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Journal of System Simulation

Abstract

Abstract: Aiming at the problem of time uncertainty in discrete manufacturing workshops, we construct an integrated scheduling mathematical model with the optimization objective of minimizing the maximum completion time based on the consideration of equipment and process constraints, and propose an improved dual-competitive deep Q-network algorithm (ID3QN) to solve the flexible integrated scheduling problem under stochastic working hours. The levels of process, machine, and overall scheduling are designed as features. Eight composite scheduling rules are formed as the action space by combining process rules based on processing times, processing sequences, and process structure tree, along with machine rules relevant to optimization objectives. The difference in average machine utilization set as rewards instantly. Simultaneously, the self-attention mechanism and the mixed sampling strategy are introduced to improve the stability and generalization of the algorithm. The empirical results demonstrate that the average variation of the ID3QN is increased by 54.63% compared to existing deep reinforcement learning algorithms under stochastic processing times, thus confirming the effectiveness of the proposed algorithm.

First Page

474

Last Page

486

CLC

TH186; TP301.6

Recommended Citation

Li Xiang, Ren Xiaoyu, Zhou Yongbing, et al. Research on Flexible Integrated Scheduling Under Stochastic Processing Times Based on Improved D3QN Algorithm[J]. Journal of System Simulation, 2025, 37(2): 474-486.

Corresponding Author

Zhang Jian

DOI

10.16182/j.issn1004731x.joss.23-1164

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