AI-DRIVEN RESILIENCE: OPTIMIZING LARGE INDUSTRIAL PORTFOLIOS IN THE ERA OF ENGINEERING MANAGEMENT 5.0

Abstract

The accelerating complexity of large industrial asset portfolios demands decision-support frameworks that transcend traditional optimization paradigms. This paper proposes the Industrial Portfolio AI Optimizer (IPAO) — a hybrid framework integrating multi-objective optimization (NSGA-II), Monte Carlo simulation, and Analytic Hierarchy Process (AHP) weighting — for industrial asset allocation under uncertainty. Validated on a synthetic 48-asset energy portfolio, IPAO yields a 29.2% gain in composite performance over mean-variance optimization, a 65.8% improvement in ESG compliance, and a 34.2% reduction in tail risk (VaR95). Embedded SHAP-based explainability satisfies governance requirements under the EU AI Act. The framework operationalizes the Industry 5.0 triad — technology, sustainability, and human-centricity — within a single auditable architecture for engineering management.