Surgery scheduling based on large language models

Fang Wan

doi:10.1016/j.artmed.2025.103151

Surgery scheduling based on large language models

2025-05-28

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DOI：10.1016/j.artmed.2025.103151

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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 LLMbased method, it outperformed EoH in overall resource allocation. Additionally, we applied LLMs for hyper-parameter 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.

Keywords:

Surgery scheduling

Large language models

Combinatorial optimization

Multi-objective

Hyperparameter optimization

Surgery scheduling based on large language models

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