Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 73-74). / In the face of large increases in the number of passengers and flights, busy airports worldwide have been trying to optimize operating efficiency and throughput and minimize congestion on a daily basis. In the case of departures, measures can be taken at the gate, on the taxiway system or at the runway queue to minimize departure delays and/or the cost of unavoidable delays. This cost includes needless fuel consumption and noxious emissions. In this thesis, we focus primarily on runway queue optimization. The first part of this work consists of designing a generic simulation which models specific days of operations at an airport. Using as input the schedule of operations specific to the modeled airport, the simulation processes all departures and stores the characteristic times of the process for each departing aircraft. The quantities of interest are either incrementally computed by the simulation or modeled using probability distributions derived from airport-specific data. We then present a dynamic programming approach to sequencing departing aircraft at the runway queue. Two algorithms are presented based on the idea of Constrained Position Shifting, which maintains a high level of fairness in the order in which aircraft gain access to runways, while also improving efficiency by comparison to First Come First Served sequencing. The objective of the first algorithm is to minimize makespan, and that of the second to minimize delays. We then focus on a specific airport, which has been experiencing one of the fastest growth rates in the industry. We analyze the output of our simulation as applied to this airport and accumulate insights about congestion at the departure runways. We next apply this sequencing algorithm to this specific airport using multiple demand profiles that represent both the current traffic levels, as well as anticipated future ones that would result in more congestion. We give quantitative arguments to confirm the positive impact of the optimization on the airport's operations. We also emphasize the importance of the aircraft mix on the techniques' performance and show that the sequencing algorithms provide higher benefits (in terms of reducing delays) as the mix becomes more heterogeneous. / by Alexandre Paul Sahyoun. / S.M.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/92700 |
| Date | January 2014 |
| Creators | Sahyoun, Alexandre Paul |
| Contributors | Amedeo R. Odoni., Massachusetts Institute of Technology. Operations Research Center., Massachusetts Institute of Technology. Operations Research Center. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 74 pages, application/pdf |
| Rights | 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 |
Page generated in 0.0017 seconds