Deep reinforcement learning for flexible assembly job shop scheduling problem

The flexible assembly job shop scheduling problem with dynamic products arrival was addressed，to minimize total tardiness． A mathematical programming model was proposed based on event points，which contains four decision-making sequences：processing machine assignment，processing operation sequence，assembly station assignment，and assembly operation sequence． This model was solved by deep reinforcement learning algorithm based multi-agent． Firstly，the proposed algorithm consisted of four agents corresponding to four decision sequences，and multi-agent adopted a value decomposition networks (VDN) based cooperative strategy． Secondly，the reward function with tardiness was designed，the digital features of production system were extracted as global features，and the scheduling actions of each agent were defined． Finally，an elite experience pool was designed to fully exploit the value of high return samples． The experimental results show that the proposed method is superior to both classical heuristic rules and meta-heuristic rules in different scenarios. © 2023 Huazhong University of Science and Technology. All rights reserved.