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作者：Shahmizad, Maral; Buchanan, Austin
作者单位：Oklahoma State University System; Oklahoma State University - Stillwater
摘要：When partitioning a state into political districts, a common criterion is that political subdivisions, like counties, should not be split across multiple districts. This criterion is encoded into most state constitutions and is sometimes enforced quite strictly by the courts. However, map drawers, courts, and the public typically do not know what amount of splitting is truly necessary, even to satisfy basic criteria, like contiguity and population balance. In this paper, we provide answers for...
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作者：Blanchet, Jose; Lam, Henry; Liu, Yang; Wang, Ruodu
作者单位：Stanford University; Columbia University; The Chinese University of Hong Kong, Shenzhen; University of Waterloo
摘要：Quantile aggregation with dependence uncertainty has a long history in probability theory, with wide applications in finance, risk management, statistics, and operations research. Using a recent result on inf-convolution of quantile-based risk measures, we establish new analytical bounds for quantile aggregation, which we call convolution bounds. Convolution bounds both unify every analytical result available in quantile aggregation and enlighten our understanding of these methods. These bound...
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作者：Xia, Jun; Xu, Zhou; Baldacci, Roberto
作者单位：Shanghai Jiao Tong University; Hong Kong Polytechnic University; Qatar Foundation (QF); Hamad Bin Khalifa University-Qatar
摘要：The liner shipping network design (LSND) problem involves creating regular ship rotations to transport containerized cargo between seaports. The objective is to maximize carrier profit by balancing revenue from satisfied demand against operating and transshipment costs. Finding an optimal solution is challenging because of complex rotation structures and joint decisions on fleet deployment, cargo routing, and rotation design. This work introduces a set partitioning-like formulation for LSND wi...
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作者：Jiang, Jiashuo; Ma, Will; Zhang, Jiawei
作者单位：Hong Kong University of Science & Technology; Columbia University; New York University
摘要：We study the classic network revenue management (NRM) problem with accept/ reject decisions and T independent and identically distributed arrivals. We consider a distributional form in which each arrival must fall under a finite number of possible categories, each with a deterministic resource consumption vector, but a random value distributed continuously over an interval. We develop an online algorithm that achieves O(log2 T) regret under this model with the only (necessary) assumption being...
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作者：Pesenti, Silvana M.; Jaimungal, Sebastian; Saporito, Yuri F.; Targino, Rodrigo S.
作者单位：University of Toronto; University of Oxford; Getulio Vargas Foundation
摘要：We define and develop an approach for risk budgeting allocation-a risk diversification portfolio strategy-where risk is measured using a dynamic time-consistent risk measure. For this, we introduce a notion of dynamic risk contributions that generalize the classical Euler contributions, which allows us to obtain dynamic risk contributions in a recursive manner. We prove that for the class of coherent dynamic distortion risk measures, the risk allocation problem may be recast as a sequence of s...
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作者：Aveklouris, Angelos; DeValve, Levi; Stock, Maximiliano; Ward, Amy
作者单位：University of Chicago
摘要：Service platforms must determine rules for matching heterogeneous demand (customers) and supply (workers) that arrive randomly over time and may be lost if forced to wait too long for a match. Our objective is to maximize the cumulative value of matches, minus costs incurred when demand and supply wait. We develop a fluid model, that approximates the evolution of the stochastic model and captures explicitly the nonlinear dependence between the amount of demand and supply waiting and the distri...
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作者：Cai, Jun; Li, Jonathan Yu-Meng; Mao, Tiantian
作者单位：University of Waterloo; University of Ottawa; Chinese Academy of Sciences; University of Science & Technology of China, CAS
摘要：Distributionally robust optimization (DRO) has arisen as an important paradigm for addressing the issue of distributional ambiguity in decision optimization. In the case in which a decision maker is not risk neutral, the most common scheme applied in DRO for capturing the risk attitude is to employ an expected utility functional. In this paper, we propose to address a decision maker's risk attitude in DRO by following an alternative scheme known as dual expected utility. In this scheme, a dist...
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作者：Xu, Guanglin; Hanasusanto, Grani A.
作者单位：University of North Carolina; University of North Carolina Charlotte; University of Minnesota System; University of Minnesota Twin Cities; University of Illinois System; University of Illinois Urbana-Champaign
摘要：We study decision rule approximations for generic multistage robust linear optimization problems. We examine linear decision rules for the case when the objective coefficients, the recourse matrices, and the right-hand sides are uncertain, and we explore quadratic decision rules for the case when only the right-hand sides are uncertain. The resulting optimization problems are NP hard but amenable to copositive programming reformulations that give rise to tight, tractable semidefinite programmi...
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作者：Besbes, Omar; Fonseca, Yuri; Lobel, Ilan
作者单位：Columbia University; New York University
摘要：We study the problems of offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after the fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle. In the offline setting, the decision maker has information available from past periods and needs to make one decisio...
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作者：Salem, Tad; Gupta, Swati; Kamble, Vijay
作者单位：United States Department of Defense; United States Navy; United States Naval Academy; Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：Algorithmic decision making in societal contexts, such as retail pricing, loan administration, recommendations on online platforms, etc., can be framed as stochastic optimization under bandit feedback, which typically requires experimentation with different decisions for the sake of learning. Such experimentation often results in perceptions of unfairness among people impacted by these decisions; for instance, there have been several recent lawsuits accusing companies that deploy algorithmic p...
