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作者：Sahoo, Roshni; Wager, Stefan
作者单位：Stanford University; Stanford University
摘要：Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to such policies, competition arises, complicating estimation of the optimal policy. In this paper, we study capacity-constrained treatment assignments in the presence of such interference. We consider a dynamic model in which the decision maker allocates treatments at each time step and heterogeneous agents myopically b...
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作者：Namkoong, Hongseok; Ma, Yuanzhe; Glynn, Peter W.
作者单位：Columbia University; Columbia University; Stanford University
摘要：The performance of decision policies and prediction models often deteriorates when applied to environments different from the ones seen during training. To ensure reliable operation, we analyze the stability of a system under distribution shift, which is defined as the smallest change in the underlying environment that causes the system's performance to deteriorate beyond a permissible threshold. In contrast to standard tail risk measures and distributionally robust losses that require the spe...
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作者：Yang, Yu
作者单位：State University System of Florida; University of Florida
摘要：In this paper, we propose an innovative variable fixing strategy called deep Lagrangian underestimate fi xing (DeLuxing). It is a highly effective approach for removing unnecessary variables in column-generation (CG)-based exact methods used to solve challenging discrete optimization problems commonly encountered in various industries, including vehicle routing problems (VRPs). DeLuxing employs a novel linear programming (LP) formulation with only a small subset of the enumerated variables, wh...
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作者：Zhang, Xun; Ye, Zhi-Sheng; Haskell, William B.
作者单位：Southern University of Science & Technology; National University of Singapore; Purdue University System; Purdue University
摘要：We study periodic review stochastic inventory control in the data-driven setting where the retailer makes ordering decisions based only on historical demand observations without any knowledge of the probability distribution of the demand. Because an (s, S)policy is optimal when the demand distribution is known, we investigate the statistical properties of the data-driven (s, S)-policy obtained by recursively computing the empirical cost-to-go functions. This policy is inherently challenging to...
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作者：Royset, Johannes O.; Lejeune, Miguel A.
作者单位：University of Southern California; George Washington University
摘要：For parameterized mixed-binary optimization problems, we construct local decision rules that prescribe near-optimal courses of action across a set of parameter values. The decision rules stem from solving risk-adaptive training problems over classes of continuous, possibly nonlinear mappings. In asymptotic and nonasymptotic analysis, we establish that the decision rules prescribe near-optimal decisions locally for the actual problems without relying on linearity, convexity, or smoothness. The ...
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作者：Lejeune, Miguel A.; Ma, Wenbo
作者单位：George Washington University
摘要：We propose a new stochastic emergency network design model that uses a fleet of drones to quickly deliver naloxone in response to opioid overdoses. The network is represented as a collection of M/G/K / G / K queueing systems in which the capacity K of each system is a decision variable, and the service time is modeled as a decision -dependent random variable. The model is a queuing -based optimization problem which locates fixed (drone bases) and mobile (drones) servers and determines the dron...
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作者：Chen, Li; Sim, Melvyn
作者单位：University of Sydney; National University of Singapore
摘要：We propose robust optimization models and their tractable approximations that cater for ambiguity -averse decision makers whose underlying risk preferences are consistent with constant absolute risk aversion (CARA). Specifically, we focus on maximizing the worst -case expected exponential utility where the underlying uncertainty is generated from a set of stochastically independent factors with ambiguous marginals. To obtain computationally tractable formulations, we propose a hierarchy of app...
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作者：Yang, Shuoguang; Fang, Ethan X.; Shanbhag, Uday V.
作者单位：Hong Kong University of Science & Technology; Duke University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：As systems grow in size, scale, and intricacy, the challenges of misspecification become even more pronounced. In this paper, we focus on parametric misspecification in regimes complicated by risk and nonconvexity. When this misspecification may be resolved via a parallel learning process, we develop data -driven schemes for resolving a broad class of misspecified stochastic compositional optimization problems. Notably, this rather broad class of compositional problems can contend with challen...
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作者：Atkinson, Michael; Kress, Moshe
作者单位：United States Department of Defense; United States Navy; Naval Postgraduate School
摘要：The increasing prevalence of missiles and drones (hereafter referred to as threats) in attacks by both state and nonstate actors highlights the critical need for a robust defense system to counter these threats. We develop a combat model for the engagement between a Blue defender who is subject to repeated attacks by Red threats. The defender employs two types of defenses: hard interceptors, such as antiballistic missiles, and soft measures, such as directedenergy weapons and jamming. Employin...
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作者：Shen, Haoming; Jiang, Ruiwei
作者单位：University of Arkansas System; University of Arkansas Fayetteville; University of Michigan System; University of Michigan
摘要：Chance constraints yield nonconvex feasible regions in general. In particular, when the uncertain parameters are modeled by a Wasserstein ball, existing studies showed that the distributionally robust (pessimistic) chance constraint admits a mixed-integer conic representation. This paper identifies sufficient conditions that lead to convex feasible regions of chance constraints with Wasserstein ambiguity. First, when uncertainty arises from the right-hand side of a pessimistic joint chance con...
