EvoOpt-LLM: Evolving Industrial Optimization Models With Large Language Models

Supply-chain production and transportation models must account for changing production, workforce, inventory, and delivery rules while retaining the relationships among existing decisions. We present EvoOpt-LLM, an LLM-based approach to constructing an initial optimization model, injecting additional business constraints, and predicting candidate zero fixings before numerical solution. Three independently trained LoRA adapters generate formulations and code from natural-language problems, updated models from LP files and business rules, and variable-fixing lists from the current LP text. The supply-chain application centers on linking new rules to existing production decisions and conditioning fixing predictions on the model being solved. We evaluate the modules separately. A multicategory construction corpus measures basic modeling ability: with 3,000 examples, generation reaches 91%, while executability and reference-result matching among generated models reach 65.9% and 26.37%. A workforce-update case shows how adaptation changes the organization of a generated business-rule extension. With 400 pruning examples, variable-label F1 peaks near 0.56 on models of around 350–400 lines and declines on longer inputs. The results characterize LLM assistance for initial formulation, business-rule updates, and candidate variable fixing in supply-chain modeling.