Towards Industry 5.0: digital twin-enhanced approach for dynamic supply chain rescheduling with real-time order arrival and acceptance
提出一种基于数字孪生的动态调度框架，结合深度强化学习和遗传算法，解决供应链在实时订单到达下的重调度问题，提升韧性和可持续性。
In the Industry 5.0 era, digital twin (DT) technology enables a real-time connection between physical factory systems and virtual scheduling models, enhancing resilience and adaptability in response to market fluctuations. This paper introduces a resilient, human-centric, and sustainable DT-based dynamic scheduling framework tailored for supply chain rescheduling problems, particularly under dynamic order arrivals and acceptance. The static scheduling model focuses on minimising the total weighted tardiness of existing orders, while the dynamic model extends this by balancing disruption costs with potential penalties for rejecting orders. Within the DT-based framework, we integrate deep reinforcement learning (DRL) with a genetic algorithm (GA), utilising an Actor-Critic mechanism to select genetic operators dynamically. Extensive computational experiments demonstrate that the proposed DT-based framework substantially enhances supply chain resilience, offering manufacturers a sustainable and human-centric solution aligned with Industry 5.0 goals in the face of volatile demands.