Reinforcement learning applied to airline revenue management-Reference-Cited by-同舟云学术

Reinforcement learning applied to airline revenue management

Published:2020-01-28 Issue:5 Volume:19 Page:332-348
ISSN:1476-6930
Container-title:Journal of Revenue and Pricing Management
language:en
Short-container-title:J Revenue Pricing Manag

Author:

Bondoux Nicolas,Nguyen Anh Quan,Fiig Thomas,Acuna-Agost Rodrigo

Publisher

Springer Science and Business Media LLC

Subject

Strategy and Management,Economics and Econometrics,Finance,Business and International Management

Link

http://link.springer.com/content/pdf/10.1057/s41272-020-00228-4.pdf

Reference26 articles.

1. Aviv, Y., and A. Pazgal. 2005. Dynamic pricing of short life-cycle products through active learning. St. Louis: Olin School Business, Washington Univ.

2. Balvers, R.J., and T.F. Cosimano. 1990. Actively learning about demand and the dynamics of price adjustment. The Economic Journal 100 (402): 882–898.

3. Bartke, P., N. Kliewer, and C. Cleophas. 2018. Benchmarking filter-based demand estimates for airline revenue management. EURO Journal on Transportation and Logistics 7 (1): 57–88.

4. Carvalho, A.X., and M. Puterman. 2005. Learning and pricing in an internet environment with binomial demands. Journal of Revenue and Pricing Management 3: 320–336.

5. den Boer, A.V. 2012. Dynamic pricing and learning. PhD thesis, Vrije Universiteit Amsterdam.

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