Intelligent Intralogistics in the context of Reconfigurable Material Handling Systems based on LLMs

Intralogistics are a cornerstone for the operation of manufacturing/production systems. In the realm of industry 4.0 manufacturing transformation includes intelligent intralogistics as well, focusing on extending system’s capabilities towards dynamic, efficient, and adaptive material/part handling processes. Therefore, in this research work a framework for intelligent intralogistics within reconfigurable production environments is presented based on the integration AI-driven scheduling systems and natural language interfaces powered by Large Language Models (LLMs). The proposed approach combines real-time data acquisition, autonomous mobile robot coordination, and human-centric interaction for addressing intralogistics challenges (e.g., workflow variability, congestion, and traceability). Through representative industrial use cases, the framework demonstrates its ability to enhance operational flexibility, reduce downtime, and improve ergonomics for shop-floor personnel. The use of LLMs further facilitates intuitive communication between operators and machines, facilitating Human-Machine Interaction (HMI).