A machine learning approach for solution space reduction in aircraft disruption recovery

IF 6 2区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Navid Rashedi , Nolan Sankey , Vikrant Vaze , Keji Wei
{"title":"A machine learning approach for solution space reduction in aircraft disruption recovery","authors":"Navid Rashedi ,&nbsp;Nolan Sankey ,&nbsp;Vikrant Vaze ,&nbsp;Keji Wei","doi":"10.1016/j.ejor.2024.11.025","DOIUrl":null,"url":null,"abstract":"<div><div>Aircraft recovery, a critical step in airline operations recovery, aims to minimize the cost of disrupted aircraft schedules. The exact methods for aircraft recovery are computationally expensive and operationally infeasible in practice. Heuristics and hybrid approaches offer faster solutions but have inconsistent solution quality, often leading to large losses. We propose a supervised machine learning approach to accelerate aircraft recovery by pruning the solution space of the optimization problem. It leverages similarities with previously solved problem instances through an offline model-training phase, identifies components of the optimal solutions for new problem instances in the online phase, and links them to the optimization model to rapidly generate high-quality solutions. Computational results, from multiple historical disruption instances for a large US airline, demonstrate that this approach significantly outperforms exact methods on computational runtime while producing similarly high-quality solutions. It also outperforms existing heuristics due to its ability to prune solution spaces in a more principled manner, leading to higher quality solutions in similarly short runtimes. For a runtime budget of two minutes, our approach provides a solution within 1.5% of the true optimal cost, resulting in an average daily saving of over $390,000 compared to all existing approaches. The main drivers of these improvements are explainable in terms of key airline operational metrics and are validated through extensive sensitivity and robustness tests.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"323 1","pages":"Pages 297-308"},"PeriodicalIF":6.0000,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Operational Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0377221724008944","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Aircraft recovery, a critical step in airline operations recovery, aims to minimize the cost of disrupted aircraft schedules. The exact methods for aircraft recovery are computationally expensive and operationally infeasible in practice. Heuristics and hybrid approaches offer faster solutions but have inconsistent solution quality, often leading to large losses. We propose a supervised machine learning approach to accelerate aircraft recovery by pruning the solution space of the optimization problem. It leverages similarities with previously solved problem instances through an offline model-training phase, identifies components of the optimal solutions for new problem instances in the online phase, and links them to the optimization model to rapidly generate high-quality solutions. Computational results, from multiple historical disruption instances for a large US airline, demonstrate that this approach significantly outperforms exact methods on computational runtime while producing similarly high-quality solutions. It also outperforms existing heuristics due to its ability to prune solution spaces in a more principled manner, leading to higher quality solutions in similarly short runtimes. For a runtime budget of two minutes, our approach provides a solution within 1.5% of the true optimal cost, resulting in an average daily saving of over $390,000 compared to all existing approaches. The main drivers of these improvements are explainable in terms of key airline operational metrics and are validated through extensive sensitivity and robustness tests.
飞机故障恢复中求解空间减小的机器学习方法
飞机恢复是航空公司运营恢复的关键步骤,旨在将航班时刻表中断的成本降至最低。精确的飞机回收方法在计算上是昂贵的,在实际操作中是不可行的。启发式方法和混合方法提供更快的解决方案,但解决方案质量不一致,经常导致巨大的损失。我们提出了一种有监督的机器学习方法,通过修剪优化问题的解空间来加速飞机恢复。它通过离线模型训练阶段利用与先前解决的问题实例的相似性,在在线阶段识别新问题实例的最优解决方案的组件,并将它们链接到优化模型,以快速生成高质量的解决方案。来自美国一家大型航空公司的多个历史中断实例的计算结果表明,该方法在计算运行时间上明显优于精确方法,同时产生类似的高质量解决方案。它还优于现有的启发式方法,因为它能够以更有原则的方式修剪解决方案空间,从而在类似的短运行时间内产生更高质量的解决方案。对于两分钟的运行时间预算,我们的方法提供的解决方案在真正最优成本的1.5%以内,与所有现有方法相比,平均每天节省超过390,000美元。这些改进的主要驱动因素可以用关键的航空公司运营指标来解释,并通过广泛的灵敏度和稳健性测试得到验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
European Journal of Operational Research
European Journal of Operational Research 管理科学-运筹学与管理科学
CiteScore
11.90
自引率
9.40%
发文量
786
审稿时长
8.2 months
期刊介绍: The European Journal of Operational Research (EJOR) publishes high quality, original papers that contribute to the methodology of operational research (OR) and to the practice of decision making.
×
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:604180095
Book学术官方微信