  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.
