GOALI: Demand Driven Fleet Management Analysis, Models, and Algorithms for the Airline Industry

本课题研究的重点是航空公司供应管理的一个重要方面，即不确定条件下的机队分配决策及其与需求的有效协调。其目的是与我们的工业合作伙伴联合航空公司共同开发和实施通用模型和算法，以确保向航空业转让技术。航空公司目前的做法是在起飞前很早就将飞机运力分配给定期航班。这是由于机队分配决策和其他航空公司流程之间的相关性。然而，在如此早期的阶段，需求的高度不确定性对航空公司实现飞机运力与最终需求的最佳匹配构成了重大障碍。另一方面，随着时间的推移，需求预测的准确性显著提高。因此，最大限度地推迟不可撤销的转瞬即逝的决定，并在规定的决定中注入足够的灵活性，以便能够在业务限制范围内进行未来的修订，这是有利的。为了实现这一目标，我们提出了一个三阶段动态供应管理框架，该框架将初始机队分配模型与两个下游需求驱动的重新安排和交换模型协调起来，旨在利用系统的灵活性，同时纳入随着出发方法获得的逐步更准确的需求预测。其目的是在最初的机队分配决策中提供足够的灵活性，以允许有效的后续转机决策，从而促进需求与供应的更好匹配。如果成功，我们拟议的研究贡献的影响将是：(1)促进航空业改进供应管理决策，同时在此框架内考虑初始转机、后来的转机和交换模型之间的相互作用；以及(2)描述在此过程的每个阶段针对不同模型的有效解决方案，这可推广到其他类似的制造和服务行业环境。在进行这项研究中，我们建议通过结合各种特征，如基于路径级别和票价等级的需求、重新捕获溢出的乘客、一周中的需求变化和维护考虑，来研究几个模型改进在实践中的可表现性。因此，我们希望帮助消除与传统机队分配过程相关的几个关键弱点，为改进业务提供见解，并在实践中取得明显的最先进进展。

This research project proposes to focus on an essential aspect of airline supply management, namely, the fleet assignment decision, and its effective coordination with demand under conditions of uncertainty. The intent is to develop and implement general models and algorithms in concert with our industrial partner: United Airlines, in order to ensure the transfer of technology to the airline industry. The current airline practice is to assign aircraft capacity to scheduled flights well in advance of departures. This is due to the dependency between the fleet assignment decision and other airline processes. At such an early stage, however, the high uncertainty of demand poses a major impediment for airlines to best match aircraft capacities with the final demand. On the other hand, the accuracy of demand forecasts improves markedly over time. Thus, it becomes advantageous to postpone irrevocable fleeting decisions to as great a future extent as possible, and to inject sufficient flexibility in the prescribed decisions so as to facilitate the ability to make future revisions within the operational constraints. To accomplish this, we propose a three-stage dynamic supply management framework that coordinates the initial fleet assignment model with a pair of downstream demand driven re-fleeting and swapping models in a manner designed to exploit the flexibility in the system, while incorporating progressively more accurate demand forecasts obtained as departures approach. The intent is to provide sufficient flexibility in the initial fleet assignment decisions to permit effective subsequent re-fleeting decisions so as to promote a better matching of demand with supply.If successful, the impact of our proposed research contributions will be: (1) to promote improvedsupply management decisions in the airline industry, while considering the interactions betweenthe initial fleeting, later re-fleeting, and swapping models within this framework; and (2) toprescribe effective solution approaches for the various models at each stage of this process, whichcan be extended to other similar manufacturing and service industrial environments. Inconducting this study, we propose to investigate several model enhancements to improverepresentability in practice by incorporating various features such as path-level and fare-classbased demands, recapture of spilled passengers, day-of-week demand variations, andmaintenance considerations. We hope thereby to help eliminate several critical weaknessesassociated with the traditional fleet assignment process, lend insights into improving operations,and achieve a demonstrable advance in the state-of-the-art in practice.