Pareto Set-based Ant Colony Optimization for Multi-Objective SurgeryScheduling Problem

Abstract

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' utiliza- tion. 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 bal- ancing 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 sys- tematically surgery scheduling optimization considering multiple objectives related to different shareholders.