Optimizing Hospital On-Call Scheduling Across Multiple Sites: A Collaborative Metaheuristic Approach.

Area of Science:

• Operations Research
• Healthcare Management
• Computer Science

Background:

• Medical staff scheduling is a critical yet complex operational challenge.
• Ineffective scheduling impacts patient care quality and healthcare professional well-being.
• Existing methods often struggle to balance diverse constraints and preferences.

Purpose of the Study:

• To develop and evaluate an innovative multi-agent system (MAS) for optimizing medical staff shift allocation.
• To enhance fairness in workload distribution and accommodate staff preferences.
• To minimize constraint violations in complex scheduling scenarios.

Main Methods:

• Implementation of a multi-agent system (MAS) integrating multiple collaborative optimization algorithms.
• Utilizing heuristic approaches for managing multiple, dynamic schedules.
• Testing the system with both simulated and real-world medical scheduling data.

Main Results:

• The MAS approach demonstrated significant improvements in scheduling efficiency and adaptability.
• The system effectively balanced workload, optimized staff preferences, and minimized constraint violations.
• Heuristics proved particularly effective in managing complex, multi-schedule environments.

Conclusions:

• Collaborative optimization within a MAS offers a robust solution for complex medical staff scheduling.
• This approach enhances operational efficiency and staff satisfaction in healthcare settings.
• The developed system provides a flexible and adaptable tool for real-world scheduling challenges.