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Graph Convolutional Networks for logistics optimization: A survey of scheduling and operational applications

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  • Neamatian Monemi, Rahimeh
  • Gelareh, Shahin
  • González, Pedro Henrique
  • Cui, Lubin
  • Bouamrane, Karim
  • Dai, Yu-Hong
  • Maculan, Nelson

Abstract

Graph Convolutional Networks (GCNs) have emerged as pivotal tools in addressing intricate optimization and scheduling challenges within logistics, encompassing canonical problems such as the Vehicle Routing Problem (VRP), Traveling Salesman Problem (TSP), and dynamic job scheduling. This survey presents a comprehensive exploration of GCN applications, emphasizing their capacity to model spatial–temporal dependencies and their seamless integration with advanced paradigms, including reinforcement learning and hybrid optimization techniques. By leveraging these capabilities, GCNs have demonstrated enhanced scalability and interpretability, rendering them indispensable for large-scale, real-time logistics systems. The review extends to real-world implementations, illustrating GCN-driven innovations in resource allocation, traffic management, and supply chain optimization. In addition, the study critically examines persistent challenges—ranging from processing dynamic graphs to ensuring ethical deployment through fairness and sustainability. The paper concludes with forward-looking recommendations, advocating for the evolution of GCN architectures to adeptly manage real-time decision-making and uncertainty in increasingly complex logistical landscapes.

Suggested Citation

  • Neamatian Monemi, Rahimeh & Gelareh, Shahin & González, Pedro Henrique & Cui, Lubin & Bouamrane, Karim & Dai, Yu-Hong & Maculan, Nelson, 2025. "Graph Convolutional Networks for logistics optimization: A survey of scheduling and operational applications," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:transe:v:197:y:2025:i:c:s1366554525001243
    DOI: 10.1016/j.tre.2025.104083
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    References listed on IDEAS

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    1. Weiwei Jiang & Haoyu Han & Yang Zhang & Ji’an Wang & Miao He & Weixi Gu & Jianbin Mu & Xirong Cheng, 2024. "Graph Neural Networks for Routing Optimization: Challenges and Opportunities," Sustainability, MDPI, vol. 16(21), pages 1-34, October.
    2. Xuan Jing & Xifan Yao & Min Liu & Jiajun Zhou, 2024. "Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 75-93, January.
    3. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
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