A Data-Driven Two-Stage Robust Optimization Model for Sponge Iron Supply Chains under Market Volatility and Dual-Season Energy Disruption Risks - Badami, Mohammad Reza; M. Ahari, Roya; Ghandehari, Mahsa; Sherafati, Mahtab (Management Strategies and Engineering Sciences, 2026)
Energy-intensive process industries, particularly direct-reduced iron (DRI) and sponge iron manufacturing, face critical operational vulnerabilities stemming from dual-season energy disruptions—such as winter natural gas curtailments and summer electricity blackouts—compounded by volatile raw material market prices. Traditional deterministic planning and decoupled forecasting models often fail to account for overlapping systemic shocks, frequently leading to costly production halts or severe inventory stockouts. To bridge this gap, this study proposes a novel two-stage data-driven robust optimization model for sponge iron supply chains. The framework systematically translates empirical forecasting error residuals derived from deep learning models into polyhedral uncertainty sets governed by budgets of uncertainty ( ). Validated through a real-world case study of Mobarakeh Steel Company, the numerical results demonstrate that setting optimal uncertainty budgets at and achieves an optimal compromise point, yielding a 14.2% total operational cost reduction compared to deterministic baselines while completely preventing stockout-induced production halts.