Assessing the value of dynamic pricing in network revenue management

Dan Zhang, Zhaosong Lu

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Dynamic pricing for a network of resources over a finite selling horizon has received considerable attention in recent years, yet few papers provide effective computational approaches to solve the problem. We consider a resource decomposition approach to solve the problem and investigate the performance of the approach in a computational study. We compare the performance of the approach to static pricing and choice-based availability control. Our numerical results show that dynamic pricing policies from network resource decomposition can achieve significant revenue lift compared with choice-based availability control and static pricing, even when the latter is frequently resolved. As a by-product of our approach, network decomposition provides an upper bound in revenue, which is provably tighter than the well-known upper bound from a deterministic approximation.

Original languageEnglish (US)
Pages (from-to)102-115
Number of pages14
JournalINFORMS Journal on Computing
Volume25
Issue number1
DOIs
StatePublished - Dec 2013
Externally publishedYes

Keywords

  • Approximate dynamic programming
  • Choice models
  • Dynamic pricing
  • Revenue management

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