2-tuple linguistic $q$-rung orthopair fuzzy CODAS approach and its application in arc welding robot selection

Industrial robots enable manufacturers to produce high-quality products at low cost, so they are a key component of advanced production technology. Welding, assembly, disassembly, painting of printed circuit boards, pick-and-place mass production of consumer products, laboratory research, surgery, product inspection and testing are just some of the applications of industrial robots. All functions are done with a high level of endurance, speed and accuracy. Many competing attributes must be evaluated simultaneously in a comprehensive selection method to determine the performance of industrial robots. In this research article, we introduce the 2TL $q$-ROFS as a new advancement in fuzzy set theory to communicate complexities in data and presents a decision algorithm for selecting an arc welding robot utilizing the 2-tuple linguistic $q$-rung orthopair fuzzy (2TL $q$-ROF) set, which can dynamically delineate the space of ambiguous information. We propose the $q$-ROF Hamy mean ( $q$-ROFHM) and the $q$-ROF weighted Hamy mean ( $q$-ROFWHM) operators by combining the $q$-ROFS with Hamy mean operator. We investigate the properties of some of the proposed operators. Then based on the proposed maximization bias, a new optimization model is built, which is able to exploit the DM preference to find the best objective weights among attributes. Next, we extend the COmbinative Distance-Based ASsessment (CODAS) method to 2TL $q$-ROF-CODAS version which not only covers the uncertainty of human cognition but also gives DMs a larger space to represent their decisions. To validate our strategy, we present a case study of arc welding robot selection. Finally, the effectiveness and accuracy of the method are proved by parameter analysis and comparative analysis results. The results show that our method effectively addresses the evaluation and selection of arc welding robots and captures the relationship between an arbitrary number of attributes.