Integrating Quantum Mechanics and Fuzzy Logic for Enhanced MCDM: A Case Study on Robot Evaluation

Industrial robot selection represents a complex multi-criteria decision-making (MCDM) challenge, significantly impacting production processes in terms of precision, efficiency, and cost-effectiveness.This study aims to enhance the robot selection process by integrating traditional MCDM techniques with innovative weighting models based on fuzzy logic and quantum-inspired probabilistic modeling.The proposed approach combines classical and fuzzy MCDM methods including CODAS, TOPSIS, VIKOR, MOORA, fuzzy TOPSIS, fuzzy EDAS, and fuzzy VIKOR with five distinct weighting techniques: Best-Worst (BW), CRITIC, Entropy, Quantum-inspired, and Fuzzy Quantum-inspired.Quantum and Fuzzy Quantum weighting consistently ranked the IRB 1010-1.5/0.37 model as the most favorable across several methods, such as TOPSIS, CODAS, and Fuzzy TOPSIS, yielding scores up to 0.5563.Conversely, Entropy and CRITIC frequently identified the IRB 760 model as the optimal choice.Fuzzy EDAS selected IRB 760 with a top score of 5.589, while Fuzzy VIKOR highlighted IRB 140-6/0.8 as the most suitable alternative with a score of 0.000000.This research provides a decision support perspective for manufacturers navigating uncertain and complex environments by combining stochastic weight generation with fuzzy uncertainty representation.A quantum-inspired probabilistic weighting design and its fuzzy extension are integrated into the criterion-weighting stage of MCDM and evaluated comparatively against widely used classical weighting schemes.