Efficient Maritime Healthcare Resource Allocation Using Reinforcement Learning

The allocation of healthcare resources on ships is crucial for safety and well-being due to limited access to external aid. Proficient medical staff on board provide a mobile healthcare facility, offering a range of services from first aid to complex procedures. This paper presents a system model utilizing Reinforcement Learning (RL) to optimize doctor-patient assignments and resource allocation in maritime settings. The RL approach focuses on dynamic, sequential decision-making, em- ploying Q-learning to adapt to changing conditions and maximize cumulative rewards. Our experimental setup involves a simulated healthcare environment with variable patient conditions and doctor availability, operating within a 24-hour cycle. The Q- learning algorithm iteratively learns optimal strategies to enhance resource utilization and patient outcomes, prioritizing emergency cases while balancing the availability of medical staff. The results highlight the potential of RL in improving healthcare delivery on ships, demonstrating the system's effectiveness in dynamic, time-constrained scenarios and contributing to overall maritime safety and operational resilience.