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English(EN) A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem

Transformer模型解决开放式车间调度问题

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的方法，使用基于Transformer的深度强化学习方法来解决开放式车间调度问题（OSSP）。该模型在较小的基准实例上进行训练，能够泛化到显著更大的问题而无需重新训练。基于Transformer的策略提供了一种基于学习的替代传统调度规则的方法，在性能上可与EST等成熟启发式算法相媲美，并在大规模实例上优于SPT和LPT。 AI

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Transformer模型解决开放式车间调度问题

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Faezeh Ardali, Mwembezi A. Nyelele, Gerald M. Knapp ·

    一种基于深度强化学习（DRL）的Transformer方法用于求解开放式车间调度问题

    arXiv:2606.13682v1 Announce Type: new Abstract: The open shop scheduling problem (OSSP) arises in many industrial and service settings but remains computationally challenging as the number of jobs and machines increases. While exact methods quickly become intractable, classical d…

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