Pareto Set-based Ant Colony Optimization for Multi-Objective Surgery Scheduling Problem

Surgery scheduling determines the individual surgery’s sequence and assigns required resources. This task plays a decisive role in providing timely treatment for the patients while ensuring a balanced hospital resources’ utilization. Considering several real life constraints associated with multiple resources during the complete 3-stage surgery flow, a surgery scheduling model is presented with multiple objectives of minimizing makespan, minimizing overtime and balancing resource utilization. A Pareto sets based ant colony algorithm with corresponding ant graph, pheromone setting and update, and Pareto sets construction is proposed to solve the multi-objective surgery scheduling problem. A test case from MD Anderson Cancer Center is built and the scheduling result by three different approaches is compared. The case study shows that the Pareto set-based ACO for multi-objective proposed in this paper achieved good results in shortening total end time, reducing nurses’ overtime and balancing resources’ utilization in general. It indicates the advantage by systematically surgery scheduling optimization considering multiple objectives related to different shareholders.