As a generalization of intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS), q‐rung orthopair fuzzy set (q‐ROFS) is a new concept in describing complex fuzzy uncertainty information. The present work focuses on the multiattribute group decision‐making (MAGDM) approach under the q‐rung orthopair fuzzy information. To begin with, some drawbacks of the existing MAGDM methods based on aggregation operators (AOs) are firstly analyzed. In addition, some improved operational laws put forward to overcome the drawbacks along with some properties of the operational law are proved. Thirdly, we put forward the improved q‐rung orthopair fuzzy‐weighted averaging (q‐IROFWA) aggregation operator and improved q‐rung orthopair fuzzy‐weighted power averaging (q‐IROFWPA) aggregation operator and present some of their properties. Then, based on the q‐IROFWA operator and q‐IROFWPA operator, we proposed a new method to deal with MAGDM problems under the fuzzy environment. Finally, some numerical examples are provided to illustrate the feasibility and validity of the proposed method.

This paper studies the improved aggregation operator based on Dombi and its application in multiattribute decision making (MADM). Firstly, the axiomatic definition of interval-valued hesitant fuzzy elements (IVHFEs) based on Dombi t-norm and t-conorm is introduced. Secondly, we propose a series of IVHFE aggregation operators (IVHFDWA, IVHFDWG, IVHFDOWA, IVHFDOWG, IVHFDHA, and IVHFDHG). In order to get the weight information of attributes, based on the basic idea of traditional distance formula, a distance formula between IVHFEs is constructed, and the optimization model of attribute weight is established. Then, a case study for selection of information security platform is given to demonstrate the merits of the developed method. The parameters of aggregation operator are analyzed in detail, and the critical value of parameters affecting decision making is given. Finally, this method is compared with other methods, which proves the validity and applicability of this method.

Probabilistic linguistic term sets (PLTSs), which assign different weights to various linguistic terms, offer an efficient framework for expressing preferences. Meanwhile, the TODIM method, grounded in prospect theory, is adept at incorporating the cognitive behaviors of decision-makers into the decision-making process. In this paper, we extend the TODIM method to solve multi-attribute group decision making (MAGDM) problems with PLTSs. At first, we extend the Frank operators to PLTSs and propose the probabilistic linguistic Frank weighted averaging (PLFWA) operator based on adjusted rules of PLTSs. Further, some desirable properties of them are studied. Meanwhile, we present an innovative distance measure, deeply anchored in linguistic scale functions, designed to overcome the shortcomings of current distance metrics. What’s more, the combined weights for attributes can be obtained by the criteria importance though intercriteria correlation (CRITIC) method and the best-worst method (BWM) and the steps of the extended TODIM method for PLTSs are proposed. Finally, a numerical example for the purchase selection of electric vehicles is given, and some sensitivity and comparative analysis are used to illustrate the effectiveness and rationality of this new method.

The prevailing era of the Internet of Things (IoT) has renewed all fields of life in general, but, especially with the advent of artificial intelligence (AI), has drawn the attention of researchers into a new paradigm of life standards. This revolution has been accepted around the world for making life comfortable with the use of intelligent devices. AI-enabled machines are more intelligent and capable of completing a specific task which saves a lot of time and resources. Currently, diverse methods are available in the existing literature to handle different issues of real life based on AI and IoT systems. The role of decision-making has its prominence in the AI-enabled and IoT systems. In this article, an AI- and IoT-based intelligent assistant decision-making method is presented for power enterprise customer service. An intelligent model of the customer service data network is designed, and the method of collecting data from IoT to assist decision-making is presented. Then, the semantic relationship of customer service data is defined, and the sharing scope of data transmission and resources are determined to realize intelligent assistant decision-making of customer service in power enterprises. Simulation results show that the proposed method improves the decision data transmission speed and shortens the transmission delay, and the network performance of data interaction is better than that of the existing methods.