Hybrid digital twin and quantum AI with fuzzy multiobjective modeling in supply chain management

This research aims to respond to the increasing complexity and uncertainty in supply chains by providing a framework for robust and multi-objective decision-making that simultaneously optimizes economic, environmental, and operational goals. The proposed framework is developed by integrating digital twin technology, fuzzy mathematical modeling, and quantum artificial intelligence. The digital twin generates real-time data and dynamically updates the system conditions. The fuzzy model converts these conditions into mathematical variables, and the quantum algorithm processes them to search for the Pareto front and evaluate the decision space. The model is validated with industrial data and disturbance scenarios. The results show that this triple combination significantly improves the stability, speed, and quality of decision-making. Sensitivity analysis and disturbance simulation also confirm the system’s efficiency and adaptability. Digital twin plays a pivotal role in reconfiguring supply chain decisions in dynamic environments. This framework provides a practical tool for supply chain managers to achieve sustainable optimization and robust decision-making with real-time adaptability in complex industrial conditions.