路由问题的逆向优化

IF 4.4 2区 工程技术 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy
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引用次数: 0

摘要

我们提出了一种利用逆向优化(IO)学习决策者在路由问题中的行为的方法。IO 框架属于监督学习范畴,其前提是目标行为是未知成本函数的优化器。这个成本函数需要通过历史数据来学习,在路由问题中,可以解释为决策者的路由偏好。因此,本研究的主要贡献在于针对路由问题提出了一种具有假设函数、损失函数和随机一阶算法的 IO 方法。我们在亚马逊 "最后一英里路由研究挑战 "中进一步测试了我们的 IO 方法,该挑战的目标是利用数千个真实路由示例,学习能够复制人类驾驶员路由偏好的模型。与有资格参加最后一轮挑战赛的 48 个模型相比,我们的最终 IO 学习路由模型得分排名第二。我们的示例和结果展示了所提出的 IO 方法在路由问题中学习决策者决策的灵活性和现实潜力:本文已被 2023 年 TSL 会议交通科学特刊录用:这项工作得到了欧洲研究理事会 [TRUST-949796] 的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inverse Optimization for Routing Problems
We propose a method for learning decision makers’ behavior in routing problems using inverse optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context of routing problems, can be interpreted as the routing preferences of the decision makers. In this view, the main contributions of this study are to propose an IO methodology with a hypothesis function, loss function, and stochastic first-order algorithm tailored to routing problems. We further test our IO approach in the Amazon Last Mile Routing Research Challenge, where the goal is to learn models that replicate the routing preferences of human drivers, using thousands of real-world routing examples. Our final IO-learned routing model achieves a score that ranks second compared with the 48 models that qualified for the final round of the challenge. Our examples and results showcase the flexibility and real-world potential of the proposed IO methodology to learn from decision-makers’ decisions in routing problems.History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023.Funding: This work was supported by the European Research Council [TRUST-949796].
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来源期刊
Transportation Science
Transportation Science 工程技术-运筹学与管理科学
CiteScore
8.30
自引率
10.90%
发文量
111
审稿时长
12 months
期刊介绍: Transportation Science, published quarterly by INFORMS, is the flagship journal of the Transportation Science and Logistics Society of INFORMS. As the foremost scientific journal in the cross-disciplinary operational research field of transportation analysis, Transportation Science publishes high-quality original contributions and surveys on phenomena associated with all modes of transportation, present and prospective, including mainly all levels of planning, design, economic, operational, and social aspects. Transportation Science focuses primarily on fundamental theories, coupled with observational and experimental studies of transportation and logistics phenomena and processes, mathematical models, advanced methodologies and novel applications in transportation and logistics systems analysis, planning and design. The journal covers a broad range of topics that include vehicular and human traffic flow theories, models and their application to traffic operations and management, strategic, tactical, and operational planning of transportation and logistics systems; performance analysis methods and system design and optimization; theories and analysis methods for network and spatial activity interaction, equilibrium and dynamics; economics of transportation system supply and evaluation; methodologies for analysis of transportation user behavior and the demand for transportation and logistics services. Transportation Science is international in scope, with editors from nations around the globe. The editorial board reflects the diverse interdisciplinary interests of the transportation science and logistics community, with members that hold primary affiliations in engineering (civil, industrial, and aeronautical), physics, economics, applied mathematics, and business.
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