Constrained Market Share Maximization by Signal-Guided Optimization

Authors

  • Bo Hui Auburn University
  • Yuchen Fang Beijing University of Posts and Telecommunications
  • Tian Xia Auburn University
  • Sarp Aykent Auburn University
  • Wei-Shinn Ku Auburn University

DOI:

https://doi.org/10.1609/aaai.v37i4.25552

Keywords:

DMKM: Applications, APP: Economic/Financial

Abstract

With the rapid development of the airline industry, maximizing the market share with a constrained budget is an urgent econometric problem for an airline. We investigate the problem by adjusting flight frequencies on different flight routes. Owing to the large search space of solutions and the difficulty of predicting the market, this problem is in general daunting to solve. This paper proposes a novel two-stage optimization method to address the challenges. On the higher level, we use a signal to guide the optimization process toward a constrained satisfying solution. On the lower level, we consider the consecutive itineraries in real scenarios and model the unseen correlations between routes in itineraries for market share prediction. In theory, we prove the convergence of our optimization approach. In the experiment, we empirically verify the superiority of both our prediction model and optimization approach over existing works with large-scale real-world data. Our code has been released at: https://github.com/codingAndBS/AirlineMarket.

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Published

2023-06-26

How to Cite

Hui, B., Fang, Y., Xia, T., Aykent, S., & Ku, W.-S. (2023). Constrained Market Share Maximization by Signal-Guided Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 4330-4338. https://doi.org/10.1609/aaai.v37i4.25552

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management