A hybrid evaluation model for intelligent manufacturing under uncertainties by integrating human-machine systems and automation strategies

The accelerated development of intelligent manufacturing places a high demand on the ability to evaluate the processes that can manage the uncertainty in human-machine integration and choice of automation strategy. This paper presents a new model of assessment where Circular Spherical Fuzzy Sets (CSFS), Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA), and the CRITIC weighting technique are combined to analyze intelligent manufacturing system in case of uncertainty. With the flexibility of CSFS and objective contrast analysis of CRITIC and the ideal-real comparison of MAIRCA transparent, the model combines ambiguity, hesitation and conflict of one criterion to another and balances subjective judgments and objective weighting. Compared with conventional techniques like AHP and TOPSIS, the suggested one overcomes the rank instability and has a greater strength in the unpredictable environment, which is why it is very appropriate to Industry 5.0-oriented decision-making. The model has been shown to be applicable, robust and easy to make decisions as illustrated in a hypothetical smart manufacturing case study that emphasizes the usefulness of the model in practice by practitioners who have been interested in finding the reliability of the human-automation configuration in next-generation manufacturing systems.