Any-scale Flexible Job Shop Scheduling via Dual-attention Proximal Policy Optimization

Flexible job shop scheduling (FJSS) plays a critical role in modern intelligent manufacturing systems. With the rapid development of industrial production environments, learning-based scheduling has emerged as a promising paradigm for learning effective scheduling policies from data and enabling fast decision-making. However, existing methods struggle to generalize across variable-sized instances, learn heterogeneous interactions among operations, machines, and processing-time information, and generate reliable decisions under dynamic scheduling constraints. To address these challenges, we propose a dual-attention proximal policy optimization framework for pre-training a generalizable neural scheduler across FJSS scales. Specifically, we introduce a dual-attention representation mechanism that explicitly models the heterogeneous interactions among operations, machines, and dynamically updated completion-time information. An online mask further incorporates scheduling information during sequential decision-making by masking infeasible actions, thereby improving decision reliability and training efficiency. The proposed framework is pre-trained on a large collection of synthetic FJSS instances with diverse job-machine configurations, using high-quality Google Operations Research Tools solutions to construct informative reward and advantage signals. Extensive experiments on 217 FJSS instances from six benchmark families and 129 large-scale synthetic instances, covering problem sizes from 3 × 2 to 100 × 100 (job by machine), demonstrate strong cross-family and cross-scale generalization, with the proposed framework consistently outperforming representative heuristics and state-of-the-art learning-based methods on unseen instances.