OPTIMIZING INDUSTRIAL ROBOT SELECTION BASED ON MULTI-CRITERIA DECISION-MAKING TECHNIQUES USING TOPSIS, MOORA AND EDAS

Abstract

Advancements in engineering and information technology have facilitated the widespread integration of robots across various industries. The selection of
an appropriate industrial robot presents a significant challenge due to the diverse range of robot types and the numerous conflicting criteria that must be
considered. Multi-criteria decision making (MCDM) emerges as a valuable tool in this scenario. This study introduces a comprehensive MCDM framework
designed to enhance the optimization of the industrial robot selection process. The framework encompasses four primary criteria (load capacity, repeatability,
velocity ratio, degree of freedom) and five alternatives, with the objective weights of the criteria computed using the Entropy weighting method. The industrial
robot options are evaluated and ranked utilizing three distinct MCDM techniques, namely Technique for Order of Preference by Similarity to Ideal Solution
(TOPSIS), Multi-Objective Optimization Ratio Analysis (MOORA), and Evaluation Based on Distance from Average Solution (EDAS). This approach ensures that the
final decision is not solely based on quantitative measures but also incorporates the subjective priorities and requirements of the decision maker. The study's
findings demonstrate the consistency of the proposed methodologies, with the selected robot options being ranked from highest to lowest priority. These results
are significant as they illustrate the effectiveness of combining various MCDM methodologies to identify optimal choices for industrial production.