针对作业车间调度问题的深度强化学习模型设计模式

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shiyong Wang, Jiaxian Li, Qingsong Jiao, Fang Ma
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引用次数: 0

摘要

在优化生产效率、资源利用、成本控制、节能减排等生产目标时,生产调度具有重要作用。目前,基于深度强化学习的生产调度方法可以达到与广泛使用的元启发式算法大致相同的精度,同时表现出更高的效率和强大的泛化能力。因此,这一新范式备受关注,已有大量研究成果被报道。通过回顾现有的针对作业车间调度问题的深度强化学习模型,我们发现了典型的设计模式以及由代理、环境、状态、行动和奖励等共同组成的模式组合。围绕这一重要贡献,对训练深度强化学习调度模型和应用由此产生的调度求解器的架构和程序进行了归纳。此外,还总结了关键评估指标,并概述了有前景的研究领域。这项工作针对一系列生产调度问题研究了几种深度强化学习模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Design patterns of deep reinforcement learning models for job shop scheduling problems

Design patterns of deep reinforcement learning models for job shop scheduling problems

Production scheduling has a significant role when optimizing production objectives such as production efficiency, resource utilization, cost control, energy-saving, and emission reduction. Currently, deep reinforcement learning-based production scheduling methods achieve roughly equivalent precision as the widely used meta-heuristic algorithms while exhibiting higher efficiency, along with powerful generalization abilities. Therefore, this new paradigm has drawn much attention and plenty of research results have been reported. By reviewing available deep reinforcement learning models for the job shop scheduling problems, the typical design patterns and pattern combinations of the common components, i.e., agent, environment, state, action, and reward, were identified. Around this essential contribution, the architecture and procedure of training deep reinforcement learning scheduling models and applying resultant scheduling solvers were generalized. Furthermore, the key evaluation indicators were summarized and the promising research areas were outlined. This work surveys several deep reinforcement learning models for a range of production scheduling problems.

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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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