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Degradable airline scheduling : an approach to improve operational robustness and differentiate service quality

Author(s)
Kang, Laura Sumi, 1977-
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Massachusetts Institute of Technology. Operations Research Center.
Advisor
John-Paul Clarke.
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M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
We present a methodology for deriving robust airline schedules that are not vulnerable to disruptions caused by bad weather. In this methodology, the existing schedule is partitioned into independent sub-schedules or layers - prioritized on the basis of revenue - that provide airlines with a clear delay/cancellation policy and may enable them to market and sell tickets for flight legs based oil passenger preference for reliability. We present three different ways to incorporate degradability into the scheduling process: (1) between flight scheduling and fleet assignment (degradable schedule partitioning model), (2) with fleet assignment (degradable fleet assignment model), and (3) with aircraft routing (degradable aircraft routing model). Each problem is modeled as an integer program. Search algorithms are applied to the degradable aircraft routing model, which has a large number of decision variables. Results indicate that we can successfully assign flight legs with high revenue itineraries in the higher priority layer without adding aircraft or changing the schedule, and differentiate the service quality for passengers in different priority layers. Passengers in the high priority layers have much less delay and fewer cancellations than passengers in low priority layers even during the bad weather. In terms of recovery cost, which includes revenue lost, operational cost saving and crew delay cost, degradable airline schedules can save up to $30,000 per day. Degradable airline schedules have cost saving effect, especially when an airport with a high capacity reduction in bad weather is affected by bad weather.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004.
 
Includes bibliographical references (p. 113-118).
 
Date issued
2004
URI
http://hdl.handle.net/1721.1/17659
Department
Massachusetts Institute of Technology. Operations Research Center; Sloan School of Management
Publisher
Massachusetts Institute of Technology
Keywords
Operations Research Center.

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