A Hybrid MCDM Approach for Industrial Robots Selection for the Automotive Industry

Abstract

The use of robots in various stages of the production process is now commonplace across practically all sectors of the economy. Additionally, even for present-day small and medium-sized businesses, this has developed into a very powerful need in recent years and continues to grow in importance. The selection of an industrial robot is a very complicated decision-making issue due to the fact that there are numerous aspects and criteria that are in conflict with one another, as almost all of the earlier research emphasized. In addition, the many sophisticated requirements that have been added to these robots by the makers of robotics have led the level of complexity to expand even further. As a result, decision-makers are faced with increasingly complex decision-making difficulties that are influenced by a great deal of uncertainty. As a result of this, a combined neutrosophic multi-criteria decision-making (MCDM) approach may assist in resolving a significant number of the ambiguities that are suggested in the present article. Initially, the Entropy method was used to evaluate the criteria set for the study under the neutrosophic environment. Then, the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method was used to evaluate and rank five robots used in the automotive industry. The results indicate that the criteria of performance and working accuracy are the most influential criteria in choosing the most appropriate robot. Also, the results indicate that the KAWASAKI robot is the best choice in the manufacturing process for the automotive industry.