A multi-objective ACO for operating room scheduling optimization

• Published in: Natural computing Vol. 16; no. 4; pp. 607 - 617
• Main Author: Xiang, Wei
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2017; Springer Nature B.V
• Subjects: Ant colony optimization; Artificial Intelligence; Combinatorial analysis; Complex Systems; Computer Science; Computer simulation; Evolutionary Biology; Heuristic methods; Multiple objective analysis; Objectives; Pareto optimization; Processor Architectures; Resource management; Scheduling; Scheduling algorithms; Theory of Computation; Operating room scheduling; Ant colony optimization; Multi-objective optimization; Pareto set
• ISSN: 1567-7818; 1572-9796
• DOI: 10.1007/s11047-016-9607-9

Operating room (OR) scheduling problem is commonly recognized as a multi-objective combinatorial optimization problem with several objectives from different perspectives, e.g. minimizing waiting time from patients’ perspective, reducing overtime from medical staffs’ perspective, increasing resource utilization from OR management’s perspective etc. Those objectives are often conflicting. A meta-heuristic approach integrating Pareto sets and Ant Colony Optimization (ACO) is proposed to solve such multi-objective OR scheduling optimization problem. The Pareto sets construction and the modified ant graph model is introduced and two types of pheromone setting and updating strategies are compared to determine a more efficient multi-objective OR scheduling algorithm. The scheduling results by four different approaches, i.e. the simulation, the ACO with single objective of makespan (ACO-SO), the ACO with multi-objective by weighted sum method (ACO-weighted-sum), and the hybrid Pareto set-ACO with multi-objectives (PSACO-MO) are compared. The test case in the literature, which is from MD Anderson Cancer Center, is also used to evaluate the performance of the proposed approach. The computational results show that the PSACO-MO achieves good results in shortening makespan, reducing nurses’ overtime and balancing resources’ utilization in general.