decision making issues. We present an example of human resource selection to elaborate on the performance of our proposed approach. The outcome shows the practicality and viability of the new technique. Eventually, an organized comparison between the prevailing techniques and the suggested technique has been given.

The dual probabilistic linguistic (DPL) term sets are considered superior to probabilistic linguistic term sets. Further, the generalized Dombi (GD) operators are pretty flexible with the general parameters during the aggregation process. Besides, the Bonferroni mean (BM) operator has the advantage of considering interrelationships between criteria. In this study, we combine the merits of the GD operator, and BM operator for handling multicriteria group decision-making issues under a DPL setting. The existing research on DPL term sets do not focus on both the subjective and objective weights of decision-experts. As a result, the evaluation results are likely to be distorted. To tackle this situation, in this paper, we utilize the concepts of consistency and similarity between the decision-experts to determine the decision-experts subjective and objective weights, respectively. To calculate the weights of criteria, the grey correlation coefficient of the assessment value of criteria is used to reflect the similarity between the criteria and its reference value. Since the existing aggregation operators fail to capture the interrelations between criteria under DPL setting, so for aggregating criteria values, we propose DPL generalized Dombi BM weighted averaging and geometric aggregation operators. We provide a case study regarding biomass feedstock selection to focus on the applicability of these proposed operators. Furthermore, we investigate the effects of the parameters upon ranking order. We also perform a sensitivity assessment of criteria weights to test the stability of our method. Lastly, we provide a comparison between our approach with various extant methods.

The operations of -norm and -conorm, developed by Dombi, were generally known as Dombi operations, which may have a better expression of application if they are presented in a new form of flexibility within the general parameter. In this paper, we use Dombi operations to create a few Pythagorean fuzzy Dombi aggregation operators: Pythagorean fuzzy Dombi weighted average operator, Pythagorean fuzzy Dombi order weighted average operator, Pythagorean fuzzy Dombi hybrid weighted average operator, Pythagorean fuzzy Dombi weighted geometric operator, Pythagorean fuzzy Dombi order weighted geometric operator, and Pythagorean fuzzy Dombi hybrid weighted geometric operator. The distinguished feature of these proposed operators is examined. At that point, we have used these operators to build up a model to remedy the multiple attribute decision-making issues under Pythagorean fuzzy environment. Ultimately, a realistic instance is stated to substantiate the created model and to exhibit its applicability and viability.