Mathematical Decision Layers for Technical Proposal Generation in Industrial Electrical Houses Using Generative AI

Industrial electrical houses are engineered systems that transform and control electrical
power to supply industrial loads. Preparing technical proposals for these rooms requires
consistent engineering choices across multiple artifacts while drawing from heterogeneous
client documents, historical projects, and supplier catalogs. This paper reports an industrial
prototype that integrates generative AI, system modeling, and mathematical decision
methods to support that workflow. We represent requested outputs as ordered sequences
of functions and link those functions to candidate equipment blocks through functional
and physical graphs that enable traceable retrieval and reuse. Using this representation, we
compute a minimal internal-cost baseline by solving a mixed-integer assignment model
with sizing constraints, and we rank technically feasible alternatives using fuzzy DEMATEL
to derive criterion weights and TOPSIS to obtain an overall ordering under multiple
criteria. The workflow is illustrated with an example and the prototype tool used in a
company operating in Chile, Peru, Ecuador, and Bolivia, where document ingestion and
equipment-list extraction are integrated with human validation. The results illustrate how
structured representations, optimization, and multi-criteria ranking can support auditable
configurations for engineering review and commercial selection.