提高求解大规模车辆路径问题的算法效率,已经成为物流配送的现实需要。针对一种典型的车辆路径问题禁忌搜索算法,提出用毗邻信息指导的动态候选集规模改进禁忌搜索算法,以提高禁忌搜索算法求解大规模车辆路径问题的自适应能力和鲁棒性。算例测试证明,改进的算法在求解问题规模较大时,可以带来超过50%的搜索时间节省。 Improving the efficiency of algorithms for solving large-scale vehicle routing problems has become a real need of logistics and distribution. Aiming at a typical tabu search algorithm of vehicle routing problem, this paper proposes a tabu search algorithm based on the dynamic candidate set size guided by adjacent information to improve the tabu search algorithm to solve the adaptive capability and robustness of large-scale vehicle routing problem. The example test proves that the improved algorithm can bring more than 50% search time saving when the problem size is large.