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Multi-agentic production planning utilising simulation and optimisation
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PRE
AI
DOI：10.1016/j.cirp.2026.04.027.png)
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Abstract
En 中文
While applications of artificial intelligence proliferate, planning complex, volatile production remains a challenge. Hybrid methods that enable large language models to interact with classic models, such as simulation and optimisation, could address this problem. This work develops a workflow-based multi-agent architecture that generates production plans from unstructured user prompts. The system is tested using data from an automotive supplier, with 7 distinct experiments in lot sizing and setup sequencing, using simulated validation. The experiments show the system can reliably create suitable production plans while reducing execution times compared to manual planning. This work presents a step towards hybrid artificial intelligence. (c) 2026 The Authors. Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Keywords:
Artificial intelligence
Production planning
Scheduling
Large language models
Journal
C
IF:
3.6
Papers:
32
Citations:
0
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