优化自动驾驶转运枢纽网络:量化自动驾驶卡车的潜在影响

IF 2.1 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Chungjae Lee , Kevin Dalmeijer , Pascal Van Hentenryck , Peibo Zhang
{"title":"优化自动驾驶转运枢纽网络:量化自动驾驶卡车的潜在影响","authors":"Chungjae Lee ,&nbsp;Kevin Dalmeijer ,&nbsp;Pascal Van Hentenryck ,&nbsp;Peibo Zhang","doi":"10.1016/j.ejtl.2024.100141","DOIUrl":null,"url":null,"abstract":"<div><p>Autonomous trucks are expected to fundamentally transform the freight transportation industry. In particular, Autonomous Transfer Hub Networks (ATHNs), which combine autonomous trucks on middle miles with human-driven trucks on the first and last miles, are seen as the most likely deployment pathway for this technology. This paper presents a framework to optimize ATHN operations and evaluate the benefits of autonomous trucking. By exploiting the problem structure, this paper introduces a flow-based optimization model for this purpose that can be solved by blackbox solvers in a matter of hours. The resulting framework is easy to apply and enables the data-driven analysis of large-scale systems. The power of this approach is demonstrated on a system that spans all of the United States over a four-week horizon. The case study quantifies the potential impact of autonomous trucking and shows that ATHNs can have significant benefits over traditional transportation networks.</p></div>","PeriodicalId":45871,"journal":{"name":"EURO Journal on Transportation and Logistics","volume":"13 ","pages":"Article 100141"},"PeriodicalIF":2.1000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192437624000165/pdfft?md5=50e7f65a716bd9bab7d80ed2f378b9a5&pid=1-s2.0-S2192437624000165-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Optimizing Autonomous Transfer Hub Networks: Quantifying the potential impact of self-driving trucks\",\"authors\":\"Chungjae Lee ,&nbsp;Kevin Dalmeijer ,&nbsp;Pascal Van Hentenryck ,&nbsp;Peibo Zhang\",\"doi\":\"10.1016/j.ejtl.2024.100141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Autonomous trucks are expected to fundamentally transform the freight transportation industry. In particular, Autonomous Transfer Hub Networks (ATHNs), which combine autonomous trucks on middle miles with human-driven trucks on the first and last miles, are seen as the most likely deployment pathway for this technology. This paper presents a framework to optimize ATHN operations and evaluate the benefits of autonomous trucking. By exploiting the problem structure, this paper introduces a flow-based optimization model for this purpose that can be solved by blackbox solvers in a matter of hours. The resulting framework is easy to apply and enables the data-driven analysis of large-scale systems. The power of this approach is demonstrated on a system that spans all of the United States over a four-week horizon. The case study quantifies the potential impact of autonomous trucking and shows that ATHNs can have significant benefits over traditional transportation networks.</p></div>\",\"PeriodicalId\":45871,\"journal\":{\"name\":\"EURO Journal on Transportation and Logistics\",\"volume\":\"13 \",\"pages\":\"Article 100141\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2192437624000165/pdfft?md5=50e7f65a716bd9bab7d80ed2f378b9a5&pid=1-s2.0-S2192437624000165-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EURO Journal on Transportation and Logistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2192437624000165\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURO Journal on Transportation and Logistics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2192437624000165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0

摘要

自动驾驶卡车有望从根本上改变货运业。尤其是自动驾驶转运枢纽网络(ATHN),它将中间里程的自动驾驶卡车与首末里程的人工驾驶卡车结合在一起,被视为该技术最有可能的部署途径。本文提出了一个优化 ATHN 运营和评估自主卡车运输效益的框架。通过利用问题结构,本文为此引入了一个基于流程的优化模型,黑盒求解器可在数小时内求解该模型。由此产生的框架易于应用,并能对大规模系统进行数据驱动分析。本文在一个为期四周、横跨全美的系统上展示了这种方法的威力。该案例研究量化了自动驾驶卡车运输的潜在影响,并表明自动驾驶运输网络与传统运输网络相比具有显著优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Autonomous Transfer Hub Networks: Quantifying the potential impact of self-driving trucks

Autonomous trucks are expected to fundamentally transform the freight transportation industry. In particular, Autonomous Transfer Hub Networks (ATHNs), which combine autonomous trucks on middle miles with human-driven trucks on the first and last miles, are seen as the most likely deployment pathway for this technology. This paper presents a framework to optimize ATHN operations and evaluate the benefits of autonomous trucking. By exploiting the problem structure, this paper introduces a flow-based optimization model for this purpose that can be solved by blackbox solvers in a matter of hours. The resulting framework is easy to apply and enables the data-driven analysis of large-scale systems. The power of this approach is demonstrated on a system that spans all of the United States over a four-week horizon. The case study quantifies the potential impact of autonomous trucking and shows that ATHNs can have significant benefits over traditional transportation networks.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
4.60
自引率
0.00%
发文量
24
审稿时长
129 days
期刊介绍: The EURO Journal on Transportation and Logistics promotes the use of mathematics in general, and operations research in particular, in the context of transportation and logistics. It is a forum for the presentation of original mathematical models, methodologies and computational results, focussing on advanced applications in transportation and logistics. The journal publishes two types of document: (i) research articles and (ii) tutorials. A research article presents original methodological contributions to the field (e.g. new mathematical models, new algorithms, new simulation techniques). A tutorial provides an introduction to an advanced topic, designed to ease the use of the relevant methodology by researchers and practitioners.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信