A nonparametric self-adjusting control for joint learning and optimization of multi-product pricing with finite resource capacity

Chen, Q, Jasin, S and Duenyas, I (2019) A nonparametric self-adjusting control for joint learning and optimization of multi-product pricing with finite resource capacity. Mathematics of Operations Research, 44 (2). pp. 601-631. ISSN 0364-765X OPEN ACCESS

Abstract

We study a multi-period network revenue management problem where a seller sells multiple products, made from multiple resources with infinite capacity, in an environment where the underlying demand function is a priori unknown (in the nonparametric sense). The objective of the seller is to simultaneously learn the unknown demand function and dynamically price his products to minimize the expected revenue loss. For the problem where the number of selling periods and initial capacity are scaled by k > 0, it is known that
the expected revenue loss of any non-anticipating pricing policy is
(pk). However, there is a considerable gap between this theoretical lower bound and the performance bound of the best known heuristic control in the literature. In this paper, we propose a Nonparametric Self-adjusting Control and show that its expected revenue loss is O(k1=2+ log k) for any arbitrarily small >0, provided that the underlying demand function is sufficiently smooth. This is the tightest bound of its kind for the problem setting that we consider in this paper and it significantly improves the performance bound of existing heuristic controls in the literature; in addition, our intermediate results on the large deviation bounds for spline estimation and nonparametric stability analysis of constrained optimization are of independent interest and are potentially useful for other applications.

More Details

Item Type: Article
Subject Areas: Management Science and Operations
Additional Information:

© 2019 INFORMS

Date Deposited: 21 Feb 2019 14:28
Date of first compliant deposit: 19 Nov 2019
Subjects: Learning
Last Modified: 18 Feb 2024 01:40
URI: https://lbsresearch.london.edu/id/eprint/1083
More

Export and Share


Download

Accepted Version - Text

Statistics

Altmetrics
View details on Dimensions' website

Downloads from LBS Research Online

View details

Actions (login required)

Edit Item Edit Item