A Multi-Objective Genetic Algorithm for Healthcare Workforce Scheduling

2025-08-29

Summary

The article discusses the development of a Multi-Objective Genetic Algorithm (MOO-GA) designed to optimize healthcare workforce scheduling. This algorithm addresses the complex challenges of aligning staffing with patient demands, controlling labor costs, and ensuring staff satisfaction. By modeling the scheduling problem as a multi-objective optimization task, the MOO-GA outperforms traditional scheduling methods, achieving a 66% improvement over manual scheduling baselines.

Why This Matters

Efficient workforce scheduling in healthcare is crucial due to the sector's dynamic nature and the need to balance costs, patient care, and staff well-being. The MOO-GA provides a flexible decision-support tool that enables hospital administrators to navigate these competing demands effectively. This approach not only enhances operational efficiency but also has the potential to improve patient outcomes and reduce staff burnout.

How You Can Use This Info

Healthcare administrators can leverage this algorithm to create more balanced and efficient staff schedules, reducing costs while maintaining high standards of patient care. The insights from this study can also be applied to other industries facing similar scheduling challenges, such as retail and logistics, to optimize workforce management. By adopting advanced optimization tools like the MOO-GA, organizations can make more informed, data-driven staffing decisions.

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