Dynamic Pricing through Data Sampling
Name
a613003c22f2a51925bf0442fdab581ef87a.pdf
Size
416.05 KB
Format
Adobe PDF
Checksum (MD5)
099d250895580930ef9a7b2c2a18d55c
Author(s)
Lobel, Ruben
Cohen, Maxime
Perakis, Georgia
Date Issued
February 2018
Journal
Production and Operations Management
Publisher
Wiley
Citation
Cohen, Maxime C., Ruben Lobel, and Georgia Perakis. “Dynamic Pricing through Data Sampling.” Production and Operations Management 27, no. 6 (March 1, 2018): 1074–1088.
Version
Original manuscript
Abstract
We study a dynamic pricing problem, where a firm offers a product to be sold over a fixed time horizon. The firm has a given initial inventory level, but there is uncertainty about the demand for the product in each time period. The objective of the firm is to determine a dynamic pricing strategy that maximizes revenue throughout the entire selling season. We develop a tractable optimization model that directly uses demand data, therefore creating a practical decision tool. We show computationally that regret-based objectives can perform well when compared to average revenue maximization and to a Bayesian approach. The modeling approach proposed in this study could be particularly useful for risk-averse managers with limited access to historical data or information about the true demand distribution. Finally, we provide theoretical performance guarantees for this sampling-based solution.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1111/POMS.12854
