Distributionally Robust Newsvendor on a Metric

TL;DR

This paper introduces a distributionally robust newsvendor model on a metric, providing a near-optimal, interpretable policy with theoretical guarantees for inventory and fulfillment decisions under demand uncertainty.

Contribution

It generalizes Scarf's classical solution to a multi-location, metric-based setting with distributional ambiguity, offering a provably effective policy with practical performance.

Findings

Policy achieves poly-logarithmic approximation ratio.

First algorithm with provable performance guarantees for this setting.

Numerical experiments confirm strong practical performance.

Abstract

We consider a fundamental generalization of the classical newsvendor problem where the seller needs to decide on the inventory of a product jointly for multiple locations on a metric as well as a fulfillment policy to satisfy the uncertain demand that arises sequentially over time after the inventory decisions have been made. To address the distributional ambiguity, we consider a distributionally robust setting where the decision-maker only knows the mean and variance of the demand, and the goal is to make inventory and fulfillment decisions to minimize the worst-case expected inventory and fulfillment cost. We design a near-optimal policy for the problem with theoretical guarantees on its performance. Our policy generalizes the classical solution of Scarf (1957), maintaining its simplicity and interpretability: it identifies a hierarchical set of clusters, assigns a ``virtual" underage cost to each cluster, then makes sure that each cluster holds at least the inventory suggested by Scarf's solution if the cluster behaved as a single point with ``virtual" underage cost. As demand arrives sequentially, our policy fulfills orders from nearby clusters, minimizing fulfilment costs, while balancing inventory consumption across the clusters to avoid depleting any single one. We show that the policy achieves a poly-logarithmic approximation. To the best of our knowledge, this is the first algorithm with provable performance guarantees. Furthermore, our numerical experiments show that the policy performs well in practice.