Towards an Automatic Optimisation Model Generator Assisted with Generative Pre-trained Transformer
This work addresses the challenge of automating optimization model creation for researchers and practitioners, but it is incremental as it builds on existing language models and focuses on a specific domain.
The authors tackled the problem of automatically generating optimization models by using a pre-trained generative transformer to create initial versions in MiniZinc, with results showing feasibility as some models met specifications while others needed refinement.
This article presents a framework for generating optimisation models using a pre-trained generative transformer. The framework involves specifying the features that the optimisation model should have and using a language model to generate an initial version of the model. The model is then tested and validated, and if it contains build errors, an automatic edition process is triggered. An experiment was performed using MiniZinc as the target language and two GPT-3.5 language models for generation and debugging. The results show that the use of language models for the generation of optimisation models is feasible, with some models satisfying the requested specifications, while others require further refinement. The study provides promising evidence for the use of language models in the modelling of optimisation problems and suggests avenues for future research.