Hybrid Genetic Algorithm and Deep Reinforcement Learning Framework for IoT-Enabled Healthcare Equipment Maintenance Scheduling

We study the predictive maintenance scheduling for IoT-enabled medical equipment in multi-facility healthcare networks. The problem involves skill matching, time windows, and risk-aware priorities. We model a multi-skill Technician Routing and Scheduling Problem with IoT-predicted failure intervals and minimize a composite cost for technician activation and labor, travel/time, risk exposure within the failure window, and lateness beyond it. We propose a hybrid solver coupling a Genetic Algorithm (GA) for rapid exploration and feasible schedule generation with a Proximal Policy Optimization (PPO) agent warm-started via behavior cloning on GA elites and refined online in a receding-horizon manner. An optional, permissioned blockchain records tamper-evident maintenance events off the control loop for auditability. Across four case studies (10–30 facilities), the hybrid approach reduces total cost by 2.09–10.31% versus pure GA, by 0.57–2.65% versus pure Deep Reinforcement Learning (DRL), and by 0.93–2.86% versus OR-Tools VRP heuristic baseline. In controlled early-stopping runs guided by admissible GA/DRL time splits, we realized average wall-time savings up to 47.5% while keeping solution costs within 0.5% of full-budget runs and maintaining low or zero lateness and risk exposure. These results indicate that GA seeding improves sample efficiency and stability for DRL in complex, data-driven maintenance settings, yielding a practical, adaptive, and auditable scheduler for healthcare operations.