Automated Conversion of Static to Dynamic Scheduler via Natural Language

TL;DR

This paper presents RAGDyS, a framework using Large Language Models to automatically convert static scheduling models into dynamic ones based on natural language constraints, reducing expert dependency and adapting to environmental changes.

Contribution

Introduction of RAGDyS, a Retrieval-Augmented Generation framework that automates dynamic scheduling model creation from static models using natural language, simplifying the process for end-users.

Findings

Successfully automates constraint modeling for dynamic scheduling

Reduces reliance on optimization experts

Enables quick adaptation to environmental changes

Abstract

In this paper, we explore the potential application of Large Language Models (LLMs) that will automatically model constraints and generate code for dynamic scheduling problems given an existing static model. Static scheduling problems are modelled and coded by optimization experts. These models may be easily obsoleted as the underlying constraints may need to be fine-tuned in order to reflect changes in the scheduling rules. Furthermore, it may be necessary to turn a static model into a dynamic one in order to cope with disturbances in the environment. In this paper, we propose a Retrieval-Augmented Generation (RAG) based LLM model to automate the process of implementing constraints for Dynamic Scheduling (RAGDyS), without seeking help from an optimization modeling expert. Our framework aims to minimize technical complexities related to mathematical modelling and computational workload for end-users, thereby allowing end-users to quickly obtain a new schedule close to the original schedule with changes reflected by natural language constraint descriptions.