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Developing a Correlated Robust Optimization Model to Reduce the Price of Robustness in the Supply Chain Coordination Problem
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Amir Jalilvand-Nejad *   |
| University of Garmsar , a.jalilvandnejad@fmgarmsar.ac.ir |
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Abstract: (129 Views) |
Coordination is a critical factor in optimizing supply chain performance. Given the pervasive uncertainties in supply chain management, it is essential to develop decisions that are robust against these uncertainties while preserving operational efficiency. This paper aims to determine an optimal supply chain policy that ensures the total system cost remains robust against correlated uncertainties in demand and lead time. To address the correlation among demand data and avoid overly conservative solutions, a novel robust optimization model is proposed based on a correlated polyhedral uncertainty set. This approach explicitly accounts for demand correlation, thereby reducing the price of robustness. Numerical results demonstrate that integrating coordination as a strategic decision and employing robust optimization as a tactical tool significantly enhances supply chain performance. Moreover, incorporating demand correlation in the proposed model leads to a substantial reduction in the price of robustness and, consequently, higher supply chain profitability. Extending this framework to more complex supply chain models with multiple sources of uncertainty holds great potential for further improving the robustness and practical applicability of supply chain decision-making.
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| Keywords: robust optimization, correlated uncertainty, price of robustness, supply chain coordination. |
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Full-Text [PDF 584 kb]
(44 Downloads)
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Type of Study: Original |
Subject:
Continuous Optimization
Received: 2026/03/18 | Accepted: 2026/05/29 | Published: 2026/05/29
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