Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
This work addresses job-shop scheduling problems for operations research and AI, offering a novel improvement heuristic that is incremental over prior DRL construction methods.
The paper tackles the suboptimal performance of deep reinforcement learning (DRL) in job-shop scheduling by proposing a DRL-guided improvement heuristic that uses a graph representation for complete solutions, resulting in outperforming state-of-the-art DRL-based methods by a large margin on classic benchmarks.
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 modelling partial solutions at each construction step. This paper proposes a novel DRL-guided improvement heuristic for solving 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 present a novel message-passing mechanism that can evaluate multiple solutions simultaneously. We prove that the computational complexity of our method scales linearly with problem size. 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.