This research introduces an innovative framework that uses local LLMs to automatically construct MILP models for multi-robot task scheduling in manufacturing environments, preserving data privacy while improving efficiency.

• Automates the complex process of MILP model generation without requiring specialized domain expertise
• Addresses critical data privacy concerns by deploying LLMs locally rather than using cloud services
• Demonstrates adaptability to dynamic production constraints and changing manufacturing requirements
• Provides a practical solution for Industry 4.0 implementation in sensitive engineering environments

This research enables manufacturing systems to optimize robot coordination while maintaining security protocols, making advanced automation accessible to enterprises with strict privacy requirements.

Automatic MILP Model Construction for Multi-Robot Task Allocation and Scheduling Based on Large Language Models