The Vehicle Routing Problem (VRP) is a typical combinatorial optimization problem and has been studied for many years. However, there are few researches on the Dynamic Vehicle Routing Problem with Time Window (DVRPTW), which is an extension of VRP and more challenging with changing environmental factors, such as stochastic customer requests. Once changes happen, the routes should be adjusted for the new environments. In this paper, we construct a multi-objective optimization model for the DVRPTW and propose a new algorithm named as EL-DMOEA, where an ensemble learning method is investigated to improve the performance of the algorithm. In EL-DMOEA, to enhance the population’s diversity and accelerate the convergence, three different strategies, i.e., population-based prediction strategy, immigrant strategy and random strategy, are employed in the training process of three kinds of basic models respectively. The experimental results on the test benchmarks reveal that the proposed algorithm is effective to make promising routing plans.

车辆路径问题（VRP）是典型的组合优化问题，已经研究了很多年。但是，关于带时间窗的动态车辆路线问题（DVRPTW）的研究很少，这是VRP的扩展，并且随着环境因素（例如随机客户需求）的变化而更具挑战性。一旦发生更改，应针对新环境调整路线。在本文中，我们为DVRPTW构建了一个多目标优化模型，并提出了一种名为EL-DMOEA的新算法，其中研究了集成学习方法以提高该算法的性能。在EL-DMOEA中，为了提高人口的多样性并加快融合，三种策略分别是基于人口的预测策略，移民策略和随机策略，分别用于三种基本模型的训练过程。在测试基准上的实验结果表明，所提出的算法可以有效地制定有前景的路由计划。