Evaluating Optimization Algorithms for Dynamic Workforce Management: A Comparative Approach

Effective workforce management is critical for optimizing operational efficiency in dynamic environments, particularly in healthcare settings. This paper presents a comparative analysis of five prominent optimization algorithms—Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Tabu Search (TS)—specifically applied to the problem of dynamic staffing needs in hospitals. Each algorithm is evaluated based on key performance metrics, including execution time, solution quality, scalability, and robustness. The study reveals that while GA and SA deliver high-quality solutions, ACO and TS are better suited for dynamic environments where conditions frequently change. PSO is recognized for its speed but may compromise on solution quality in more complex scenarios. The findings suggest that healthcare practitioners should consider hybrid approaches that combine the strengths of these algorithms to enhance workforce optimization. Recommendations for future research include the exploration of adaptive algorithms and the integration of machine learning techniques to further improve staffing efficiency in healthcare and other industries.