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Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows

In: Operations Research Proceedings 2023

Author

Listed:
  • Mohammed Ghannam

    (HTW Berlin
    Zuse Institute Berlin)

  • Ambros Gleixner

    (HTW Berlin
    Zuse Institute Berlin)

Abstract

The dynamic vehicle routing problem with time windows (DVRPTW) is a generalization of the classical VRPTW to an online setting, where customer data arrives in batches and real-time routing solutions are required. In this paper, we adapt the Hybrid Genetic Search (HGS) algorithm, a successful heuristic for VRPTW, to the dynamic variant. We discuss the affected components of the HGS algorithm including giant-tour representation, cost computation, initial population, crossover, and local search. Our approach modifies these components for DVRPTW, attempting to balance solution quality and constraints on future customer arrivals. To this end, we devise methods for comparing different-sized solutions, normalizing costs, and accounting for future epochs. Though our methods do not require any prior training, computational results on data from the EURO meets NeurIPS Vehicle Routing Competition 2022 demonstrate significantly improved solution quality over the best-performing baseline algorithm.

Suggested Citation

  • Mohammed Ghannam & Ambros Gleixner, 2025. "Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows," Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 273-280, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-58405-3_35
    DOI: 10.1007/978-3-031-58405-3_35
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