Two-Stage Stochastic Programming and Robust Optimization Models for Resilient Supply Chain Network Design under Uncertainty: A Real Case Study

This paper investigates the resilient multi-period, multi-stage supply chain (SC) network design problem under demand and raw material quality uncertainty within a just-in-time (JIT) distribution setting, based on a real case study. The proposed approach models a four-stage SC comprising suppliers, manufacturers, distributors, and retailers, and develops two-stage stochastic programming and robust optimization models to enhance resilience. Unlike existing studies, this research uniquely integrates JIT distribution with the simultaneous consideration of demand and raw material quality uncertainties, providing practical, data-driven insights for decision-makers. Computational results show that the proposed models produce applicable solutions for real-world implementation. Across all models, high-quality raw materials are preferred 54% in the deterministic model, 55% on average in 30 of 40 stochastic scenarios, and 52% in the robust model, even under worst-case conditions. These findings indicate that prioritizing high-quality raw materials, despite higher purchasing costs, is crucial for maintaining JIT principles and ensuring on-time deliveries. Furthermore, the results highlight that the strategic location of distributors is critical to meeting retailers’ demand at the right time and in the right quantity.