Airline Timetable Development and Fleet Assignment Incorporating Passenger Choice

运筹学 计算机科学 启发式 利润(经济学) 背景(考古学) 机组调度 车队管理 调度(生产过程) 数学优化 工程类 经济 电信 生物 操作系统 数学 古生物学 微观经济学
作者
Keji Wei,Vikrant Vaze,Alexandre Jacquillat
出处
期刊:Transportation Science [Institute for Operations Research and the Management Sciences]
卷期号:54 (1): 139-163 被引量:30
标识
DOI:10.1287/trsc.2019.0924
摘要

Flight timetabling can greatly impact an airline’s operating profit, yet data-driven or model-based solutions to support it remain limited. Timetabling optimization is significantly complicated by two factors. First, it exhibits strong interdependencies with subsequent fleet assignment decisions of the airlines. Second, flights’ departure and arrival times are important determinants of passenger connection opportunities, of the attractiveness of each (nonstop or connecting) itinerary, and, in turn, of passengers’ booking decisions. Because of these complicating factors, most existing approaches rely on incremental timetabling. This paper introduces an original integrated optimization approach to comprehensive flight timetabling and fleet assignment under endogenous passenger choice. Passenger choice is captured by a discrete-choice generalized attraction model. The resulting optimization model is formulated as a mixed-integer linear program. This paper also proposes an original multiphase solution approach, which effectively combines several heuristics, to optimize the network-wide timetable of a major airline within a realistic computational budget. Using case study data from Alaska Airlines, computational results suggest that the combination of this paper’s model formulation and solution approaches can result in significant profit improvements as compared with the most advanced incremental approaches to flight timetabling. Additional computational experiments based on several extensions also demonstrate the benefits of this modeling and computational framework to support various types of strategic airline decision making in the context of frequency planning, revenue management, and postmerger integration.
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