Operations Research

最新文章

• Robust Assortment Optimization Under the Markov Chain Choice Model AI翻译
  作者：Desir, Antoine; Goyal, Vineet; Jiang, Bo; Xie, Tian; Zhang, Jiawei
  作者单位：INSEAD Business School; Columbia University; Shanghai University of Finance & Economics; New York University
  摘要：Assortment optimization arises widely in many practical applications, such as retailing and online advertising. In this problem, the goal is to select a subset from a universe of substitutable products to offer customers in order to maximize the expected revenue. We study a robust assortment optimization problem under the Markov chain choice model. In this formulation, the parameters of the choice model are assumed to be uncertain, and the goal is to maximize the worst case expected revenue ov...
• Robust Optimization with Moment-Dispersion Ambiguity AI翻译
  作者：Chen, Li; Fu, Chenyi; Si, Fan; Sim, Melvyn; Xiong, Peng
  作者单位：University of Sydney; Northwestern Polytechnical University; National University of Singapore
  摘要：Robust optimization presents a compelling methodology for optimization under uncertainty, providing a practical, ambiguity-averse evaluation of risk when the probability distribution is encapsulated by an ambiguity set. We introduce the moment-dispersion ambiguity set, an improvement on the moment-based set, enabling separate characterization of a random variable's location, dispersion, and support. To describe dispersion, we define the dispersion characteristic function, capturing complex att...
• A Random Consideration Set Model for Demand Estimation, Assortment Optimization, and Pricing AI翻译
  作者：Gallego, Guillermo; Li, Anran
  作者单位：The Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong
  摘要：In this paper, we operationalize the random consideration set (RCS) choice model proposed by Manzini and Mariotti which assumes that consumers make purchase decisions based on a fixed preference ordering and random consideration sets drawn from independent attention probabilities. We provide a necessary condition, a sufficient condition, and a fast algorithm to estimate preference ordering and attention probabilities from sales transaction data, thereby uniquely identifying the model parameter...
• In Congestion Games, Taxes Achieve Optimal Approximation AI翻译
  作者：Paccagnan, Dario; Gairing, Martin
  作者单位：Imperial College London; University of Liverpool
  摘要：In this work, we address the problem of minimizing social cost in atomic congestion games. For this problem, we present lower bounds on the approximation ratio achievable in polynomial time and demonstrate that efficiently computable taxes result in polynomial time algorithms matching such bounds. Perhaps surprisingly, these results show that indirect interventions, in the form of efficiently computed taxation mechanisms, yield the same performance achievable by the best polynomial time algori...
• Conditional Distributionally Robust Functionals AI翻译
  作者：Shapiro, Alexander; Pichler, Alois
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Many decisions, in particular decisions in a managerial context, are subject to uncertainty. Risk measures cope with uncertainty by involving more than one candidate probability. The corresponding risk averse decision takes all potential candidate probabilities into account and is robust with respect to all potential probabilities. This paper considers conditional robust decision making, where decisions are subject to additional prior knowledge or information. The literature discusses various ...
• Characterizing and Computing the Set of Nash Equilibria via Vector Optimization AI翻译
  作者：Feinstein, Zachary; Rudloff, Birgit
  作者单位：Stevens Institute of Technology; Vienna University of Economics & Business
  摘要：Nash equilibria and Pareto optimality are two distinct concepts when dealing with multiple criteria. It is well known that the two concepts do not coincide. However, this work, we show that it is possible to characterize the set of all Nash equilibria for any noncooperative game as the Pareto-optimal solutions of a certain vector optimization problem. To accomplish this task, we increase the dimensionality of the objective function and formulate a nonconvex ordering cone under which Nash equil...
• Efficient Learning for Clustering and Optimizing Context-Dependent Designs AI翻译
  作者：Li, Haidong; Lam, Henry; Peng, Yijie
  作者单位：Peking University; Columbia University; Peking University
  摘要：We consider a simulation optimization problem for context-dependent decision making. Under a Gaussian mixture model-based Bayesian framework, we develop a dynamic sampling policy to maximize the worst-case probability of correctly selecting the best design over all contexts, which utilizes both global clustering information and local performance information. In particular, we design a computationally efficient approximation method to learn these sources of information, thereby leading to an im...
• Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition AI翻译
  作者：Aravena, Ignacio; Molzahn, Daniel K.; Zhang, Shixuan; Petra, Cosmin G.; Curtis, Frank E.; Tu, Shenyinying; Wachter, Andreas; Wei, Ermin; Wong, Elizabeth; Gholami, Amin; Sun, Kaizhao; Sun, Xu Andy; Elbert, Stephen T.; Holzer, Jesse T.; Veeramany, Arun
  作者单位：United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Georgia; Georgia Institute of Technology; Lehigh University; Northwestern University; University of California System; University of California San Diego; Massachusetts Institute of Technology (MIT); United States Department of Energy (DOE); Pacific Northwest National Laboratory
  摘要：The optimal power-flow problem is central to many tasks in the design and operation of electric power grids. This problem seeks the minimum-cost operating point for an electric power grid while satisfying both engineering requirements and physical laws describing how power travels through the electric network. By additionally considering the possibility of component failures and using an accurate alternating current (AC) power-flow model of the electric network, the security-constrained AC opt...
• Technical Note-Data-Driven Profit Estimation Error in the Newsvendor Model AI翻译
  作者：Siegel, Andrew F.; Wagner, Michael R.
  作者单位：University of Washington; University of Washington Seattle
  摘要：In this note, we identify a statistically significant error in naively estimating the expected profit in a data-driven newsvendor model, and we show how to correct the error. In particular, we analyze a newsvendor model where the continuous demand distribution is not known, and only a sample of demand data is available. In this context, an empirical demand distribution, that is induced by the sample of data, is used in place of the (unknown) true distribution. The quantity at the critical perc...
• Optimal Leveraged Portfolio Selection Under Quasi-Elastic Market Impact AI翻译
  作者：Edirisinghe, Chanaka; Chen, Jingnan; Jeong, Jaehwan
  作者单位：Rensselaer Polytechnic Institute; Beihang University; Radford University
  摘要：We study optimal portfolio choice under leveraging to improve portfolio performance when trade execution faces market impact. We consider a quasi-elastic market with continuous trading in which temporary liquidity costs are sufficiently large relative to permanent impact. The resulting convex optimization model is used to show analytically that an unlevered portfolio maximizing the Sharpe ratio is no longer a tangency portfolio, and increasing the portfolio target mean leads to severely underm...