Flight short-term booking demand forecasting based on a long short-term memory network

He, Haonan; Chen, Liangyu; Wang, Shanyong

doi:10.1016/j.cie.2023.109707

Cited by 2 publications

(1 citation statement)

References 58 publications

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“…It achieves a mean absolute percentage error (MAPE) that is significantly 45.1% lower than alternative techniques, emphasizing its enhanced predictive accuracy. This research not only showcases the relevance of deep learning algorithms in airline demand forecasting but also emphasizes their capacity to enable airlines in effectively managing demand-related risks and optimizing revenue management strategies [24].…”

Section: Quantity Decisionmentioning

confidence: 82%

Revenue Management Strategies in Airline Industry: A Literature Review

Manalu,

Demas Haryo Bismantoko,

Oktaviana Putri

et al. 2024

j.sist.teknik.industri.

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The airline industry has rapidly evolved, fostering intense competition among companies. This competition drives airlines to formulate strategies for revenue maximization, giving rise to Revenue Management. This literature review spans the past 13 years, examining the development of Airline Revenue Management methods. By analyzing 22 journals with at least Q2 and SINTA 2 indexing, three primary scopes emerge: Quantity Decision, Pricing Decision, and Structural Decision. Airlines predominantly employ dynamic pricing and programming to optimize revenue by adapting to ongoing changes. The development trend in Airline Revenue Management indicates a shift towards faster and more accurate processing through increased integration with simulation and algorithm programming. This paper identifies the three main scopes involved in revenue management strategies and explores the diverse approaches airlines take to optimize income. Notably, dynamic pricing and programming remain prevalent methods, adapting to changing decision variables. The evolving landscape emphasizes integration with advanced technology for efficient processing. The study utilizes numerical and case studies to exemplify the ongoing development of Airline Revenue Management methods within this dynamic industry.

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Section: Quantity Decisionmentioning

confidence: 82%