Surgery scheduling problem considering the affinity and preferences in the surgical team

Surgery scheduling is crucial in healthcare management, particularly in hospitals and clinics. This study tackles the elective surgery scheduling problem by integrating affinity and preferences among the surgical team's members. Although these concepts can enhance coordination and improve team performance, they remain understudied in the literature. Affinity is usually quantified as a numerical representation of compatibility between team members, and preferences denote a surgeon's interest in specific surgical resources. Existing approaches have not integrated simultaneously affinity and preferences. In addition, they use mathematical programming models that often incorporate affinity and preferences as constraints or additional objective function terms, adopting a multi-objective approach. The former can significantly reduce the number of surgeries performed, while the latter increases computational complexity. To overcome these limitations, we propose mathematical programming models with a score-based penalty approach that integrates affinity and preferences while maximizing the priority of scheduled surgeries. Our approach is evaluated against two alternative models: a baseline model without affinity or preferences and a constraint-based model that follows conventional literature, incorporating these concepts as hard constraints. We implement these models using integer linear programming and constraint programming. The results show the feasibility of considering affinity and preferences among surgical team members. This can enhance the surgical team's quality with negligible impact on the number of surgeries performed. Therefore, our approach can generate stronger human relationships among surgical team members, which could contribute positively to patient surgical outcomes, as demonstrated by some studies in the literature.