Dynamic pricing of wireless internet based on usage and stochastically changing capacity

Demet Batur, Jennifer K. Ryan, Zhongyuan Zhao, Mehmet C. Vuran

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Problem definition: Inspired by new developments in dynamic spectrum access, we study the dynamic pricing of wireless Internet access when demand and capacity (bandwidth) are stochastic. Academic/practical relevance: The demand for wireless Internet access has increased enormously. However, the spectrum available to wireless service providers is limited. The industry has, thus, altered conventional license-based spectrum access policies through unlicensed spectrum operations. The additional spectrum obtained through these operations has stochastic capacity. Thus, the pricing of this service by the service provider has novel challenges. The problem considered in this paper is, therefore, of high practical relevance and new to the academic literature. Methodology: We study this pricing problem using a Markov decision process model in which customers are posted dynamic prices based on their bandwidth requirement and the available capacity. Results: We characterize the structure of the optimal pricing policy as a function of the system state and of the input parameters. Because it is impossible to solve this problem for practically large state spaces, we propose a heuristic dynamic pricing policy that performs very well, particularly when the ratio of capacity to demand rate is low. Managerial implications: We demonstrate the value of using a dynamic heuristic pricing policy compared with the myopic and optimal static policies. The previous literature has studied similar systems with fixed capacity and has characterized conditions under which myopic policies perform well. In contrast, our setting has dynamic (stochastic) capacity, and we find that identifying good state-dependent heuristic pricing policies is of greater importance. Our heuristic policy is computationally more tractable and easier to implement than the optimal dynamic and static pricing policies. It also provides a significant performance improvement relative to the myopic and optimal static policies when capacity is scarce, a condition that holds for the practical setting that motivated this research.

Original languageEnglish (US)
Pages (from-to)833-852
Number of pages20
JournalManufacturing and Service Operations Management
Volume21
Issue number4
DOIs
StatePublished - Sep 2019

Keywords

  • Dynamic pricing
  • Markov decision process
  • Stochastic capacity
  • Wireless internet

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

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