Work

A Dynamic Scheduling Method Combining Iterative Optimization and Deep Reinforcement Learning to Solve Sudden Disturbance Events in a Flexible Manufacturing Process

Year: 2024

Type: article

Abstract: Unpredictable sudden disturbances such as equipment failure, processing time lag, and order changes increase the deviation between actual production and the planned schedule, seriously affecting production efficiency. This phenomenon is particularly severe in flexible manufacturing. In this paper, a dynamic scheduling method combining iterative optimization and deep reinforcement learning (DRL) is proposed to address the impact of uncertain disturbances. A real-time DRL production environment model is established for the flexible job scheduling problem. Based on the DRL model, an agent training strategy and an autonomous decision-making method are proposed. An event-driven and period-driven hybrid dynamic rescheduling trigger strategy with four judgment mechanisms have been developed. Decision-making method and rescheduling trigger strategy solve the problem of how and when to reschedule for dynamic scheduling problem. The data experiment results show that the trained DRL decision-making model can provide timely feedback on the adjusted scheduling arrangements for different scale order problems. The proposed dynamic scheduling decision-making method and rescheduling trigger strategy can achieve high responsiveness, quick feedback, high quality, and high stability for flexible manufacturing process scheduling decision-making under sudden disturbance.

Source: Mathematics

DOI: 10.3390/math13010004

Authors: Jun Yan, Tianzuo Zhao, Tao Zhang, Hongyan Chu, Congbin Yang, Yueze Zhang

Institution: Beijing University of Technology

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funder: National Natural Science Foundation of China