AI-Driven Adaptive Scheduling and Dynamic Allocation of Manufacturing Resources

Abstract

As global market competition intensifies and customer demand becomes increasingly personalized, the limitations of traditional rigid production models have become salient, making flexible manufacturing a core mandate of intelligent manufacturing. Focusing on the digital thread spanning design, process planning, and production, this paper investigates how artificial intelligence (AI) enables adaptive scheduling and dynamic allocation of manufacturing resources. We first analyze the constraints of conventional production scheduling systems in responding to real-time disruptions, and then construct a digital-thread-based AI adaptive scheduling framework. Integrating multi-source heterogeneous data—such as real-time order streams, machine status, and material inventories—and leveraging AI algorithms including deep learning and reinforcement learning, the framework achieves autonomous decision-making and dynamic optimization of production planning. We then examine disturbance-aware dynamic scheduling strategies, load-balancing-based elastic resource allocation, and the application of digital twin technology for strategy simulation and validation. Finally, a simulation case demonstrates the effectiveness of the proposed approach in shortening order lead times, improving equipment utilization, and enhancing system robustness against disruptions, followed by a discussion of future research directions.