Nonparametric multi-product dynamic pricing with demand learning via simultaneous price perturbation

Xiangyu Yang, Jianghua Zhang, Jian-Qiang Hu and Jiaqiao Hu

European Journal of Operational Research, 2024, vol. 319, issue 1, 191-205

Abstract: We consider the problem of multi-product dynamic pricing with demand learning and propose a nonparametric online learning algorithm based on the simultaneous perturbation stochastic approximation (SPSA) method. The algorithm uses only two price experimentations at each iteration, regardless of problem dimension, and could be especially efficient for solving high-dimensional problems. Under moderate conditions, we prove that the price estimates converge in mean-squared error (MSE) to the optimal price. Furthermore, we show that by suitably choosing input parameters, our algorithm achieves an expected cumulative regret of order OT over T periods, which is the best possible growth rate in the literature. The exact constants in the rate can be identified explicitly. We investigate the extensions of the algorithm to application scenarios characterized by non-stationary demand and inventory constraints. Simulation experiments reveal that our algorithm is effective for a range of test problems and performs favorably compared with a recently proposed alternative method for high-dimensional problems.

Keywords: Decision analysis; Dynamic pricing with demand learning; Online learning; Simultaneous perturbation stochastic approximation (SPSA); Revenue management (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037722172400465X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:319:y:2024:i:1:p:191-205

DOI: 10.1016/j.ejor.2024.06.017

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().