The Data-Driven Censored Newsvendor Problem

TL;DR

This paper investigates a censored data-driven newsvendor problem, analyzing how demand censoring impacts learning performance, and proposes a robust algorithm with proven guarantees across various censoring levels.

Contribution

It provides a theoretical framework characterizing the limits of learning under demand censoring and introduces a robust algorithm with finite-sample guarantees and near-optimal performance.

Findings

Derived a necessary and sufficient condition for vanishing regret under demand censoring.

Established a lower bound on policy performance due to demand censoring.

Proposed a robust algorithm with finite-sample guarantees and demonstrated its effectiveness.

Abstract

We study a censored variant of the data-driven newsvendor problem, where the decision-maker must select an ordering quantity that minimizes expected overage and underage costs based only on offline censored sales data, rather than historical demand realizations. Our goal is to understand how the degree of historical demand censoring affects the performance of any learning algorithm for this problem. To isolate this impact, we adopt a distributionally robust optimization framework, evaluating policies according to their worst-case regret over an ambiguity set of distributions. This set is defined by the largest historical order quantity (the observable boundary of the dataset), and contains all distributions matching the true demand distribution up to this boundary, while allowing them to be arbitrary afterwards. We demonstrate a spectrum of achievability under demand censoring by deriving a natural necessary and sufficient condition under which vanishing regret is an achievable goal. In regimes in which it is not, we exactly characterize the information loss due to censoring: an insurmountable lower bound on the performance of any policy, even when the decision-maker has access to infinitely many demand samples. We then leverage these sharp characterizations to propose a natural robust algorithm that adapts to the historical level of demand censoring. We derive finite-sample guarantees for this algorithm across all possible censoring regimes and show its near-optimality with matching lower bounds (up to polylogarithmic factors). We moreover demonstrate its robust performance via extensive numerical experiments on both synthetic and real-world datasets.