2026-09-19T04:56:29Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/452872022-01-13T07:54:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Hamsa Balakrishnan.
Phu, Thi Vu
Massachusetts Institute of Technology. Computation for Design and Optimization Program.
Massachusetts Institute of Technology. Computation for Design and Optimization Program
2009-04-29T17:20:44Z
2009-04-29T17:20:44Z
2008
2008
http://hdl.handle.net/1721.1/45287
311865384
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.
Includes bibliographical references (leaves 73-74).
The steady increase of congestion in air traffic networks has resulted in significant economic losses and potential safety issues in the air transportation. A potential way to reduce congestion is to adopt efficient air traffic management policies, such as, optimally scheduling and routing air traffic throughout the network. In recent years, several models have been proposed to predict and manage air traffic. This thesis focuses on the comparison of two such approaches to air traffic flow management: (i) a discrete Mixed Integer Program model, and (ii) a continuous flow-based model. The continuous model is applied in a multi-commodity setting to take into account the origins and destinations of the aircraft. Sequential quadratic programming is used to optimize the continuous model. A comparison of the performance of the two models based on a set of large scale test cases is provided. Preliminary results suggest that the linear programming relaxation of the discrete model provides results similar to the continuous flow-based model for high volumes of air traffic.
by Thi Vu Phu.
S.M.
74 leaves
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Computation for Design and Optimization Program.
A comparison of discrete and flow-based models for air traffic flow management
Thesis
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<Abstract>The steady increase of congestion in air traffic networks has resulted in significant economic losses and potential safety issues in the air transportation. A potential way to reduce congestion is to adopt efficient air traffic management policies, such as, optimally scheduling and routing air traffic throughout the network. In recent years, several models have been proposed to predict and manage air traffic. This thesis focuses on the comparison of two such approaches to air traffic flow management: (i) a discrete Mixed Integer Program model, and (ii) a continuous flow-based model. The continuous model is applied in a multi-commodity setting to take into account the origins and destinations of the aircraft. Sequential quadratic programming is used to optimize the continuous model. A comparison of the performance of the two models based on a set of large scale test cases is provided. Preliminary results suggest that the linear programming relaxation of the discrete model provides results similar to the continuous flow-based model for high volumes of air traffic.</Abstract>
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