Solving Data-Driven Newsvendor Pricing Problems with Decision-Dependent Effect

Understand

This paper investigates the data-driven pricing newsvendor problem, which focuses on maximizing expected profit by deciding on inventory and pricing levels based on historical demand and feature data.

• We first build an approximate model by assigning weights to historical samples.
• However, due to decision-dependent effects, the resulting approximate model is complicated and unable to solve directly.
• To address this issue, we introduce the concept of approximate gradients and design an Approximate Gradient Descent (AGD) algorithm.