A Hybrid Multi-Criteria Decision-Making Technique Combining AHP and GRA for Industrial Collaborative Robot Selection

Technological progress in robotics has played a pivotal role in revolutionising manufacturing systems across industries. Collaborative robotics has become prominent among crucial innovations, where robotics finds extensive applications in production processes. Small and medium-sized enterprises are engaged in activities that include unsafe and repetitive jobs. The selection of an appropriate robot for an efficient industrial process has become complex due to the abundance of robot configurations and capabilities. A combination of objective and subjective elements determines the characteristics and operational accuracy of the best industrial collaborative robot (cobot). The proposed technique for selecting collaborative robots integrates analytic hierarchy process (AHP) and grey relational analysis (GRA) for determining criterion weights and ranking cobots, respectively. The combined methodology addresses decision-making uncertainties by evaluating robots based on parameters, such as reachability, repeatability, robot mass, power consumption, footprint, payload, and cobot price. The method, novel in the collaborative robot selection, aims to classify several robot models for flexible assembly stations comprehensively. The paper highlights the identification of critical criteria and the methodology for evaluating and selecting the most appropriate collaborative robots and standard industrial robots for an application.