Title: Foundation Models for Industrial Scheduling Leveraging the Techniques from LLMs
Primary area: applications to robotics, autonomy, planning
Keywords: Industrial Scheduling, LLMs, reinformance learning, Scaling law

Abstract: The advent of large language models (LLMs) has significantly boosted productivity across various sectors. However, their application in the industrial domain remains underexplored and often yields suboptimal results, primarily due to stringent requirements for technological maturity, safety, and standardization. 
To address this gap, we leverage key techniques instrumental to the success of LLMs—such as the decoder-only architecture and scaling laws—rather than using LLMs directly, to develop a foundational model for industrial scheduling. In contrast to prior methods that focus on specific types of scheduling problems, our model is designed as a general-purpose framework capable of handling diverse task operations, objectives, and constraints reflective of real-world industrial environments. 
Through extensive experiments, our foundation models have demonstrated clear superiority over conventional scheduling methods and algorithms using LLMs directly. Notably, the foundation models for scheduling have exhibited scaling law, generalization ability, and adaptability analogous to those observed in LLMs. 
These results indicate that the principles underpinning LLMs extend beyond natural language processing, showing strong potential for broader industrial and manufacturing applications. 
Code at \url{https://anonymous.4open.science/r/Foundation-Models-for-Industrial-Scheduling-7BD4}

TL;DR: Leveraging the techniques from LLMs to develop foundation models to solve real-world industrial scheduling problems