Pretrained LLMs as Real-Time Controllers for Robot Operated Serial Production Line

2025-03-05

AI Summary

Manufacturing mobile robot scheduling faces bottlenecks including poor customizability, high computational overhead, and opaque decision-making. Method: This paper proposes a novel real-time control paradigm leveraging off-the-shelf large language models (LLMs), specifically un-fine-tuned GPT-4, for serial production line robot scheduling. It employs natural language instruction parsing, rule-guided prompt engineering, real-time state encoding, and action-mapping interfaces to enable lightweight deployment—without domain-specific fine-tuning or reinforcement learning retraining. Contribution/Results: The approach ensures interpretability, scalability, and low computational dependency. Experiments demonstrate that its throughput optimization performance surpasses classical heuristics (e.g., FCFS, SPT, LPT) and matches that of multi-agent reinforcement learning (MARL), while substantially reducing expert involvement and computational resource consumption.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The manufacturing industry is undergoing a transformative shift, driven by cutting-edge technologies like 5G, AI, and cloud computing. Despite these advancements, effective system control, which is crucial for optimizing production efficiency, remains a complex challenge due to the intricate, knowledge-dependent nature of manufacturing processes and the reliance on domain-specific expertise. Conventional control methods often demand heavy customization, considerable computational resources, and lack transparency in decision-making. In this work, we investigate the feasibility of using Large Language Models (LLMs), particularly GPT-4, as a straightforward, adaptable solution for controlling manufacturing systems, specifically, mobile robot scheduling. We introduce an LLM-based control framework to assign mobile robots to different machines in robot assisted serial production lines, evaluating its performance in terms of system throughput. Our proposed framework outperforms traditional scheduling approaches such as First-Come-First-Served (FCFS), Shortest Processing Time (SPT), and Longest Processing Time (LPT). While it achieves performance that is on par with state-of-the-art methods like Multi-Agent Reinforcement Learning (MARL), it offers a distinct advantage by delivering comparable throughput without the need for extensive retraining. These results suggest that the proposed LLM-based solution is well-suited for scenarios where technical expertise, computational resources, and financial investment are limited, while decision transparency and system scalability are critical concerns.

Problem

• Optimizing production efficiency in manufacturing systems.
• Reducing reliance on domain-specific expertise and computational resources.
• Enhancing decision transparency and system scalability in robot scheduling.

Innovation

• LLMs control robot scheduling in manufacturing
• Framework outperforms FCFS, SPT, LPT methods
• No extensive retraining needed for high throughput