Medical staff scheduling is a complex challenge with significant implications for patient care and staff well-being. This study presents an innovative approach that combines multiple optimization algorithms working collaboratively in a multi-agent system (MAS) to address shift allocation. By integrating these algorithms, the method ensures fair distribution, optimizes staff preferences, and minimizes constraint violations, effectively balancing workload and respecting individual requests. Tested on both simulated and real-world data, the solution demonstrates enhanced scheduling efficiency and adaptability particularly using heuristics for managing multiple schedules.
Ajmi et al. (Thu,) studied this question.