Optimizing Small-Scale Surgery Scheduling with Large Language Model

Abstract

Large Language Model (LLM) have recently been widely used in various fields. In this work, we apply LLMs for the first time to a classic combinatorial optimization problem—surgery scheduling—while considering multiple objectives. Traditional multi-objective algorithms, such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), usually require domain expertise to carefully design operators to achieve satisfactory performance. In this work, we first design prompts to enable LLM to directly solve small-scale surgery scheduling problems. As the scale increases, we introduce an innovative method combining LLM with NSGA-II (LLM-NSGA), where LLM act as evolutionary optimizers to perform selection, crossover, and mutation operations instead of the conventional NSGA-II mechanisms. The results show that when the number of cases is up to 40, LLM can directly obtain high-quality solutions based on prompts. As the number of cases increases, LLM-NSGA can find better solutions than NSGA-II.