Dear ROW community,
https://sites.google.com/view/row-series/home
Next talk:
ROW by Prof. Vineet Goyal
Time: January 23, 2026, at 15:30 CET
Title:
Distributionally Robust Newsvendor on a Metric
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 (where the expectation is taken over the worst case choice of distribution with given mean and variance).
We present a significant generalization of the classical result of Scarf (1958) and give a policy with strong theoretical guarantees as well as good practical performance while maintaining the simplicity and interpretability of the solution in Scarf (1958). In particular, our policy first identifies a hierarchical clustering of the locations, and assigns a "virtual-underage cost" for each cluster. Our inventory solution ensures that for each cluster, the total inventory in the cluster is at least as large as the inventory level suggested by Scarf's solution for the virtual-underage cost if the cluster was a single point. We present a worst-case performance guarantee for our policy and also demonstrate that the policy performs well in practice. To the best of our knowledge, this is the first algorithm with provable performance guarantees.
(This is joint work with Ayoub Foussoul)
Bio:
Vineet Goyal is Professor in the Industrial Engineering and Operations Research Department at Columbia University where he joined in 2010 after his PhD in Algorithms, Combinatorics, and Optimization (ACO) from Carnegie Mellon University in 2008 and Postdoc at the Operations Research Center at MIT. He is interested in the design of efficient and robust data-driven algorithms for large scale dynamic optimization problems with applications in revenue management, health care and resource allocation problems. His research has been continually supported by grants from NSF, DARPA and industry including NSF CAREER Award in 2014 and faculty research awards from Google, IBM, Adobe, and Amazon.
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* January 23, 2026: Vineet Goyal (Columbia University)
* February 6, 2026: Beste Basciftci (University of Iowa)
* February 20, 2026: Justin Starreveld (University of Amsterdam) & Irina Wang (Princeton University)
* March 6, 2026: Emilio Carrizosa (University of Seville)
* April 17, 2026: Soroosh Shafiee (Cornell University)
* May 1, 2026: Bo Zeng (University of Pittsburgh)
* May 15, 2026: Halil Ibrahim Bayrak (TU Munich) & Menglei Jia (Shanghai Jiao Tong University)
* May 29, 2026: Peter Zhang (Carnegie Mellon University)
* June 12, 2026: Igor Malheiros (University of Montpellier) & TBA
* June 26, 2026: Amir Ardestani Jaafari (University of British Columbia)