# A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor

arXiv: https://arxiv.org/abs/2405.16887 · HTML: https://mlsanity.com/paper/2405.16887/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 27 May 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [RT-H: Action Hierarchies Using Language](https://mlsanity.com/paper/2403.01823/) (2024)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [Prompt a Robot to Walk with Large Language Models](https://mlsanity.com/paper/2309.09969/) (2023)
- [Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems](https://mlsanity.com/paper/2307.06187/) (2023)
- [Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention](https://mlsanity.com/paper/2103.00213/) (2021)

## Built on by (most cited first, 4 of 4)

- [LLM-Powered AI Agent Systems and Their Applications in Industry](https://mlsanity.com/paper/2505.16120/) (2025)
- [DDD-GenDT: Dynamic Data-driven Generative Digital Twin Framework](https://mlsanity.com/paper/2501.00051/) (2024)
- [Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges](https://mlsanity.com/paper/2605.02592/) (2026)
- [An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing](https://mlsanity.com/paper/2605.13221/) (2026)

## Abstract (arXiv)

As customer demand for multi-variety and small-batch production increases, dynamic disturbances place greater demands on manufacturing systems. To address such challenges, researchers proposed the multi-agent manufacturing system. However, conventional agent negotiation typically relies on pre-defined and fixed heuristic rules, which are ill-suited to managing complex and fluctuating disturbances. In current implementations, mainstream approaches based on reinforcement learning require the development of simulators and training models specific to a given shopfloor, necessitating substantial computational resources and lacking scalability. To overcome this limitation, the present study proposes a Large Language Model-based (LLM-based) multi-agent manufacturing system for intelligent shopfloor management. By defining the diverse modules of agents and their collaborative methods, this system facilitates the processing of all workpieces with minimal human intervention. The agents in this system consist of the Machine Server Module (MSM), Bid Inviter Module (BIM), Bidder Module (BM), Thinking Module (TM), and Decision Module (DM). By harnessing the reasoning capabilities of LLMs, these modules enable agents to dynamically analyze shopfloor information and select appropriate processing machines. The LLM-based modules, predefined by system prompts, provide dynamic functionality for the system without the need for pre-training. Extensive experiments were conducted in physical shopfloor settings. The results demonstrate that the proposed system exhibits strong adaptability, and achieves superior performance (makespan) and stability (as measured by sample standard deviation) compared to other approaches without requiring pre-training.