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Journals / Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics / 2026 / Volume: 75 Issue: 1

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

Beren Gürsoy Yılmaz(KARADENİZ TEKNİK ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0001-7493-0639
Ömer Faruk Yılmaz(KARADENIZ TECHNICAL UNIVERSITY, FACULTY OF ENGINEERING)0000-0002-6202-4011
Selin Soner Kara(YILDIZ TECHNICAL UNIVERSITY)0000-0002-0894-0772
Pages
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Publication Date
2026-03-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Computational results show that the proposed models produce applicable solutions for real-world implementation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact:Computational results show that the proposed models produce applicable solutions for real-world implementation.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Two-stage stochastic programming · robust optimization · supply chain network design

Abstract

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.

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Cite This Article

Yılmaz, B. G., Yılmaz, Ö. F., Kara, S. S. (2026). Two-Stage Stochastic Programming and Robust Optimization Models for Resilient Supply Chain Network Design under Uncertainty: A Real Case Study. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, Volume: 75 Issue: 1. https://doi.org/10.31801/cfsuasmas.1743440

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