A Novel Application of FDOSM for Industrial Robot Selection Using MCDM Techniques

Abstract

Industrial robots offer a range of capabilities and specifications depending on their intended applications. The role of robots in industries, especially in manufacturing, logistics, and related fields, has become increasingly important. As a result, selecting an industrial robot creates a complex decision-making challenge due to the vast array of options available and the absence of uniform performance standards. Decision by Opinion Score Method (FDOSM) is a reliable and consistent method that work in a fuzzy environment, presenting greater flexibility and less computational effort than previous methods. It efficiently addresses challenges by evaluating multiple alternatives according to multiple criteria, enhancing decision-making accuracy. It relies on the use of an opinion matrix to aggregate expert judgments, helping to resolve differences and reduce computational complexity. FDOSM consists of three phases: data input, transformation, and processing units, and both individual and group decision-making contexts are applied to FDOSM. A case study on industrial robot selection demonstrates FDOSM's ability to logically rank alternatives. The R3 (Cybotech V15 Electric) achieved the highest ranking with a score of 2.0944, demonstrating its suitability for pick and place operations in manufacturing systems. Also, among the robots evaluated, the R5 (Unimation PUMA 500/600) achieved the lowest ranking with a score of 3.6. The results demonstrate the effectiveness of the method utilized, as it agrees well with expert opinions and demonstrates the ability to improve decision reliability by addressing discrepancies in expert judgments. The study validates its findings by comparing the mean scores of the two groups, demonstrating that the method provides consistent and logical rankings.