The vehicle routing problem is a classical combinatorial optimization problem. This work is about a variant of the vehicle routing problem with dynamically changing orders and time windows. In real-world applications often the demands change during operation time. New orders occur and others are canceled. In this case new schedules need to be generated on-the-fly. Online optimization algorithms for dynamical vehicle routing address this problem but so far they do not consider time windows. Moreover, to match the scenarios found in real-world problems adaptations of benchmarks are required. In this paper, a practical problem is modeled based on the procedure of daily routing of a delivery company. New orders by customers are introduced dynamically during the working day and need to be integrated into the schedule. A multiple ant colony algorithm combined with powerful local search procedures is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on a new benchmark based on simulations of a working day. The problems are taken from Solomon’s benchmarks but a certain percentage of the orders are only revealed to the algorithm during operation time. Different versions of the MACS algorithm are tested and a high performing variant is identified. Finally, the algorithm is tested in situ: In a field study, the algorithm schedules a fleet of cars for a surveillance company. We compare the performance of the algorithm to that of the procedure used by the company and we summarize insights gained from the implementation of the real-world study. The results show that the multiple ant colony algorithm can get a much better solution on the academic benchmark problem and also can be integrated in a real-world environment.

中文翻译：

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具有时间窗的动态车辆路线选择理论和实践。

车辆路径问题是经典的组合优化问题。这项工作是关于带有动态更改的订单和时间窗口的车辆路径问题的一种变体。在实际应用中，需求通常会在运行期间发生变化。发生新订单，其他订单被取消。在这种情况下，需要即时生成新的时间表。用于动态车辆路线选择的在线优化算法解决了该问题，但到目前为止，它们并未考虑时间窗。此外，为了匹配在实际问题中发现的场景，需要对基准进行调整。在本文中，一个实际问题是基于送货公司的日常路由流程建模的。在工作日动态引入客户的新订单，并且需要将其集成到时间表中。提出了一种结合强大的局部搜索程序的多蚁群算法，以解决带时间窗的动态车辆路径问题。在基于工作日模拟的新基准上对性能进行了测试。这些问题取材于所罗门的基准测试，但是一定比例的订单仅在操作期间才显示给算法。测试了MACS算法的不同版本，并确定了高性能的变体。最后，对该算法进行了现场测试：在现场研究中，该算法为监视公司安排了一批车队。我们将算法的性能与公司使用的程序的性能进行了比较，并总结了从实际研究的实施中获得的见解。