Dynamic vehicle routing with time windows in theory and practice.

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Natural Computing Pub Date : 2017-01-01 Epub Date: 2016-04-09 DOI:10.1007/s11047-016-9550-9
Zhiwei Yang, Jan-Paul van Osta, Barry van Veen, Rick van Krevelen, Richard van Klaveren, Andries Stam, Joost Kok, Thomas Bäck, Michael Emmerich
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引用次数: 1

Abstract

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.

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具有时间窗的动态车辆路线的理论与实践。
车辆路径问题是一个经典的组合优化问题。本文研究的是具有动态变化顺序和时间窗的车辆路径问题的一个变体。在实际应用中,需求经常在操作期间发生变化。新订单出现,其他订单被取消。在这种情况下,需要动态地生成新的时间表。动态车辆路径的在线优化算法解决了这一问题,但迄今为止它们没有考虑时间窗口。此外,为了匹配实际问题中的场景,需要对基准进行调整。本文以某快递公司的日常路线规划过程为基础,对一个实际问题进行了建模。客户的新订单是在工作日内动态引入的,需要集成到计划中。针对带时间窗的车辆动态路径问题,提出了一种结合强大局部搜索过程的多蚁群算法。性能是在一个基于工作日模拟的新基准上测试的。这些问题取自Solomon的基准测试,但有一定比例的订单只在运行期间显示给算法。对不同版本的MACS算法进行了测试,并确定了一个高性能的变体。最后,该算法在现场进行了测试:在现场研究中,该算法为一家监控公司安排了车队。我们将算法的性能与公司使用的程序的性能进行了比较,并总结了从现实世界研究的实施中获得的见解。结果表明,多蚁群算法能较好地解决学术基准问题,并能与实际环境相结合。
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来源期刊
Natural Computing
Natural Computing Computer Science-Computer Science Applications
CiteScore
4.40
自引率
4.80%
发文量
49
审稿时长
3 months
期刊介绍: The journal is soliciting papers on all aspects of natural computing. Because of the interdisciplinary character of the journal a special effort will be made to solicit survey, review, and tutorial papers which would make research trends in a given subarea more accessible to the broad audience of the journal.
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