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

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

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

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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...

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

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

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

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

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

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

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

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

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

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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...

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

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

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

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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...

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

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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...

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