14 citations · 24 across the 9 of their papers we have counts for
9 papers
The Value of Human Expertise
Bradley Sturt
We consider optimization applications with unknown parameters where the decision maker believes that the optimal value of the nominal problem-the optimization problem they would ha…
Assortment Optimization and the Sample Average Approximation
Hassaan Khalid, Bradley Sturt
We consider a simple approach to solving assortment optimization under the random utility maximization model. The approach uses Monte-Carlo simulation to construct a ranking-based…
The Blessing of Strategic Customers in Personalized Pricing
Zhi Chen, Bradley Sturt, Weijun Xie
We consider a feature-based personalized pricing problem in which the buyer is strategic: given the seller's pricing policy, the buyer can augment the features that they reveal to…
Improving the Security of United States Elections with Robust Optimization
Braden L. Crimmins, J. Alex Halderman, Bradley Sturt
For more than a century, election officials across the United States have inspected voting machines before elections using a procedure called Logic and Accuracy Testing (LAT). This…
Group fairness in dynamic refugee assignment
Daniel Freund, Thodoris Lykouris, Elisabeth Paulson +2
Ensuring that refugees and asylum seekers thrive (e.g., find employment) in their host countries is a profound humanitarian goal, and a primary driver of employment is the geograph…
The Value of Robust Assortment Optimization Under Ranking-based Choice Models
Bradley Sturt
We study a class of robust assortment optimization problems that was proposed by Farias, Jagabathula, and Shah (2013). The goal in these problems is to find an assortment that maxi…
A nonparametric algorithm for optimal stopping based on robust optimization
Bradley Sturt
Optimal stopping is a fundamental class of stochastic dynamic optimization problems with numerous applications in finance and operations management. We introduce a new approach for…
Dynamic optimization with side information
Dimitris Bertsimas, Christopher McCord, Bradley Sturt
We develop a tractable and flexible approach for incorporating side information into dynamic optimization under uncertainty. The proposed framework uses predictive machine learning…
Two-stage sample robust optimization
Dimitris Bertsimas, Shimrit Shtern, Bradley Sturt
We investigate a simple approximation scheme, based on overlapping linear decision rules, for solving data-driven two-stage distributionally robust optimization problems with the t…