  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 that do not
require any prior training. Despite this limitation, computational results on
data from the EURO meets NeurIPS Vehicle Routing Competition 2022 demonstrate
significantly improved solution quality over the best-performing baseline
algorithm.
