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最新文章

• A unifying Framework for Farrell Profit Efficiency Measurement AI翻译
  作者：Fare, Rolf; He, Xinju; Li, Sungko; Zelenyuk, Valentin
  作者单位：Oregon State University; Hong Kong Baptist University; University of Queensland; University of Queensland
  摘要：Measuring profit efficiency is a challenging task, and many different approaches have been suggested. This paper synthesizes existing approaches and develops a general Farrell-type approach of the profit efficiency measurement. Our derivations unveil new and useful relationships between existing measures and the proposed new Farrell-type measures. In addition, this helps us establish a generalized and unifying framework for studying efficiency behavior of firms, where the profit efficiency mea...
• Bayesian Exploration for Approximate Dynamic Programming AI翻译
  作者：Ryzhov, Ilya O.; Mes, Martijn R. K.; Powell, Warren B.; van den Berg, Gerald
  作者单位：University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; University of Twente; Princeton University
  摘要：Approximate dynamic programming (ADP) is a general methodological framework for multistage stochastic optimization problems in transportation, finance, energy, and other domains. We propose a new approach to the exploration/exploitation dilemma in ADP that leverages two important concepts from the optimal learning literature: first, we show how a Bayesian belief structure can be used to express uncertainty about the value function in ADP; second, we develop a new exploration strategy based on ...
• Good Clusterings Have Large Volume AI翻译
  作者：Borgwardt, Steffen; Happach, Felix
  作者单位：University of Colorado System; University of Colorado Denver; Technical University of Munich; Technical University of Munich
  摘要：The clustering of a data set is one of the core tasks in data analytics. Many clustering algorithms exhibit a strong contrast between a favorable performance in practice and bad theoretical worst cases. Prime examples are least-squares assignments and the popular k-means algorithm. We are interested in this contrast and study it through polyhedral theory. Several popular clustering algorithms can be connected to finding a vertex of the so-called bounded-shape partition polytopes. The vertices ...
• Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees AI翻译
  作者：Ghosh, Soumyadip; Lam, Henry
  作者单位：International Business Machines (IBM); IBM USA; Columbia University
  摘要：Any performance analysis based on stochastic simulation is subject to the errors inherent in misspecifying the modeling assumptions, particularly the input distributions. In situations with little support from data, we investigate the use of worst-case analysis to analyze these errors, by representing the partial, nonparametric knowledge of the input models via optimization constraints. We study the performance and robustness guarantees of this approach. We design and analyze a numerical schem...
• Gaussian Markov Random Fields for Discrete Optimization via Simulation: Framework and Algorithms AI翻译
  作者：Salemi, Peter L.; Song, Eunhye; Nelson, Barry L.; Staum, Jeremy
  作者单位：MITRE Corporation; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Northwestern University
  摘要：We consider optimizing the expected value of some performance measure of a dynamic stochastic simulation with a statistical guarantee for optimality when the decision variables are discrete, in particular, integer-ordered; the number of feasible solutions is large; and the model execution is too slow to simulate even a substantial fraction of them. Our goal is to create algorithms that stop searching when they can provide inference about the remaining optimality gap similar to the correct-sele...
• Periodic Little's Law AI翻译
  作者：Whitt, Ward; Zhang, Xiaopei
  作者单位：Columbia University
  摘要：Motivated by our recent study of patient flow data from an Israeli emergency department (ED), we establish a sample path periodic Little's law (PLL), which extends the sample path Little's law (LL). The ED data analysis led us to propose a periodic stochastic process to represent the aggregate ED occupancy level, with the length of a periodic cycle being 1 week. Because we conducted the ED data analysis over successive hours, we construct our PLL in discrete time. The PLL helps explain the rem...
• Load Balancing in the Nondegenerate Slowdown Regime AI翻译
  作者：Gupta, Varun; Walton, Neil
  作者单位：University of Chicago; University of Manchester
  摘要：We analyze join-the-shortest-queue (JSQ) in a contemporary scaling regime known as the nondegenerate slowdown (NDS) regime. Join-the-shortest-queue is a classical load-balancing policy for queueing systems with multiple parallel servers. Parallel server queueing systems are regularly analyzed and dimensioned by diffusion approximations achieved in the Halfin-Whitt scaling regime. However, when jobs must be dispatched to a server upon arrival, we advocate the nondegenerate slowdown regime to co...
• The Big Data Newsvendor: Practical Insights from Machine Learning AI翻译
  作者：Ban, Gah-Yi; Rudin, Cynthia
  作者单位：University of London; London Business School; Duke University
  摘要：We investigate the data-driven newsvendor problem when one has n observations of p features related to the demand as well as historical demand data. Rather than a two-step process of first estimating a demand distribution then optimizing for the optimal order quantity, we propose solving the big data newsvendor problem via singlestep machine-learning algorithms. Specifically, we propose algorithms based on the empirical risk minimization (ERM) principle, with and without regularization, and an...