Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process

본문 미리보기

arXiv:2608.00015v1 Announce Type: new Abstract: Both optimization modeling and constraint modeling are non-trivial problems requiring deep domain expertise and proficiency in modeling formalism languages. Despite their importance across logistics, healthcare, and supply chain management, current large language models regularly produce structurally inconsistent or incomplete optimization formulations, particularly in combinatorial settings. This paper evaluates whether a Retrieval-Augmented Gene