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arXiv cs.AI· Javier Pernas-\'Alvarez, Diego Crespo-Pereira·· 10 小时前AI 评分40

SimuLean:面向制造系统设计的智能体离散事件仿真开源 3D 模拟器与基准

Agentic discrete-event simulation for manufacturing system design: an open 3D simulator, a benchmark and an industrial case

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研究团队推出 SimuLean——基于 Unity 引擎的开源 3D 离散事件仿真(DES)模拟器,可由 AI 智能体通过领域级命令建模,在 18 项制造建模任务的 108 次尝试中,前沿智能体成功构建 93 个有效模型(86%),每个约耗时两分钟。

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Abstract:The design of manufacturing systems relies on discrete-event simulation (DES) models that experts still build in tool-specific interfaces. AI agents can now write simulation code, but every model they write from scratch is new, unverified software. We present SimuLean, an open-source 3D DES simulator on the Unity engine designed to be programmed by AI agents: its modelling semantics are verified once, in the platform, and the agent builds and studies one model through domain-level commands, in the 3D scene or headless; physically simulated durations are calibrated for fast headless studies. We evaluated it on 18 manufacturing modelling tasks, from single machines to published production lines. A frontier agent built valid models in 93 of 108 attempts (86%), in about two minutes each; on the benchmark's interface version, two frontier agents built 105 of 108 models through domain commands alone, so that each comes with a record of how it was built. Every residual failure was silent - a model that builds and runs but answers wrongly - and only comparison with reference models revealed them all. In an industrial case, one agent working alone rebuilt, verified and studied a robotised welding workshop from a published study, in SimuLean and in SimPy. Comparing them with the published, expert-built model exposed an error in it; against the corrected model, the agent-built models agree within 1%. One agent can already build complete, realistic and traceable models for the design of manufacturing systems; independent reference models are what make them trustworthy.
Comments: 55 pages, 15 figures, 8 tables. Software: this https URL (doi:https://doi.org/10.5281/zenodo.23565564)
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2611.10602 [cs.AI]
  (or arXiv:2611.10602v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2611.10602

arXiv-issued DOI via DataCite

Submission history

From: Javier Pernas-Álvarez [view email]
[v1] Sat, 7 Nov 2026 09:12:09 UTC (2,711 KB)
[v2] Tue, 10 Nov 2026 07:53:28 UTC (2,711 KB)

来源:arXiv cs.AI · arxiv.org