Prioritizing robots in intelligent manufacturing using q-rung orthopair fuzzy decision-making method and unknown weight information

The rapid evolution of intelligent manufacturing systems necessitates the integration of advanced robotics to meet increasing demands for productivity, precision, and adaptability. Robots play an indispensable role across a spectrum of operations, from assembly to inspection, directly influencing the efficiency and effectiveness of modern manufacturing environments. Addressing the critical need for enhanced decision-making in technological investments, this study evaluates and ranks various types of robots using an integrated decision-making framework. Utilizing the q-rung orthopair fuzzy set (qROFS) to manage uncertain and subjective expert evaluations, this paper combines entropy and similarity measures to determine expert weight coefficients, reflecting the certainty and support degrees of their opinions. Criteria weights are derived using the full consistency method (FUCOM) for subjective weighting and the criteria importance through intercriteria correlation (CRITIC) method for objective weighting. The comprehensive rankings of the robots are then established using the combined compromise for ideal solution (CoCoFISo) method. A practical case study demonstrates the application of the proposed method. Results from the case study indicate that robots for machine tending rank as the most influential, followed by inspection, assembly, welding, and material handling and packaging robots, showcasing their pivotal roles in enhancing manufacturing productivity and safety. This study not only presents a methodological advancement in handling expert uncertainty but also offers actionable insights for integrating robotic technologies in intelligent manufacturing systems, thereby supporting strategic decision-making and operational optimization.