Vehicle routing problems (VRPs) with deadlines have received significant attention around the world. Motivated by a real-world food delivery problem, we assu...

Recently there has been a surge of interest in operations research~(OR) and the machine learning~(ML) community in combining prediction algorithms and optimi...

Optimizing static risk-averse objectives in Markov decision processes is challenging because they do not readily admit dynamic programming decompositions. Pr...

In order to improve the efficiency of the last-mile delivery system when customers are possibly absent for deliveries, we propose the idea of employing the c...

In this paper, we study a novel approach for data-driven decision-making under uncertainty in the presence of contextual information. Specifically, we addres...

This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the adverti...

Recently equal risk pricing, a framework for fair derivative pricing, was extended to consider dynamic risk measures. However, all current implementations ei...

We study a predisaster relief network design problem with uncertain demands. The aim is to determine the prepositioning and reallocation of relief supplies. ...

The problem of portfolio management represents an important and challenging class of dynamic decision making problems, where rebalancing decisions need to be...

Optimization with stochastic dominance constraints has recently received an increasing amount of attention in the quantitative risk management literature. In...

Within the context of optimization under uncertainty, a well-known alternative to minimizing expected value or the worst-case scenario consists in minimizing...

In this paper, we study a distributionally robust multi-item newsvendor problem, where the demand distribution is unknown but specified with a general event-...

Battery charging of electric vehicles (EVs) needs to be properly coordinated by electricity producers to maintain the network reliability. In this paper, we ...

Optimal stopping is the problem of deciding the right time at which to take a particular action in a stochastic system, in order to maximize an expected rewa...

Generation expansion planning (GEP) is a classical problem that determines an optimal investment plan for existing and future electricity generation technolo...

Conditional estimation given specific covariate values (i.e., local conditional estimation or functional estimation) is ubiquitously useful with applications...

We study a distributionally robust version of the classical capacitated facility location problem with a distributional ambiguity set defined as a Wasserst...

In this paper, we consider the problem of equal risk pricing and hedging in which the fair price of an option is the price that exposes both sides of the con...

Utility-based shortfall risk measure (SR) effectively captures decision maker’s risk attitude on tail losses by an increasing convex loss function. In this ...

Conditional Value at Risk (CVaR) is widely used to account for the preferences of a risk-averse agent in the extreme loss scenarios. To study the effectiven...