← Back to Volume 1, Issue 6

Exploration on Practical Application of Cold-Chain Logistics Distribution in Real Road Networks from the Perspective of Dynamic Vehicle Routing Problems

Yuping Wang
Vol.1, No.6, pp.76-80, 2026 DOI: 10.62051/ajmse.v1n6.09 September 22, 2026
Download PDF
Abstract: This paper investigates the application of cold-chain logistics distribution in real road networks through the framework of dynamic vehicle routing problems (DVRP). It does not offer new empirical data, but gathers previous research on vehicle routing, dynamic routing models and cold chain distribution to explore how dynamic VRP formulations can support decision-making under real-time information, traffic fluctuations and temperature-control constraints. Based on taxonomies of DVRP variants, classification studies of vehicle routing problems and specific models for perishable products and multi-compartment refrigerated vehicles have been conducted, and this paper explores how logistics practitioners can adapt the dynamic VRP framework to real road conditions, such as time-dependent travel times, congestion, and heterogeneous customer requirements. Cold-chain distribution in practice is expected to integrate routing optimisation with risk management for product quality, customer satisfaction and environmental performance, and dynamic VRP approaches offer a practical, albeit not exhaustive, path to explore these trade-offs. Therefore, the application should be based on a well-defined problem, integrated with monitoring technology, and have realistic expectations for the limits of optimisation models.
Keywords: Dynamic Vehicle Routing, Cold-chain Logistics, Real Road Networks, Perishable Products, Time-dependent Travel Times, Customer Satisfaction
APA Citation: Yuping Wang (2026). Exploration on Practical Application of Cold-Chain Logistics Distribution in Real Road Networks from the Perspective of Dynamic Vehicle Routing Problems. Academic Journal of Management Science and Engineering, 1(6), 76-80. https://doi.org/10.62051/ajmse.v1n6.09

References

  1. Toth, P., & Vigo, D. (2014). Vehicle routing: Problems, methods, and applications (2nd ed.). SIAM. https:// doi. org/ 10.1137/1.9781611973594.
  2. Braekers, K., Ramaekers, K., & Van Nieuwenhuyse, I. (2016). The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 99, 300–313. https://doi.org/ 10. 1016/ j. cie.2016.08.003.
  3. Psaraftis, H. N., Wen, M., & Kontovas, C. A. (2016). Dynamic vehicle routing problems: Three decades and counting. Networks, 67(1), 3–31. https://doi.org/10.1002/net.21597.
  4. Ritzinger, U., Puchinger, J., & Hartl, R. F. (2016). A survey on dynamic and stochastic vehicle routing problems. International Journal of Production Research, 54(1), 215–231. https://doi.org/10.1080/00207543.2015.1043436.
  5. Osvald, A., & Stirn, L. Z. (2008). A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food. Journal of Food Engineering, 85(2), 285–295. https://doi.org/10.1016/j.jfoodeng.2007.07.008.
  6. Chen, L., Liu, Y., & Langevin, A. (2019). A multi-compartment vehicle routing problem in cold-chain distribution. Computers & Operations Research, 111, 58–66. https://doi.org/10.1016/j.cor.2019.07.007.
  7. Qin, G., Tao, F., & Li, L. (2019). A vehicle routing optimization problem for cold chain logistics considering customer satisfaction and carbon emissions. International Journal of Environmental Research and Public Health, 16(4), 576. https://doi.org/10.3390/ijerph16040576.
  8. Qi, B., & Li, G. (2024). The evolution of the cold chain logistics vehicle routing problem: A bibliometric and visualization review. Digital Transportation and Safety, 3(3), 92–114.
  9. Shi, H., Hong, Y., Zhang, Q., & Qin, J. (2025). Research on cold chain logistics joint distribution vehicle routing optimization based on uncertainty entropy and time-varying network. Entropy, 27(5), 540. https://doi.org/ 10. 3390/ e27050540.
  10. Li, Y., Lim, M. K., & Tseng, M. L. (2019). A green vehicle routing model based on modified particle swarm optimization for cold chain logistics. Industrial Management & Data Systems, 119(3), 473–494. https:// doi. org/ 10.1108/IMDS-06-2018-0241.
  11. Liao, T. Y., & Hu, T. Y. (2011). An object-oriented evaluation framework for dynamic vehicle routing problems under real-time information. Expert Systems with Applications, 38(10), 12548–12558. https://doi. org/10. 1016/ j. eswa. 2011.04.042.