Distributionally Robust Newsvendor on a Metric
Ayoub Foussoul, Vineet Goyal

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Platforms and Economics · Business Strategy and Innovation · Economic theories and models
