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<p>As a significant infrastructure of contemporary consumption and commodity production, supply chains formulate entire networks, from industrial production to sales. In recent years, several key challenges have emerged for supply chains, along with the rapid improvement in production capabilities and increased consumption, such as efficiency, robustness, and flexibility, among other concerns. To address these challenges, supply chains have been trending toward developing strongly interconnected networking structures and highly automated intelligent management. In the meantime, supply chain management (SCM) methods are increasingly being reshaped by artificial intelligence (AI)-driven decision-making techniques. This review provides a brief yet systematic survey of current modeling and optimization approaches for SCM. Specifically, we first introduce the fundamental decision-making problems in the four key areas of SCM, covering inventory management, logistics, production planning, and demand forecasting. We then review the classic and contemporary AI-driven methods employed to address these problems. Finally, we highlight some challenges and future research directions in the context of SCM.</p>

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A survey of supply chain management modeling and optimization: Key problems and recent solutions

  • 期刊论文

  • 发表日期：

    2025年8月22日

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  • Yu Sun,
  • Zhenqian Wang,
  • Haibo Gu,
  • Shaolin Tan,
  • Jinhu Lü

摘要

As a significant infrastructure of contemporary consumption and commodity production, supply chains formulate entire networks, from industrial production to sales. In recent years, several key challenges have emerged for supply chains, along with the rapid improvement in production capabilities and increased consumption, such as efficiency, robustness, and flexibility, among other concerns. To address these challenges, supply chains have been trending toward developing strongly interconnected networking structures and highly automated intelligent management. In the meantime, supply chain management (SCM) methods are increasingly being reshaped by artificial intelligence (AI)-driven decision-making techniques. This review provides a brief yet systematic survey of current modeling and optimization approaches for SCM. Specifically, we first introduce the fundamental decision-making problems in the four key areas of SCM, covering inventory management, logistics, production planning, and demand forecasting. We then review the classic and contemporary AI-driven methods employed to address these problems. Finally, we highlight some challenges and future research directions in the context of SCM.