Automated Conversion of Static to Dynamic Scheduler via Natural Language

Understand

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.