Job scheduling is a well-known Combinatorial Optimization problem with
endless applications. Well planned schedules bring many benefits in the context
of automated systems: among others, they limit production costs and waste.
Nevertheless, the NP-hardness of this problem makes it essential to use
heuristics whose design is difficult, requires specialized knowledge and often
produces methods tailored to the specific task. This paper presents an original
end-to-end Deep Reinforcement Learning approach to scheduling that
automatically learns dispatching rules. Our technique is inspired by natural
language encoder-decoder models for sequence processing and has never been
used, to the best of our knowledge, for scheduling purposes. We applied and
tested our method in particular to some benchmark instances of Job Shop
Problem, but this technique is general enough to be potentially used to tackle
other different optimal job scheduling tasks with minimal intervention. Results
demonstrate that we outperform many classical approaches exploiting priority
dispatching rules and show competitive results on state-of-the-art Deep
Reinforcement Learning ones.

通过自动学习调度规则，本文提出了一种原创的端到端深度强化学习方法来解决作业调度的 NP - 难度问题，该技术受到自然语言编码器 - 解码器模型的启发，可在最小干预下用于处理其他不同的优化作业调度任务。研究结果表明，我们在利用优先调度规则方面超过了许多传统方法，并且在最先进的深度强化学习方法中取得了有竞争力的结果。