Using machine learning to identify hidden constraints in vehicle routing problems

IF 4.1 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Anna Konovalenko , Lars Magnus Hvattum , Mohamed Kais Msakni
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引用次数: 0

Abstract

Last-mile delivery involves a series of complex tasks in an unpredictable environment. Decision support tools based on optimization algorithms construct efficient routes for drivers, optimizing the cost of making deliveries. However, drivers often deviate from these routes due to factors not considered in the decision-making process. This discrepancy raises the question of how to identify routes that are useable in real-world scenarios. Our research proposes using modern machine learning techniques to classify routes based on their practical usability. In a controlled environment, we demonstrate that machine learning can learn hidden factors influencing route viability by focusing on variants of the vehicle routing problem with additional constraints like time window, capacity and precedence. For each underlying constraint, we show that a machine learning model can be trained to classify routes based on whether or not they violate the constraint. Using datasets generated from well-known benchmark instances, we present computational experiments to evaluate model performance. We discuss which types of constraints are more challenging to recognize and how large a dataset must be to allow for accurate classification. This research has the potential to improve existing decision tools, enabling them to generate routes that better account for real-world complexities.
最后一英里配送涉及在不可预测的环境中执行一系列复杂任务。基于优化算法的决策支持工具为驾驶员构建了高效路线,优化了送货成本。然而,由于决策过程中未考虑的因素,司机经常会偏离这些路线。这种差异提出了一个问题:如何确定现实世界中可用的路线。我们的研究建议使用现代机器学习技术,根据实际可用性对路线进行分类。在一个受控环境中,我们证明了机器学习可以学习影响路线可行性的隐藏因素,具体方法是将重点放在带有额外约束条件(如时间窗口、容量和优先级)的车辆路由问题变体上。对于每个基本约束条件,我们都展示了机器学习模型可以根据是否违反约束条件进行分类。我们利用从知名基准实例中生成的数据集,通过计算实验来评估模型的性能。我们讨论了哪种类型的约束更难识别,以及必须有多大的数据集才能实现准确分类。这项研究有望改进现有的决策工具,使其能够生成更能反映现实世界复杂性的路线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Operations Research
Computers & Operations Research 工程技术-工程:工业
CiteScore
8.60
自引率
8.70%
发文量
292
审稿时长
8.5 months
期刊介绍: Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.
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