Uncertainty-Aware Decision Making in Supply Chain and Logistics: Integration of Fuzzy, Stochastic, and Analytical Approaches

Dear Colleagues,

The ever-increasing complexity and volatility of modern supply chains have underscored the importance of decision-making during periods of uncertainty. Disruptions in supply and demand, market fluctuations, geopolitical tensions, and sustainability pressures are reshaping how supply chains are planned and managed. As a result, supply chain professionals and researchers alike are utilizing more advanced and uncertainty-aware decision support tools to enhance resilience, agility, and performance. This Special Issue aims to compile state-of-the-art research on decision-making in supply chain and logistics systems where uncertainty plays a crucial role. We particularly welcome submissions that employ fuzzy set theory, stochastic models, optimization, inferential statistics, and data analytics to handle ambiguity, randomness, and imprecision in parameters, constraints, or objectives. The methodologies explored in this Special Issue may span deterministic, probabilistic, fuzzy, or hybrid approaches that can be applied to both strategic and operational decisions. While the use of real-world data is encouraged, it is not a requirement—conceptual models, simulation-based studies, and theoretical frameworks are equally welcome, provided that they contribute to understanding or solving real-world supply chain challenges. This Special Issue welcomes the submission of high-quality original contributions that explore the following topics:

• Fuzzy and stochastic optimization models in supply chain management;
• Data-driven and data-augmented decision making in logistics;
• Supply chain risk and resilience analysis under uncertainty;
• Hybrid models combining fuzzy logic, stochastic processes, and simulation;
• Robust and stochastic multi-objective optimization;
• Inferential statistics for supply chain performance assessment;
• Uncertainty modeling in supplier selection, demand forecasting, and transportation planning;
• Machine learning and AI techniques integrated with fuzzy and probabilistic reasoning in supply chains;
• Decision support systems for sustainable and resilient supply chains.

Dr. Omid Jadidi
Guest Editor

Manuscript Submission Information

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• supply chain uncertainty
• fuzzy and stochastic optimization
• decision support systems
• resilient and sustainable supply chains
• data-driven decision making
• hybrid modeling approaches

• Multi-criteria Decision Making in Supply Chain Management in Systems (16 articles)