Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and Efficiency

The Anaesthetist Rostering Problem (ARP) presents significant challenges in healthcare management due to complex constraints and regulations. Existing models for the ARP often fail to address the complexities of real-world hospital environments, particularly in integrating monthly and weekly schedules across multiple locations. This study addresses the key question: How can we develop anaesthetist rosters that improve both staff fairness and operational efficiency while meeting complex hospital requirements? To address this challenge, we propose a novel mixed-integer linear programming model with updated constraints, parameters, and an enhanced evaluation function. We implemented the model at Hospital Canselor Tuanku Muhriz (HCTM), Malaysia, to handle various shift types across multiple locations for both monthly and weekly rosters. The proposed model optimises the roster by satisfying mandatory constraints first (such as legal requirements), and then minimising soft constraint violations, such as employee preferences, through iterative refinement. The evaluation function assesses roster quality by minimising penalties for soft constraint violations. We modified the evaluation function and constraints to accommodate HCTM’s specific shift patterns and rest requirements, enhancing fairness, flexibility, and workload distribution. The model reduced penalties by 69.57% for monthly rosters and 64.37% for weekly rosters compared to manual scheduling. Statistical analysis proved significant enhancement in monthly rosters and weekly rosters. The model improves workload fairness and scheduling efficiency, bridging theoretical models and practical applications. It contributes to healthcare workforce management by offering better resource allocation and increased staff satisfaction.