ReLA: Representation Learning and Aggregation for Job Scheduling with Reinforcement Learning

TL;DR

ReLA is a reinforcement learning-based scheduler that uses structured representation learning and aggregation to improve job scheduling efficiency and quality across various instance sizes, significantly reducing optimality gaps.

Contribution

ReLA introduces a novel RL scheduler with multi-scale representation learning and aggregation, enhancing scheduling performance and scalability in manufacturing systems.

Findings

ReLA achieves the best makespan in most tested settings.

Reduces optimality gap by 13.0% on small/medium instances.

Reduces optimality gap by 78.6% on large instances.

Abstract

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.