The Library
Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications
Tools
Tools
Tools
Doan, Xuan Vinh, Lei, Xiao and Shen, Siqian (2020) Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications. European Journal of Operational Research, 282 (1). pp. 235-251. doi:10.1016/j.ejor.2019.09.003
|
PDF
WRAP-pricing-reusable-resources-under-ambiguous-distributions-demand-service-time-emerging-applications-Doan-2019.pdf - Accepted Version - Requires a PDF viewer. Download (1037Kb) | Preview |
Official URL: https://doi.org/10.1016/j.ejor.2019.09.003
Abstract
Monopolistic pricing models for revenue management are widely used in practice to set prices of multiple products with uncertain demand arrivals. The literature often assumes deterministic time of serving each demand and that the distribution of uncertainty is fully known. In this paper, we consider a new class of revenue management problems inspired by emerging applications such as cloud computing and city parking, where we dynamically determine prices for multiple products sharing limited resource and aim to maximize the expected revenue over a finite horizon. Random demand of each product arrives in each period, modeled by a function of the arrival time, product type, and price. Unlike the traditional monopolistic pricing, here each demand stays in the system for uncertain time. Both demand and service time follow ambiguous distributions, and we formulate robust deterministic approximation models to construct efficient heuristic fixed-price pricing policies. We conduct numerical studies by testing cloud computing service pricing instances based on data published by the Amazon Web Services (AWS) and demonstrate the efficacy of our approach for managing revenue and risk under various distributions of demand and service time.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HF Commerce Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
||||||||
| Divisions: | Faculty of Social Sciences > Warwick Business School > Operational Research & Management Sciences Faculty of Social Sciences > Warwick Business School |
||||||||
| Library of Congress Subject Headings (LCSH): | Risk management , Revenue management, Pricing, Pricing -- Mathematical models, Cloud computing | ||||||||
| Journal or Publication Title: | European Journal of Operational Research | ||||||||
| Publisher: | Elsevier Science BV | ||||||||
| ISSN: | 0377-2217 | ||||||||
| Official Date: | 1 April 2020 | ||||||||
| Dates: |
|
||||||||
| Volume: | 282 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | pp. 235-251 | ||||||||
| DOI: | 10.1016/j.ejor.2019.09.003 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| RIOXX Funder/Project Grant: |
|
||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
