Newsvendor problems: An integrated method for estimation and optimisation

ABSTRACT

Newsvendor problems (NVP) form a classical and important family of stochastic optimisation problems. In this paper, we consider a data-driven method proposed recently by Ban and Rudin. We first examine it from a statistical viewpoint, and establish a connection with quantile regression. We then extend the approach to nonlinear NVP. Finally, we give extensive experimental results, on both simulated and real data. The results indicate that the approach performs as well as conventional ones when applied to linear NVP, but performs better when applied to nonlinear NVP. There is also evidence that the approach is more robust with respect to model misspecification. (C) 2021 Elsevier B.V. All rights reserved.