Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops

Abstract

The traditional shop floor scheduling problem mainly focuses on the static environment, which is unrealistic in actual production. To solve this problem, this paper proposes a digital twin-driven shop floor adaptive scheduling method. Firstly, a digital twin model of the actual production line is established to monitor the operation of the actual production line in real time and provide a real-time data source for subsequent scheduling; secondly, to address the problem that the solution quality and efficiency of the traditional genetic algorithm cannot meet the actual production demand, the key parameters in the genetic algorithm are dynamically adjusted using a reinforcement learning enhanced genetic algorithm to improve the solution efficiency and quality. Finally, the digital twin system captures dynamic events and issues warnings when dynamic events occur in the actual production process, and adaptively optimizes the initial scheduling scheme. The effectiveness of the proposed method is verified through the construction of the digital twin system, extensive dynamic scheduling experiments, and validation in a laboratory environment. It achieves real-time monitoring of the scheduling environment, accurately captures abnormal events in the production process, and combines with the scheduling algorithm to effectively solve a key problem in smart manufacturing.