TY - JOUR
T1 - JOINT OPTIMIZATION OF SERIAL SYSTEM BASED ON DEEP REINFORCEMENT LEARNING
AU - Cai, Zhiqiang
AU - Wang, Xin
AU - Ye, Zhenggeng
AU - Si, Shubin
N1 - Publisher Copyright:
© The Institution of Engineering & Technology 2024.
PY - 2024
Y1 - 2024
N2 - While the serial manufacturing system is a productive mode of production, rework and maintenance are inevitable aspects of real-world operations, and their promptness and effectiveness are crucial for both production efficiency and product quality. This study begins by analyzing the manufacturing system to understand the impact of unqualified work-in-progress (WIP) on the overall system. Maintenance tasks can enhance machine processing quality and reduce the production of unqualified WIP items; however, they also increase maintenance costs, consume system resources, and decrease production output. Conversely, rework can lower manufacturing costs by converting unqualified WIPs into qualified ones, but processing unqualified WIPs can negatively impact machine quality, leading to further unqualified WIP production. To simulate and optimize the rework and maintenance process, thereby maximizing system production efficiency and product quality, this study proposes a combined optimization model based on the Double Deep Q-Network (DDQN) algorithm. Experimental findings demonstrate that the proposed DDQN algorithm offers significant practical value and a broad range of application prospects. It effectively optimizes rework and maintenance decisions, reduces the number of unqualified WIPs, lowers production costs, and enhances production efficiency.
AB - While the serial manufacturing system is a productive mode of production, rework and maintenance are inevitable aspects of real-world operations, and their promptness and effectiveness are crucial for both production efficiency and product quality. This study begins by analyzing the manufacturing system to understand the impact of unqualified work-in-progress (WIP) on the overall system. Maintenance tasks can enhance machine processing quality and reduce the production of unqualified WIP items; however, they also increase maintenance costs, consume system resources, and decrease production output. Conversely, rework can lower manufacturing costs by converting unqualified WIPs into qualified ones, but processing unqualified WIPs can negatively impact machine quality, leading to further unqualified WIP production. To simulate and optimize the rework and maintenance process, thereby maximizing system production efficiency and product quality, this study proposes a combined optimization model based on the Double Deep Q-Network (DDQN) algorithm. Experimental findings demonstrate that the proposed DDQN algorithm offers significant practical value and a broad range of application prospects. It effectively optimizes rework and maintenance decisions, reduces the number of unqualified WIPs, lowers production costs, and enhances production efficiency.
KW - BUFFER
KW - JOINT OPERATION
KW - MAINTENANCE
KW - REINFORCEMENT LEARNING
UR - http://www.scopus.com/inward/record.url?scp=85216679138&partnerID=8YFLogxK
U2 - 10.1049/icp.2024.3540
DO - 10.1049/icp.2024.3540
M3 - 会议文章
AN - SCOPUS:85216679138
SN - 2732-4494
VL - 2024
SP - 753
EP - 759
JO - IET Conference Proceedings
JF - IET Conference Proceedings
IS - 12
T2 - 14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024
Y2 - 24 July 2024 through 27 July 2024
ER -