Mechanism-Faithful Queueing Simulation Model Translation with Large Language Model Support

TL;DR

This paper introduces a framework that uses large language models to improve the translation of conceptual queueing models into executable simulation scripts, enhancing reliability and correctness.

Contribution

It presents a category-template framework with a staged workflow that improves script executability, compliance, and mechanism fidelity in queueing simulations.

Findings

Enhanced script executability and compliance across various queueing settings.

Better preservation of routing semantics and interruption-resume logic.

Identified remaining challenges in multi-node transfer and residual-service updates.

Abstract

Queueing simulation studies often require substantial manual effort to translate conceptual system descriptions into executable programs and to verify that the implemented mechanisms match the intended queueing logic. Although large language models (LLMs) may produce executable scripts, executability alone is insufficient when arrival, routing, interruption, or reporting logic is wrong. This study presents a simulation-oriented support framework for \texttt{SimPy}-based queueing model translation. We propose a category-template framework for mechanism coverage with a staged adaptation workflow that targets structured event logic and common simulation-specific failure modes. On held-out task instances, the adapted models improve executability, output-format compliance, and instruction-mechanism consistency across basic, behavioral, and networked queueing settings, so the generated scripts are more reliable as queueing simulation scripts. Error analysis shows better preservation of routing semantics and interruption-resume logic, while also exposing remaining weaknesses in multi-node transfer and residual-service updates. Overall, the results suggest that the proposed framework can act as a simulation-faithful generator for more standardized and reproducible queueing model construction.