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作者：Manou, Athanasia; Economou, Antonis; Karaesmen, Fikri
作者单位：National & Kapodistrian University of Athens; Koc University
摘要：We consider a transportation station, where customers arrive according to a Poisson process. A transportation facility visits the station according to a renewal process and serves at each visit a random number of customers according to its capacity. We assume that the arriving customers decide whether to join the station or balk, based on a natural reward-cost structure. We study the strategic behavior of the customers and determine their symmetric Nash equilibrium strategies under two levels ...
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作者：Brown, David B.; Smith, James E.
作者单位：Duke University
摘要：We consider the information relaxation approach for calculating performance bounds for stochastic dynamic programs (DPs). This approach generates performance bounds by solving problems with relaxed nonanticipativity constraints and a penalty that punishes violations of these nonanticipativity constraints. In this paper, we study DPs that have a convex structure and consider gradient penalties that are based on first-order linear approximations of approximate value functions. When used with per...
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作者：Arlotto, Alessandro; Gans, Noah; Steele, J. Michael
作者单位：Duke University; University of Pennsylvania; University of Pennsylvania
摘要：We identify a rich class of finite-horizon Markov decision problems (MDPs) for which the variance of the optimal total reward can be bounded by a simple linear function of its expected value. The class is characterized by three natural properties: reward nonnegativity and boundedness, existence of a do-nothing action, and optimal action monotonicity. These properties are commonly present and typically easy to check. Implications of the class properties and of the variance bound are illustrated...
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作者：Cho, Soo-Haeng; Jang, Hoon; Lee, Taesik; Turner, John
作者单位：Carnegie Mellon University; Korea Advanced Institute of Science & Technology (KAIST); University of California System; University of California Irvine
摘要：This paper studies the problem of simultaneously locating trauma centers and helicopters. The standard approach to locating helicopters involves the use of helicopter busy fractions to model the random availability of helicopters. However, busy fractions cannot be estimated a priori in our problem because the demand for each helicopter cannot be determined until the trauma center locations are selected. To overcome this challenge, we endogenize the computation of busy fractions within an optim...
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作者：Davis, James M.; Gallego, Guillermo; Topaloglu, Huseyin
作者单位：Cornell University; Columbia University
摘要：We study a class of assortment optimization problems where customers choose among the offered products according to the nested logit model. There is a fixed revenue associated with each product. The objective is to find an assortment of products to offer so as to maximize the expected revenue per customer. We show that the problem is polynomially solvable when the nest dissimilarity parameters of the choice model are less than one and the customers always make a purchase within the selected ne...
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作者：Cote, Jean-Francois; Gendreau, Michel; Potvin, Jean-Yves
作者单位：Laval University; Universite de Montreal; Laval University; Universite de Montreal; Polytechnique Montreal; Universite de Montreal; Universite de Montreal
摘要：This paper describes an exact algorithm for solving a two-dimensional orthogonal packing problem with unloading constraints, which occurs as a subproblem of mixed vehicle routing and loading problems. The packing considered in this work is basically a feasibility problem involving a single bin. The problem is addressed through a decomposition approach wherein a branch-and-cut algorithm is designed for solving a one-dimensional relaxation of the original problem. When an integer solution is fou...
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作者：Keskin, N. Bora; Zeevi, Assaf
作者单位：University of Chicago; Columbia University
摘要：We consider a monopolist who sells a set of products over a time horizon of T periods. The seller initially does not know the parameters of the products' linear demand curve, but can estimate them based on demand observations. We first assume that the seller knows nothing about the parameters of the demand curve, and then consider the case where the seller knows the expected demand under an incumbent price. It is shown that the smallest achievable revenue loss in T periods, relative to a clair...
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作者：Kao, Yi-Hao; Van Roy, Benjamin
作者单位：Stanford University
摘要：We consider a problem involving estimation of a high-dimensional covariance matrix that is the sum of a diagonal matrix and a low-rank matrix, and making a decision based on the resulting estimate. Such problems arise, for example, in portfolio management, where a common approach employs principal component analysis (PCA) to estimate factors used in constructing the low-rank term of the covariance matrix. The decision problem is typically treated separately, with the estimated covariance matri...
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作者：Cavus, Ozlem; Ruszczynski, Andrzej
作者单位：Ihsan Dogramaci Bilkent University; Rutgers University System; Rutgers University New Brunswick
摘要：The total cost problem for discrete-time controlled transient Markov models is considered. The objective functional is a Markov dynamic risk measure of the total cost. Two solution methods, value and policy iteration, are proposed, and their convergence is analyzed. In the policy iteration method, we propose two algorithms for policy evaluation: the nonsmooth Newton method and convex programming, and we prove their convergence. The results are illustrated on a credit limit control problem.
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作者：Natarajan, Karthik; Shi, Dongjian; Toh, Kim-Chuan
作者单位：Singapore University of Technology & Design; National University of Singapore
摘要：In this paper, we propose a new probabilistic model for minimizing the anticipated regret in combinatorial optimization problems with distributional uncertainty in the objective coefficients. The interval uncertainty representation of data is supplemented with information on the marginal distributions. As a decision criterion, we minimize the worst-case conditional value at risk of regret. The proposed model includes the interval data minmax regret model as a special case. For the class of com...
