当前位置: X-MOL 学术EURO Journal on Transportation and Logistics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation
EURO Journal on Transportation and Logistics ( IF 2.1 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.ejtl.2020.100020
Yassine Yaakoubi , François Soumis , Simon Lacoste-Julien

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of complex rules and regulations, determining whether a sequence of flights constitutes a feasible pairing can be quite difficult by itself, making CPP one of the hardest of the airline planning problems. In this paper, we first propose to improve the prototype Baseline solver of Desaulniers et al. (2020) by adding dynamic control strategies to obtain an efficient solver for large-scale CPPs: Commercial-GENCOL-DCA. These solvers are designed to aggregate the flights covering constraints to reduce the size of the problem. Then, we use machine learning (ML) to produce clusters of flights having a high probability of being performed consecutively by the same crew. The solver combines several advanced Operations Research techniques to assemble and modify these clusters, when necessary, to produce a good solution. We show, on monthly CPPs with up to 50 000 flights, that Commercial-GENCOL-DCA with clusters produced by ML-based heuristics outperforms Baseline fed by initial clusters that are pairings of a solution obtained by rolling horizon with GENCOL. The reduction of solution cost averages between 6.8% and 8.52%, which is mainly due to the reduction in the cost of global constraints between 69.79% and 78.11%.

中文翻译:

机组人员配对中的机器学习以构建用于动态约束集合的初始聚类

机组配对问题(CPP)通常被建模为集合划分问题,其中必须将飞行成对进行划分。配对是由连接时间和休息时间分开的一系列飞行航程,从相同的起点开始和结束。由于存在大量复杂的法规,因此,确定一个航班的飞行顺序是否构成可行的配对本身非常困难,这使CPP成为最难的航空公司计划问题之一。在本文中,我们首先提出改进Desaulniers等人的基线求解器原型。(2020年),通过添加动态控制策略来获得大型CPP的高效求解器:Commercial-GENCOL-DCA。这些求解器旨在汇总涵盖约束的航班,以减小问题的规模。然后,我们使用机器学习(ML)来产生由同一机组连续执行的机率很高的机群。该求解器结合了几种先进的运筹学技术,可以在必要时组装和修改这些群集,以产生良好的解决方案。我们显示,在每月进行多达5万次飞行的CPP上,采用基于ML的启发式方法生成的集群的Commercial-GENCOL-DCA优于通过初始集群生成的Baseline,该初始集群是通过GENCOL滚动获得的解决方案的配对。解决方案成本的平均降低幅度为6.8%至8.52%,这主要是由于全球约束的成本降低了69.79%至78.11%。该求解器结合了几种先进的运筹学技术,可以在必要时组装和修改这些群集,以产生良好的解决方案。我们显示,在每月进行多达5万次飞行的CPP上,采用基于ML的启发式方法生成的集群的Commercial-GENCOL-DCA优于通过初始集群生成的Baseline,该初始集群是通过GENCOL滚动获得的解决方案的配对。解决方案成本的平均降低幅度为6.8%至8.52%,这主要是由于全球约束的成本降低了69.79%至78.11%。该求解器结合了几种先进的运筹学技术,可以在必要时组装和修改这些群集,以产生良好的解决方案。我们显示,在每月进行多达5万次飞行的CPP上,采用基于ML的启发式方法生成的集群的Commercial-GENCOL-DCA优于通过初始集群生成的Baseline,该初始集群是通过GENCOL滚动获得的解决方案的配对。解决方案成本的平均降低幅度为6.8%至8.52%,这主要是由于全球约束的成本降低了69.79%至78.11%。带有通过基于ML的启发式方法生成的集群的Commercial-GENCOL-DCA优于由初始集群提供的基线,该初始集群是通过与GENCOL滚动视野而获得的解决方案的配对。解决方案成本的平均降低幅度为6.8%至8.52%,这主要是由于全球约束的成本降低了69.79%至78.11%。带有通过基于ML的启发式技术生成的集群的Commercial-GENCOL-DCA优于通过初始集群提供的基线,该初始集群是通过与GENCOL滚动视野而获得的解决方案的配对。解决方案成本的平均降低幅度为6.8%至8.52%,这主要是由于全球约束的成本降低了69.79%至78.11%。
更新日期:2020-09-01
down
wechat
bug