Mathematical Modeling for Digital and Intelligent Supply Chains

Dear Colleagues,

Modern supply chain management (SCM) is undergoing a profound transformation, driven by increasing demands for resilience, sustainability, adaptability, and digital intelligence. Against this backdrop, this Special Issue, entitled “Mathematical Modeling for Digital and Intelligent Supply Chains,” aims to provide a high-level forum for the dissemination of original research on advanced mathematical frameworks, analytical models, and computational methodologies for contemporary supply chain systems.

This Special Issue focuses on the role of mathematical modeling as a rigorous and unifying language for understanding, designing, and improving supply chain systems in the era of digitalization and industrial transformation. We particularly welcome studies that incorporate emerging technological elements—such as data-driven analytics, artificial intelligence (including generative AI and traditional machine learning), blockchain, digital twins, and the Physical Internet—into supply chain planning, coordination, optimization, and control. Contributions may address a broad range of supply chain system problems, including but not limited to network design, sourcing, production and inventory planning, logistics coordination, risk management, sustainability governance, and system reconfiguration under uncertainty.

In addition to methodological novelty, this Special Issue seeks to highlight the intersection between classical operations research and the new frontier of intelligent industrial technologies. We are especially interested in research that combines first-principles mathematical modeling with modern data-centric and AI-enabled approaches to support decision-making in increasingly complex and interconnected supply chain environments. Such research may move beyond traditional efficiency-oriented objectives to explicitly incorporate resilience, environmental and social considerations, and human-centered operational design as endogenous elements of supply chain systems.

We invite original contributions that bridge rigorous mathematical theory and practical challenges in real-world SCM. Potential topics include, but are not limited to:

• Mathematical Modeling of Supply Chain Systems: analytical, stochastic, dynamic, and optimization-based models; game theory and mechanism design for strategic interactions; simulation and data-driven approaches for complex supply chain decision-making.
• Data-Driven Supply Chain Optimization: integration of machine learning, predictive analytics, and data-enabled decision models in supply chain operations.
• Artificial Intelligence in SCM: generative AI (e.g., large language models and diffusion models) and deep learning-enabled methods for forecasting, planning, coordination, negotiation, and decision support.
• Resilient and Adaptive Supply Chains: models for disruption management, risk mitigation, recovery planning, and reconfiguration under uncertainty.
• Sustainable and ESG-Aware Supply Chains: endogenous modeling of environmental, social, and governance criteria in supply chain objectives and constraints.
• Blockchain and Smart Contract Applications: mathematical and computational models for transparency, traceability, trust, and decentralized coordination.
• Physical Internet and Hyperconnected Logistics: modeling open logistics systems, resource sharing, modular flows, and interoperable transportation networks.
• Digital Twins and Intelligent Supply Chain Systems: model-based integration of real-time data, simulation, and decision intelligence.
• Industry 5.0 and Human-Centric Operations: human–machine collaboration, socio-technical system design, and resilient operational ecosystems.
• Advanced Algorithms and Computational Methods: exact, heuristic, metaheuristic, and decomposition approaches for solving large-scale and NP-hard supply chain problems.

Dr. Yelin Fu
Dr. Wei Wu
Guest Editors

Dr. Yiji Cai
Guest Editor Assistant

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• supply chain management
• mathematical modeling
• supply chain configuration
• ESG
• machine learning
• generative artificial intelligence
• blockchain
• physical internet
• Industry 5.0
• operations research
• optimization algorithms
• supply chain and logistics finance
• game theory