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

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

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 197, issue C

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.

Keywords: Graph convolutional networks (GCNs); Optimization; Scheduling and job scheduling; Scalability Interpretability; Transfer learning; Ethical implications; Logistics; Vehicle routing problem (VRP); Traveling salesman problem (TSP); Resource allocation; Reinforcement learning (RL) and Deep reinforcement learning (DRL); Dynamic graph processing; Real-world applications; Combinatorial optimization; Traffic flow optimization; Meta-heuristics and Hybrid optimization techniques; Spectral graph theory; Environmental sustainability; Multi-objective optimization; Spatial–temporal dependencies (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1366554525001243
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:transe:v:197:y:2025:i:c:s1366554525001243

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/bibliographic
http://www.elsevier. ... 600244/bibliographic

DOI: 10.1016/j.tre.2025.104083

Access Statistics for this article

Transportation Research Part E: Logistics and Transportation Review is currently edited by W. Talley

More articles in Transportation Research Part E: Logistics and Transportation Review from Elsevier
Bibliographic data for series maintained by Catherine Liu ().