Dynamic Pricing Through Data Sampling

In this paper 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 robust and dynamic pricing strategy that maximizes revenue over the entire selling season. We develop a tractable optimization model that directly uses demand data, therefore creating a practical decision tool. Furthermore, we provide theoretical performance guarantees for this sampling-based solution, based on the number of samples used. Finally, we compare the revenue performance of our model using numerical simulations, exploring the behavior of the model with different robust objectives, sample sizes, and sampling distributions. This modeling approach could be particularly important for risk-averse managers with limited access to historical data or information about the demand distribution.