An Adaptive Scheduling Framework for Healthcare Workforce Optimization Using PSO–GA Hybrid Algorithms
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Abstract
An efficient hospital workforce schedule is critical to the quality of patient care, nurse fatigue, and resource utilization. Traditional scheduling methods, manual planning and rules-based heuristics, are not flexible and cannot adjust to variability in patient demand in real-time. We propose a hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA) model, where PSO rapidly explores global solutions and GA refines them via crossover/mutation, ensuring workload balance and constraint satisfaction. Tested on real data from an urban hospital (50 doctors, 100 nurses, 20 operating rooms), the model reduced patient wait times by 66.7% (from 4.5 to 1.5 hours) and staff overtime by 40% compared to rule-based methods, while maintaining moderate computational efficiency (25% faster than GA-only) Performance profiling and performance comparisons indicated that both efficiency and effectiveness were improved using the hybrid PSO-GA method, compared with conventional scheduling methods. This study presents a scalable answer to the modern-day scheduling issues with the healthcare context, while adapting to the fluctuating demands of patient care settings in real-time.
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References
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