Dynamic Pricing through Data Sampling

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.

• 저널Production and Operations Management · 27(6) · 1074–1088
• 토픽Advanced Statistical Process Monitoring · Statistics, Probability and Uncertainty
• DOI10.1111/poms.12854
• 저자Maxime C. Cohen, Ruben Lobel, Georgia Perakis