Emergency Surgical Scheduling Model Based on Moth-flame Optimization Algorithm

In this paper, we propose an optimization approach based on an improved Moth Flame Optimization (MFO) algorithm for solving emergency operating room scheduling problems. The purpose of the MFO is to minimize the maximum span of operations, ensuring patients receive their surgeries in a timely manner. This nature-inspired algorithm stimulates the moth's special navigation method at night called transverse orientation. The moth uses the moonlight to sustain a fixed angle to the moon, therefore, guaranteeing a straight line. However, a light source can cause a useless or deadly spiral fly path for moths. The results show that MFO has advantages over Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), particularly when comparing the performance of the algorithms under different spiral curves when considering the unrestricted use of surgical beds between different procedures and the optimization of algorithm speed.