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.

翻译：带时间窗的动态车辆路径问题（DVRPTW）是经典VRPTW问题在在线场景中的泛化，其中客户数据以批次形式到达，需要实时生成路径方案。本文针对动态变体，改进了适用于VRPTW的高效启发式算法——混合遗传搜索（HGS）。我们讨论了HGS算法中受影响的组件，包括大路线表示、代价计算、初始种群、交叉操作和局部搜索。所提方法针对DVRPTW对这些组件进行了调整，力求在解质量与对未来客户到达的约束之间取得平衡。为此，我们设计了无需任何预训练的方法，用于比较不同规模解、标准化代价以及考虑未来时段。尽管存在这一限制，基于EURO meets NeurIPS车辆路径竞赛2022数据的计算结果表明，相较于最优基线算法，本方法的解质量显著提升。