Out-of-Distribution Modular Hospital Fit-Out Scheduling via Memory-augmented Deep Reinforcement Learning^*

The transition from Industry 4.0 to Industry 5.0 has highlighted the critical role of Modular Integrated Construction (MiC), particularly in rapidly deployable modular hospitals that address urgent healthcare demands. As the final stage before delivery, fit-out directly impacts both project speed and healthcare quality. However, scheduling in this phase faces challenges from dynamic labor allocation and worker fatigue, which traditional methods struggle to handle in out-of-distribution (OOD) settings. To tackle this, we reformulate the problem as a Flexible Job-shop Scheduling Problem with Workload Constraints (WL-FJSP) and propose a memory-augmented framework that models worker-task dynamics. By incorporating adaptive gating mechanisms, the model captures fatigue variations and jointly optimizes medical task fulfillment and fit-out efficiency. Experiments show improved performance over traditional and state-of-the-art methods, with strong generalization across varying instance scales.