Learning Flexible Job Shop Scheduling with Bidirectional Cross-Attention Network via Deep Reinforcement Learning

Neural combinatorial optimization NCO for solving scheduling problems have gained increasing research attention because they do not rely on expert knowledge. However, existing NCO approaches face significant challenges in the flexible job shop scheduling problem FJSP , because neural networks struggle to effectively capture the heterogeneous interactions among multiple machines and operations. To address this issue, we propose a bidirectional cross attention neural architecture trained by deep reinforcement learning. Our approach introduces dual interaction mechanism enables simultaneous learning of operation priorities and machine availability constraints. We demonstrate the effectiveness of this approach through extensive experi-ments, showing its superiority over classical network architectures on both synthetic datasets.