Optimizing Operating Room Scheduling Using Bat Algorithm

Abstract

Operating room (OR) scheduling is a difficult combinatorial optimization problem and has an importance in hospital resource management. Effective scheduling is critical to utilizing operating rooms effectively and avoiding long waiting times, as well as minimizing costs. Due to the high computational complexity of the Mixed Integer Programming (MIP) in large scale instances, meta heuristics such as Bat algorithm (BA) can provide strong alternatives to solve this problem. In this study, a new model for optimization of surgical scheduling with considering performance measures such as makespan, waiting time and scheduling cost via BA is introduced. Stochastic elements are introduced in the schedule generator, using Pearson III and normal distributions to generate surgery times according to real-life situations. Experiments show that heuristic approaches based on computational efficiency, especially in larger instances when exact solvers are unfeasible. It is the better performance of swarm-intelligence-based algorithms over traditional methods in generating high-quality schedule solutions with the lowest possible execution time. The results of this study are practical for intelligent decision support systems for hospital scheduling optimization.