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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.06543 |
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| _version_ | 1866914529709916160 |
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| author | Chen, Jun-Qi Zhang, Kun Zheng, Rui Zhong, Ying |
| author_facet | Chen, Jun-Qi Zhang, Kun Zheng, Rui Zhong, Ying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06543 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mechanism-Faithful Queueing Simulation Model Translation with Large Language Model Support Chen, Jun-Qi Zhang, Kun Zheng, Rui Zhong, Ying Computation and Language Artificial Intelligence Machine Learning 60K99, 90-10, 93C65 I.6.2; I.2.2 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. |
| title | Mechanism-Faithful Queueing Simulation Model Translation with Large Language Model Support |
| topic | Computation and Language Artificial Intelligence Machine Learning 60K99, 90-10, 93C65 I.6.2; I.2.2 |
| url | https://arxiv.org/abs/2601.06543 |