Surgery scheduling based on large language models

Large Language Models (LLMs) have shown remarkable potential in various fields. This study explores their application in solving multi-objective combinatorial optimization problems-surgery scheduling problem. Traditional multi-objective optimization algorithms, such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II), often require domain expertise for designing precise operators. Here, we propose LLM-NSGA, where LLMs act as evolutionary optimizers, performing selection, crossover, and mutation operations. Results show that for 40 cases, LLMs can independently generate high-quality solutions from prompts. As problem size increases, LLM-NSGA outperformed traditional approaches like NSGA-II and MOEA/D, achieving average improvements of 5.39 %, 80 %, and 0.42 % in three objectives. While LLM-NSGA provided similar results to EoH, another LLM-based method, it outperformed EoH in overall resource allocation. Additionally, we applied LLMs for hyperparameter optimization, comparing them with Bayesian Optimization and Ant Colony Optimization (ACO). LLMs reduced runtime by an average of 23.68 %, and their generated parameters, validated with NSGA-II, produced better surgery scheduling solutions. This demonstrates that LLMs can not only help traditional algorithms find better solutions but also optimize their parameters efficiently.