Persistent imbalances between electricity supply and demand remain a significant challenge in many developing economies, where energy-intensive industries exert considerable pressure on power systems. Demand response (DR) programs, particularly Time-of-Use (TOU) pricing, are widely implemented to encourage load shifting and mitigate peak demand. However, in heavy industries such as steelmaking, the effectiveness of TOU tariffs is strongly influenced by operational feasibility, production continuity requirements, and the imprecision of production-driven energy needs. This study develops an optimization-based framework for industrial demand response scheduling using operational and tariff data from the Iranian Alloy Steel Company. The deterministic formulation is first developed as a baseline model to minimize electricity costs while satisfying operational and production constraints. The proposed approach then extends this formulation within a credibility-based possibilistic programming framework, where production-driven uncertainty in daily energy requirements is represented using triangular fuzzy numbers. The results indicate that implementing the possibilistic formulation improves operational reliability by increasing the off-peak load share from 35% to 42% under uncertain production conditions, incurring only a marginal cost increase of 4.85% compared to the baseline deterministic model. Furthermore, the credibility-based schedule reduces the peak load exposure by 5.7% (from 140 MWh to 132 MWh), avoiding excessive peak-hour concentration. Overall, the findings quantify a clear trade-off, revealing that a 4.85% investment in an operational safety margin yields a more balanced and secure schedule, significantly reducing the risk of severe cost escalations in steelmaking demand response.