[4A1-GS-10] AI application:

[4A1-GS-10-03] A Study on Generating Nurse Schedules from Natural Language Input Using Large Language Models

〇MITSUHISA OTA¹, Takashi Nishibayashi¹, Masahiro Kazama¹ (1. Ubie Inc.)

Keywords:Nurse Scheduling Problem, Mathematical Optimization, Large Language Model

Creating nurse schedules is a complex task that must account for a wide range of considerations, including the number of night shifts, the required combination of skills, and hospital-specific constraints. Although the field of mathematical optimization has long studied this issue as a “nurse scheduling problem,” translating real-world scheduling rules into a mathematical optimization model requires specialized knowledge, limiting its practical dissemination. In this study, we investigate a method to automatically convert scheduling rules written in natural language into a mathematical optimization model.While research on automatically formulating mathematical optimization problems with large language models (LLMs) has progressed, many real-world complex problems still pose challenges, partly due to the vast problem space involved. Our work narrows this space by focusing on the conditions commonly assumed in nurse scheduling problems, and then attempts to convert these rules into a mathematical optimization problem using an LLM. Preliminary validation suggests that it may be possible to generate nurse schedules solely from natural language input. We aim for this research to contribute to more efficient schedule creation in healthcare settings.