APPLYING EDAS AS AN APPLICABLE MCDM METHOD FOR INDUSTRIAL ROBOT SELECTION

Abstract

In order to stay an actual competitor in today's environment, it is essential for manufacturing organizations to make decisions promptly and correctly. In the real-time manufacturing decision making problems, some alternatives are more likely to be evaluated with respect to multiple conflicting criteria. Several multi-criteria decision-making (MCDM) methods have been available to help decision makers in choosing the best decisive course of actions. The aim of the study is to apply an efficient and relatively new method called Evaluation based on Distance from Average Solution (EDAS) as an applicable and useful MCDM method for robot selection problem (RSP). In order to examine the feasibility and effectiveness of the presented method, several numerical examples from the literature are considered. Comparing with other methods especially MCDM methods given in the literature for the industrial RSPs, the Spearman's rank correlations analysis indicates that this method is capable of accurately ranking selected robots.