This paper studies the optimal insurance problem within the risk minimization framework and from a decision maker (DM)’s perspective. We assume that the DM is uncertain about the underlying distribution of her loss and would consider all the distributions that closely surround a given...

We consider a general multi-period repositioning problem in vehicle-sharing networks such as bicycle, scooter, and car-sharing systems. This problem involves uncertainties along multiple dimensions, including demand, travel time, and repositioning duration. It is also subject to various...

The ranking-based choice model is a popular model in revenue management for predicting demand for a firm’s products based on the assortment of products that the firm offers to their customers. Because this model has a huge number of parameters, many different ranking-based choice models can be...

In this paper, we study the feature-based newsvendor problem in the presence of historical demand and related demand covariates. We adopt general kernel methods to estimate the true demand distributions conditioned on the given demand features. To account for estimation errors, we propose a...

In practical optimization problems, we typically model uncertainty as a random variable though its true probability distribution is unobservable to the decision maker. Historical data provides some information of this distribution that we can use to approximately quantify the risk of an...

We propose a new prescriptive analytics model based on robust satisficing that incorporates a prediction model to determine the here-and-now decision that would achieve a target expected reward as well as possible under both risk ambiguity and estimation uncertainty. The reward function of the...

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 solving computationally-demanding stochastic optimal stopping problems with known probability distributions. The...