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A hybrid optimisation strategy for large-scale vehicle routing problems with time windows using solution initialisation

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  • Yongzhong Wu
  • Minqi Xu
  • Mianmian Huang

Abstract

This paper investigates a novel hybrid optimisation strategy that integrates a machine learning algorithm with a meta-heuristics to tackle large-scale vehicle routing problems with time windows (VRPTW). Specifically, the K-means clustering algorithm is employed to generate initial routing solutions, subsequently optimised by an artificial bee colony (ABC) algorithm. The new approach is tested on large-scale real-life cases. The computational results show that the new algorithm outperforms a well-established ABC algorithm in terms of both objective value and computation time. In addition, the experiments highlight the importance of considering both the distance between customers and customer time windows in the clustering process to ensure good computational results.

Suggested Citation

  • Yongzhong Wu & Minqi Xu & Mianmian Huang, 2025. "A hybrid optimisation strategy for large-scale vehicle routing problems with time windows using solution initialisation," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 50(4), pages 492-511.
    • Handle: RePEc:ids:ijisen:v:50:y:2025:i:4:p:492-511
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