Ranking and Selection of Robot Using Advance Correlation Coefficient Single-Valued Neutrosophic Set Multi-Attribute Decision-Making Technique

Abstract

Industrial robot selection plays a critical role in modern manufacturing systems, as the choice of an appropriate robot directly affects productivity, operational efficiency, product quality, and overall cost performance. However, selecting a suitable robot is a challenging task due to the presence of multiple conflicting criteria and the inherent uncertainty in decision data, making it a complex Multi-Attribute Decision-Making (MADM) problem. This study proposes an advanced decision-making framework that integrates Single-Valued Neutrosophic Sets (SVNS) with the Advanced Correlation Coefficient (ACC) to enhance the reliability and effectiveness of robot ranking and selection. The proposed approach transforms conventional decision data into the SVNS domain, where each alternative is represented through three independent membership degrees: truth, indeterminacy, and falsity, allowing a more realistic handling of uncertainty and incomplete information. The ACC measure is then used to determine the closeness of each alternative to the ideal solution, enabling a structured ranking process. A standard robot selection case study is employed to demonstrate the applicability of the method. The results show strong agreement with existing techniques, confirming its validity and robustness. The method is computationally simple and does not require complex weight calculations, making it suitable for practical engineering decision-making applications.