Dynamic pricing and inventory control with nonparametric demand learning

Byung Do Chung, Jiahan Li, Tao Yao

Research output: Contribution to journalArticle

Abstract

This paper studies joint dynamic pricing and inventory planning with demand learning. Demand is assumed to be a function of price with an uncertain price-sensitivity parameter. We introduce a nonparametric functional-coefficient autoregressive (FAR) state-space model without assumptions on the parametric structure and apply a Bayesian method using Markov chain Monte Carlo (MCMC) algorithms to estimate model parameters. We develop an optimal control model and obtain optimal pricing and inventory plan based on the estimated parameters. We use numerical computations with single and dynamic replenishment policies to evaluate the proposed demand learning algorithm and optimal control based methods and demonstrate the importance of dynamic pricing, inventory control, and demand learning.

Original languageEnglish
Pages (from-to)259-271
Number of pages13
JournalInternational Journal of Services Operations and Informatics
Volume6
Issue number3
DOIs
Publication statusPublished - 2011 Jul 1

Fingerprint

Inventory control
Costs
Markov processes
Learning algorithms
Planning
Dynamic pricing
Optimal control

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

Cite this

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Dynamic pricing and inventory control with nonparametric demand learning. / Chung, Byung Do; Li, Jiahan; Yao, Tao.

In: International Journal of Services Operations and Informatics, Vol. 6, No. 3, 01.07.2011, p. 259-271.

Research output: Contribution to journalArticle

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