Constant Regret Resolving Heuristics for Price-Based Revenue Management

Yining Wang () and He Wang ()
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Yining Wang: Warrington College of Business, University of Florida, Gainesville, Florida 32611
He Wang: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

Operations Research, 2022, vol. 70, issue 6, 3538-3557

Abstract: Price-based revenue management is an important problem in operations management with many practical applications. The problem considers a seller who sells one or multiple products over T consecutive periods and is subject to constraints on the initial inventory levels of resources. Whereas, in theory, the optimal pricing policy could be obtained via dynamic programming, computing the exact dynamic programming solution is often intractable. Approximate policies, such as the resolving heuristics, are often applied as computationally tractable alternatives. In this paper, we show the following two results for price-based network revenue management under a continuous price set. First, we prove that a natural resolving heuristic attains O (1) regret compared with the value of the optimal policy. This improves the O ( ln T ) regret upper bound established in the prior work by Jasin in 2014. Second, we prove that there is an Ω ( ln T ) gap between the value of the optimal policy and that of the fluid model. This complements our upper bound result by showing that the fluid is not an adequate information-relaxed benchmark when analyzing price-based revenue management algorithms.

Keywords: Stochastic Models; resolving; self-adjusting controls; price-based revenue management; dynamic pricing (search for similar items in EconPapers)
Date: 2022
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