Dynamic vehicle routing problem (DVRP) has attracted increasing attention due to its wide applications in logistics. Compared with the static vehicle routing problem, DVRP is characterized by the prior unknown customer requests dynamically appearing in route execution. Nevertheless, the newly appeared customers pose a great challenge to route optimizer, since the optimized route may be contrarily of bad quality when including the new customers that are far from planned routes in route planning. To address this issue, in this paper we propose a demand coverage diversity based metaheuristic, termed ACO-CD, in the framework of ant colony algorithm. In ACO-CD, a demand coverage diversity adaptation method is suggested to maintain the diversity of covered customers in routes so that the optimizer can effectively response to the newly appeared customer requests. Experimental results on 27 DVRP test instances demonstrate the effectiveness of the proposed demand coverage diversity adaptation method and the superiority of the proposed ACO-CD over four state-of-the-art DVRP algorithms in terms of solution quality.

动态车辆路径问题（DVRP）由于其在物流中的广泛应用而引起了越来越多的关注。与静态车辆路径问题相比，DVRP的特征在于在执行路线时动态出现先前的未知客户请求。但是，新出现的客户对路线优化器提出了巨大的挑战，因为当在路线规划中包含距离规划路线较远的新客户时，优化的路线可能会带来不良的质量。为了解决这个问题，本文在蚁群算法的框架内提出了一种基于需求覆盖多样性的元启发式算法，称为ACO-CD。在ACO-CD中 建议使用需求覆盖多样性调整方法来维护路线中覆盖客户的多样性，以便优化器可以有效响应新出现的客户需求。在27个DVRP测试实例上的实验结果证明了所提出的需求覆盖多样性自适应方法的有效性以及所提出的ACO-CD在解决方案质量方面优于四种最新DVRP算法的优越性。