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作者：Bertsimas, Dimitris; O'Hair, Allison
作者单位：Massachusetts Institute of Technology (MIT)
摘要：Preference learning has been a topic of research in many fields, including operations research, marketing, machine learning, and behavioral economics. In this work, we strive to combine the ideas from these different fields into a single methodology to learn preferences and make decisions. We use robust and integer optimization in an adaptive and dynamic way to determine preferences from data that are consistent with human behavior. We use integer optimization to address human inconsistency, r...
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作者：Xie, Jing; Frazier, Peter I.
作者单位：Cornell University
摘要：We consider the problem of efficiently allocating simulation effort to determine which of several simulated systems have mean performance exceeding a threshold of known value. Within a Bayesian formulation of this problem, the optimal fully sequential policy for allocating simulation effort is the solution to a dynamic program. When sampling is limited by probabilistic termination or sampling costs, we show that this dynamic program can be solved efficiently, providing a tractable way to compu...
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作者：Chen, Xin; Hu, Peng; He, Simai
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Huazhong University of Science & Technology; City University of Hong Kong
摘要：This paper establishes a new preservation property of supermodularity in a class of two-dimensional parametric optimization problems, where the constraint sets may not be lattices. This property and its extensions unify several results in the literature and provide powerful tools to analyze a variety of operations models including a two-product coordinated pricing and inventory control problem with cross-price effects that we use as an illustrative example.
