Recent studies in using deep reinforcement learning (DRL) to solve Job-shop
scheduling problems (JSSP) focus on construction heuristics. However, their
performance is still far from optimality, mainly because the underlying graph
representation scheme is unsuitable for modeling partial solutions at each
construction step. This paper proposes a novel DRL-based method to learn
improvement heuristics for JSSP, where graph representation is employed to
encode complete solutions. We design a Graph Neural Network based
representation scheme, consisting of two modules to effectively capture the
information of dynamic topology and different types of nodes in graphs
encountered during the improvement process. To speed up solution evaluation
during improvement, we design a novel message-passing mechanism that can
evaluate multiple solutions simultaneously. Extensive experiments on classic
benchmarks show that the improvement policy learned by our method outperforms
state-of-the-art DRL-based methods by a large margin.

本文提出了一种基于图神经网络的深度强化学习方法，用于解决作业车间调度问题中的改进启发式问题，并设计了一种新颖的传递信息机制来加快解决方案评估，实验结果表明，该方法比现有的基于深度强化学习的方法在 JSSP 领域中具有更好的性能.

深度强化学习在作业车间调度中的搜索应用

Learning to Search for Job Shop Scheduling via Deep Reinforcement Learning

Recent studies in using deep learning to solve routing problems focus on
construction heuristics, the solutions of which are still far from optimality.
Improvement heuristics have great potential to narrow this gap by iteratively
refining a solution. However, classic improvement heuristics are all guided by
hand-crafted rules which may limit their performance. In this paper, we propose
a deep reinforcement learning framework to learn the improvement heuristics for
routing problems. We design a self-attention based deep architecture as the
policy network to guide the selection of next solution. We apply our method to
two important routing problems, i.e. travelling salesman problem (TSP) and
capacitated vehicle routing problem (CVRP). Experiments show that our method
outperforms state-of-the-art deep learning based approaches. The learned
policies are more effective than the traditional hand-crafted ones, and can be
further enhanced by simple diversifying strategies. Moreover, the policies
generalize well to different problem sizes, initial solutions and even
real-world dataset.

本文提出一种基于 self-attention 的深度强化学习框架，以学习解决旅行商问题（TSP）和有容量车辆路径问题（CVRP）的改进启发式算法，实验表明该方法性能优于现有的深度学习方法，并且具有良好的泛化能力。