With advanced information technologies and industrial intelligence, Industry 4.0 has been witnessing a large scale digital transformation. Intelligent transportation plays an important role in the new era and the classic vehicle routing problem (VRP), which is a typical problem in providing intelligent transportation, has been drawing more attention in recent years. In this article, we study multidepot VRP (MDVRP) that considers the management of the vehicles and the optimization of the routes among multiple depots, making the VRP variant more meaningful. In addressing the time efficiency and depot cooperation challenges, we apply the artificial bee colony (ABC) algorithm to the MDVRP. To begin with, we degrade MDVRP to single-depot VRP by introducing depot clustering. Then we modify the ABC algorithm for single-depot VRP to generate solutions for each depot. Finally, we propose a coevolution strategy in depot combination to generate a complete solution of the MDVRP. We conduct extensive experiments with different parameters and compare our algorithm with a greedy algorithm and a genetic algorithm (GA). The results show that the ABC algorithm has a good performance and achieve up to 70% advantage over the greedy algorithm and 3% advantage over the GA.

中文翻译：

---

将人工蜂群算法应用于多站点车辆路径问题

借助先进的信息技术和工业智能，工业4.0正在见证大规模的数字化转型。智能交通在新时代发挥着重要作用，作为智能交通领域典型问题的经典车辆路径问题（VRP）近年来越来越受到关注。在本文中，我们研究了多站点 VRP（MDVRP），它考虑了车辆的管理和多个站点之间的路线优化，使 VRP 变体更有意义。在解决时间效率和站点合作挑战时，我们将人工蜂群 (ABC) 算法应用于 MDVRP。首先，我们通过引入仓库集群将 MDVRP 降级为单仓库 VRP。然后我们修改单站 VRP 的 ABC 算法，为每个站生成解决方案。最后，我们提出了仓库组合中的协同进化策略，以生成 MDVRP 的完整解决方案。我们使用不同的参数进行了广泛的实验，并将我们的算法与贪心算法和遗传算法 (GA) 进行了比较。结果表明，ABC算法具有良好的性能，比贪心算法有70%的优势，比GA有3%的优势。