Authors: Byung Do Chung, Jiahan Li, Tao Yao
Addresses: The Harold & Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA. ' Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA. ' The Harold & Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA
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.
Keywords: demand learning; dynamic pricing; inventory replenishment; inventories; Markov chain Monte Carlo; nonparametric learning; price-sensitivity parameters; uncertain parameters; autoregressive models; functional-coefficient models; state-space models; nonparametric models; Thomas Bayes; Bayesian methods; optimal control models; optimal pricing; inventory plans; single replenishment policies; dynamic replenishment policies; inventory control; uncertainty; services operations; informatics.
International Journal of Services Operations and Informatics, 2011 Vol.6 No.3, pp.259 - 271
Available online: 22 Jul 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article