Analysis and Prioritization of the Factors of the Robotic Industry With the Assistance of EDAS Technique Based on Intuitionistic Fuzzy Rough Yager Aggregation Operators

The focus of the interdisciplinary and scientific discipline of robotics is to design, maintain and use mechanical robotics. There exist many issues faced by the robotic industry but there are some factors that can cover these complexities effectively. Handling vague and imprecise data is a difficult task nowadays. So there is a need to define such kind of effective and valuable tool that can handle complex and vague data more dominantly. The evaluation based on distance from average solution (EDAS) method is a very useful tool that can handle complex data more effectively. The best alternative can be chosen based on distance from the average solution. The EDAS method is relatively simple to use and provide a quick evaluation of alternative based on multiple criteria. Yager t-norm and t-conorm are two fuzzy logic operators proposed by Yager. So based on the importance of Yager t-norm and t-conorm, initially, in this article, we have proposed the basic operative laws for intuitionistic fuzzy rough numbers. Based on these developed operational laws, we have developed some new intuitionistic fuzzy rough aggregation operators called intuitionistic fuzzy rough Yager average (weighted, ordered weighted, hybrid) aggregation operators and intuitionistic fuzzy rough Yager geometric (weighted, ordered weighted, hybrid) aggregation operators. Moreover, we have proposed the EDAS technique based on intuitionistic fuzzy rough Yager aggregation operators and used these notions for the selection of suitable factors that play a vital role in the robotic industry. Also, to show the effective use of these introduced notions, we have proposed an algorithm for the EDAS method based on intuitionistic fuzzy rough Yager aggregation operators along with a descriptive example. To show the superiority of the introduced work we have developed a comparative analysis.