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最新文章

• Central Limit Theorems for Aggregate Efficiency AI翻译
  作者：Simar, Leopold; Zelenyuk, Valentin
  作者单位：Universite Catholique Louvain; University of Queensland; University of Queensland
  摘要：Applied researchers in the field of efficiency and productivity analysis often need to estimate and make inference about aggregate efficiency, such as industry efficiency or aggregate efficiency of a group of distinct firms within an industry (e.g., public versus private firms, regulated versus unregulated firms, etc.). While there are approaches to obtain point estimates for such important measures, no asymptotic theory has been derived for it. This is the gap in the literature we fill with t...
• Effects of System Parameters on the Optimal Cost and Policy in a Class of Multidimensional Queueing Control Problems AI翻译
  作者：Vercraene, Samuel; Gayon, Jean-Philippe; Karaesmen, Fikri
  作者单位：Institut National des Sciences Appliquees de Lyon - INSA Lyon; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS)
  摘要：We consider a class of Markov Decision Processes frequently employed to model queueing and inventory control problems. For these problems, we explore how changes in different system input parameters (transition rates, costs, discount rates etc.) affect the optimal cost and the optimal policy when the state space of the problem is multidimensional. To address a large class of problems, we introduce two generic dynamic programming operators to model different types of controlled events. For thes...
• An Equilibrium Analysis of a Multiclass Queue with Endogenous Abandonments in Heavy Traffic AI翻译
  作者：Ata, Baris; Peng, Xiaoshan
  作者单位：University of Chicago
  摘要：This paper studies a multiclass queueing system with endogenous abandonments where congestion affects customers' abandonment behavior, and vice versa. Our model captures this interaction by developing two closely related models: an abandonment model and a queueing model. In the abandonment model, customers take the virtual waiting time distribution as given. Class k customers receive a reward r(k) from service and incur a cost c(k) per period of waiting. Customers are forward looking and make ...
• Robust Adaptive Routing Under Uncertainty AI翻译
  作者：Flajolet, Arthur; Blandin, Sebastien; Jaillet, Patrick
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
  摘要：We consider the problem of finding an optimal history-dependent routing strategy on a directed graph weighted by stochastic arc costs when the objective is to minimize the risk of spending more than a prescribed budget. To help mitigate the impact of the lack of information on the arc cost probability distributions, we introduce a robust counterpart where the distributions are only known through confidence intervals on some statistics such as the mean, the mean absolute deviation, and any quan...
• Learning to Optimize via Information-Directed Sampling AI翻译
  作者：Russo, Daniel; Van Roy, Benjamin
  作者单位：Columbia University; Stanford University
  摘要：We propose information-directed sampling-a new approach to online optimization problems in which a decision maker must balance between exploration and exploitation while learning from partial feedback. Each action is sampled in a manner that minimizes the ratio between squared expected single-period regret and a measure of information gain: the mutual information between the optimal action and the next observation. We establish an expected regret bound for information-directed sampling that ap...
• The First Approximation Algorithm for the Maximin Latin Hypercube Design Problem AI翻译
  作者：Le Guiban, Kaourintin; Rimmel, Arpad; Weisser, Marc-Antoine; Tomasik, Joanna
  作者单位：Universite Paris Saclay
  摘要：In metamodeling, the choice of sampling points is crucial for the quality of the model. In this context, the maximin Latin hypercube designs (LHD), with their space-filling and noncollapsing properties, are particularly efficient. To this day, there is no polynomial time algorithm that produces optimal maximin LHDs, i.e., in which the minimum distance between two points (the separation distance) is maximal. We are interested in LHDs with a separation distance as large as possible. The algorith...
• Nonparametric Production Technologies with Multiple Component Processes AI翻译
  作者：Podinovski, Victor V.; Olesen, Ole Bent; Sarrico, Claudia S.
  作者单位：Loughborough University; University of Southern Denmark; Universidade de Lisboa
  摘要：We develop a nonparametric methodology for assessing the efficiency of decision-making units operating in a production technology with several component processes. The latter is modeled by the new multiple hybrid returns-to-scale (MHRS) technology, formally derived from an explicitly stated set of production axioms. In contrast with the existing models of data envelopment analysis (DEA), the MHRS technology allows the incorporation of component-specific and shared inputs and outputs that repre...
• A Flocking-Based Approach for Distributed Stochastic Optimization AI翻译
  作者：Pu, Shi; Garcia, Alfredo
  作者单位：Arizona State University; Arizona State University-Tempe; Texas A&M University System; Texas A&M University College Station
  摘要：In recent years, the paradigm of cloud computing has emerged as an architecture for computing that makes use of distributed (networked) computing resources. In this paper, we consider a distributed computing algorithmic scheme for stochastic optimization, which relies on modest communication requirements among processors and most importantly, does not require synchronization. Specifically, we analyze a scheme with N > 1 independent threads each implementing a stochastic gradient algorithm. The...