Choice-Based Airline Schedule Design and Fleet Assignment: A Decomposition Approach

计算机科学 地铁列车时刻表 分解 本德分解 运筹学 调度(生产过程) 水准点(测量) 网络规划与设计 利润(经济学) 航空 数学优化 工程类 经济 数学 生物 操作系统 大地测量学 航空航天工程 微观经济学 计算机网络 地理 生态学
作者
Chiwei Yan,Cynthia Barnhart,Vikrant Vaze
出处
期刊:Transportation Science [Institute for Operations Research and the Management Sciences]
卷期号:56 (6): 1410-1431 被引量:11
标识
DOI:10.1287/trsc.2022.1141
摘要

We study an integrated airline schedule design and fleet assignment model for constructing schedules by simultaneously selecting from a pool of optional flights and assigning fleet types to these scheduled flights. This is a crucial tactical decision that greatly influences airline profits. As passenger demand is often substitutable among available fare products (defined as a combination of an itinerary and a fare class) between the same origin–destination pair, we present an optimization approach that includes a passenger choice model for fare product selections. To tackle the formidable computational challenge of solving this large-scale network design problem, we propose a decomposition approach based on partitioning the flight network into smaller subnetworks by exploiting weak dependencies in network structure. The decomposition relies on a series of approximation analyses and a novel fare split problem to allocate optimally the fares of products that are shared by flights in different subnetworks. We present several reformulations that represent fleet assignment and schedule decisions and formally characterize their relative strengths. This gives rise to a new reformulation that is able to trade off strength and size flexibly. We conduct detailed computational experiments using two realistically sized airline instances to demonstrate the effectiveness of our approach. Under a simulated passenger booking environment with both perfect and imperfect forecasts, we show that the fleeting and scheduling decisions informed by our approach deliver significant and robust profit improvement over all benchmark implementations and previous models in the literature.
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