An ant colony optimization approach for solving an operating room surgery scheduling problem

Operating room surgery scheduling deals with determining operation start times of surgeries on hand and allocating the required resources to the scheduled surgeries, considering several constraints to ensure a complete surgery flow, the resource availability, and specialties and qualifications of human resources. This task plays a crucial role in providing timely treatments for the patients while ensuring the balance in the hospital's resource utilization. By observing similarities between operating room surgery scheduling and a multi-resource constraint flexible job shop scheduling problem (FJSSP) in manufacturing, this article proposes an Ant Colony Optimization (ACO) approach to efficiently solve such surgery scheduling problems based on the knowledge gained in FJSSP. Numerical experiments are performed on five surgery test cases with different problem sizes and resource availability. The performance of the ACO algorithm was compared against schedules generated by a discrete event system simulation model built in SIMIO on five test cases. The results showed a superior performance of ACO in makespan, overtime, and the variation coefficient of working time.