optimization model and account for the risk of violating future operational constraints, we propose an entropic robust optimization …

sales data?In this work, we provide the first answers to the identification question by using robust optimization. We begin …

. To account for estimation errors, we propose a distributionally robust optimization (DRO) approach with the ambiguity set …

-driven robust optimization techniques. To tackle overfitting, Long et al. (2022) propose a robust satisficing model, which is …

stopping problems with known probability distributions. The approach uses simulation to construct a robust optimization problem … that approximates the stochastic optimal stopping problem to any arbitrary accuracy; we then solve the robust optimization … designing algorithms for solving the robust optimization problems that approximate the stochastic optimal stopping problems …