ReLA: Representation Learning and Aggregation for Job Scheduling with Reinforcement Learning
This work addresses scheduling inefficiencies in manufacturing systems, offering improved schedule quality and speed, though it is incremental as it builds on existing RL methods with novel representation techniques.
The paper tackles job scheduling in manufacturing by proposing ReLA, a reinforcement-learning scheduler that uses representation learning and aggregation, achieving reductions in optimality gaps of 13.0% on non-large instances and 78.6% on large-scale instances compared to state-of-the-art baselines.
Job scheduling is widely used in real-world manufacturing systems to assign ordered job operations to machines under various constraints. Existing solutions remain limited by long running time or insufficient schedule quality, especially when problem scale increases. In this paper, we propose ReLA, a reinforcement-learning (RL) scheduler built on structured representation learning and aggregation. ReLA first learns diverse representations from scheduling entities, including job operations and machines, using two intra-entity learning modules with self-attention and convolution and one inter-entity learning module with cross-attention. These modules are applied in a multi-scale architecture, and their outputs are aggregated to support RL decision-making. Across experiments on small, medium, and large job instances, ReLA achieves the best makespan in most tested settings over the latest solutions. On non-large instances, ReLA reduces the optimality gap of the SOTA baseline by 13.0%, while on large-scale instances it reduces the gap by 78.6%, with the average optimality gaps lowered to 7.3% and 2.1%, respectively. These results confirm that ReLA's learned representations and aggregation provide strong decision support for RL scheduling, and enable fast job completion and decision-making for real-world applications.