Authors - Shashank Mallesh, Anithadevi M D, Srinidhi G A, Chandana Sreenivas Abstract - Hospital surge capacity management remains a critical challenge in healthcare systems, particularly during pandemic events. This research presents a novel Adaptive Multi-Objective Capacity Management (AMCM) framework that integrates Long Short-Term Memory (LSTM) networks with Multi-Objective Particle Swarm Optimization (MOPSO) to optimize bed allocation, staffing schedules, and equipment distribution. The framework simultaneously minimizes patient wait times, operational costs, and resource wastage while maximizing bed utilization efficiency. Comprehensive evaluation against state-of-the-art algorithms including Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and traditional Mixed Integer Linear Programming (MILP) demonstrates superior performance across all metrics. The proposed method achieves 94.7% bed utilization accuracy, reduces emergency department wait times by 42.3%, and decreases surge-related costs by 38.9% compared to conventional approaches. Validated on real-world COVID-19 hospital data spanning 18 months across five major health systems, the AMCM framework provides healthcare administrators with an intelligent decision support system for proactive capacity planning and dynamic resource allocation.
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