Context-Aware LLM-Guided Neighbourhood Search for Simulation-Based Production Scheduling

Abstract

The paper presents a novel large language model-based neighbourhood search method for production scheduling using discrete-event simulation (DES) as the modelling and evaluation engine. The combination of large language models (LLMs) and DES represents a relatively new area of research with significant potential. The current study aims to demonstrate the applicability of LLMs for the automated closed-loop optimisation of production systems, using the DES environment as a configurable modelling tool and an evaluation engine at the same time, from which the results and the related contextual information are extracted at the end of each simulation run to provide feedback and context for the LLM in its autonomous search for improved solutions. The architecture was implemented with the use of Mistral Small 3.2 (24B) as the LLM and Siemens Tecnomatix Plant Simulation as the DES environment. The results from the proposed approach were compared with those of a genetic algorithm (GA) native to the applied DES environment, and with those achieved using simulated annealing (SA). While the proposed method was generally outperformed by the GA and the SA in terms of final solution quality and computational cost, it demonstrated advantages in terms of simulation demand while also providing a highly adaptable optimisation framework with significant potential for improvement and wider generalisation in the future.