A Multi-Criteria Group Decision-Making Approach for Robot Selection Using Interval-Valued Intuitionistic Fuzzy Information and Aczel-Alsina Bonferroni Means

Abstract

The process of identifying the most appropriate robot for a particular industrial task has grown challenging and more difficult in the fast-paced environment. It is merely driven by the complex evolution and continuous integration of modern characteristics and advanced features by various suppliers. Industrial robots are now widely available in the marketplace, each possessing a distinctive collection of skills, attributes, and requirements. However, the selection of optimal robots is heavily influenced by factors such as the manufacturing environment, product design, production system, and overall cost considerations. These factors directly impact the decision-making process. The ultimate goal for the decision-maker is to pinpoint and choose the most suitable robot, capable of delivering the desired output while minimizing costs and catering to the specific requirements of the industry. So, to consider this, in this paper, the hybrid structure of the Aczel–Alsina (AA) and Bonferroni mean (BM) operators for the interval-valued intuitionistic fuzzy (IVIF) environment has been proposed, which can show the interrelationship between multiple criteria and assist experts in the decision-making (DM) process. Moreover, the algorithm and methodology for the multi-criteria group decision-making (MCGDM) problem have been defined, which are further utilized by solving a real-world problem to demonstrate the effectiveness and validity of the proposed method. At last, the comparative analysis between prior and proposed studies has been presented, followed by the conclusion of the results.