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：动态车辆路径问题（DVRPTW）是经典VRPTW问题在在线场景下的推广，其特点是客户数据分批次到达，且需要实时生成路由方案。本文对成功求解VRPTW的启发式算法——混合遗传搜索（HGS）算法进行适配，以应对动态变体。我们详细讨论了HGS算法中受影响的模块，包括巨型路径表示、成本计算、初始种群、交叉算子及局部搜索。通过对这些组件进行面向DVRPTW的针对性改造，我们在解的质量与未来客户到达约束之间寻求平衡。为此，我们设计了无需任何预训练的方法，用于比较不同规模的解、标准化成本以及预判未来时段的影响。尽管存在这一限制，基于EURO与NeurIPS联合举办的2022年车辆路径竞赛数据的计算结果表明，该算法相较最优基准算法显著提升了求解质量。