TY - GEN
T1 - Large-scale flight frequency optimization with global convergence in the US domestic air passenger markets
AU - Jeon, Jinsung
AU - Lee, Dongeun
AU - Hwang, Seunghyun
AU - Kang, Soyoung
AU - Park, Noseong
AU - Li, Duanshun
AU - Lee, Kookjin
AU - Liu, Jing
N1 - Funding Information:
Noseong Park (noseong@yonsei.ac.kr) is the corresponding author. This work was supported by the IITP grant funded by the Korea government (MSIT), No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University).
Publisher Copyright:
© 2021 by SIAM.
PY - 2021
Y1 - 2021
N2 - The US domestic air passenger transportation is one of the largest markets worldwide. Optimally allocating flights to the US domestic airways (i.e., air routes) is essential in maximizing the revenue of airlines and many research works have been proposed to improve their market shares/profits. Most proposed methods, however, suffer from a lack of scalability; even state-of-the-art methods demonstrate their performance with only tens of routes. To address this shortcoming, we propose a novel unified framework to integrate the market share prediction model and the frequency optimization module, which significantly improves the scalability of the entire framework. By design, our proposed prediction model is concave w.r.t. flight frequency and its gradients are Lipschitz continuous. Exploiting these two properties allows us to use an alternating direction method of multipliers (ADMM)-based optimization technique, which quickly solves a large-scale frequency optimization problem with guaranteed global convergence. Our proposed method is able to solve a problem whose search space size is O(n700) (vs. O(n30) in existing works).
AB - The US domestic air passenger transportation is one of the largest markets worldwide. Optimally allocating flights to the US domestic airways (i.e., air routes) is essential in maximizing the revenue of airlines and many research works have been proposed to improve their market shares/profits. Most proposed methods, however, suffer from a lack of scalability; even state-of-the-art methods demonstrate their performance with only tens of routes. To address this shortcoming, we propose a novel unified framework to integrate the market share prediction model and the frequency optimization module, which significantly improves the scalability of the entire framework. By design, our proposed prediction model is concave w.r.t. flight frequency and its gradients are Lipschitz continuous. Exploiting these two properties allows us to use an alternating direction method of multipliers (ADMM)-based optimization technique, which quickly solves a large-scale frequency optimization problem with guaranteed global convergence. Our proposed method is able to solve a problem whose search space size is O(n700) (vs. O(n30) in existing works).
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M3 - Conference contribution
AN - SCOPUS:85120999498
T3 - SIAM International Conference on Data Mining, SDM 2021
SP - 711
EP - 719
BT - SIAM International Conference on Data Mining, SDM 2021
PB - Siam Society
T2 - 2021 SIAM International Conference on Data Mining, SDM 2021
Y2 - 29 April 2021 through 1 May 2021
ER -