Learning and optimizing through dynamic pricing
Ang Li and
Wei Wang ()
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Ravi Kumar: PROS Inc.
Ang Li: PROS Inc.
Wei Wang: PROS Inc.
Journal of Revenue and Pricing Management, 2018, vol. 17, issue 2, 63-77
Abstract Many airlines have been actively looking into class-free inventory control approaches, in which the control policy consists of dynamically varying prices over a continuous interval rather than opening and closing fare classes. As evidenced both in literature and in practice, one of the big challenges in this setting is the trade-off between policies that learn the demand parameters quickly and those that maximize expected revenue. Starting in a typical single-leg airline revenue management context, we investigate the applicability of recent advances in the area of optimal control with learning. We consider a demand model where customers’ maximum willingness-to-pay has a Gaussian distribution and we analyze several estimation and pricing approaches that include the expectation–maximization and a scheme of active generation of price variability. We show that our model ensures discovery of the underlying customer behavior while providing an appropriate level of expected revenue via a simulated example.
Keywords: Dynamic pricing; Exploration–exploitation; Revenue management; Regret; Expectation–maximization; Simulation (search for similar items in EconPapers)
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