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最新文章

• A Token-Based Central Queue with Order-Independent Service Rates AI翻译
  作者：Ayesta, Urtzi; Bodas, Tejas; Dorsman, Jan-Pieter L.; Verloop, Ina Maria
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Universite Federale Toulouse Midi-Pyrenees (ComUE); Institut National Polytechnique de Toulouse; Basque Foundation for Science; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; Institut National Polytechnique de Toulouse; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Physics (INP); University of Basque Country; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Dharwad; University of Amsterdam
  摘要：We study a token-based central queue with multiple customer types. Customers of each type arrive according to a Poisson process and have an associated set of compatible tokens. Customers may only receive service when they have claimed a compatible token. If, upon arrival, more than one compatible token is available, then an assignment rule determines which token will be claimed. The service rate obtained by a customer is state-dependent, that is, it depends on the set of claimed tokens and on ...
• Technical Note-Two-Stage Sample Robust Optimization AI翻译
  作者：Bertsimas, Dimitris; Shtern, Shimrit; Sturt, Bradley
  作者单位：Massachusetts Institute of Technology (MIT); Technion Israel Institute of Technology; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：We investigate a simple approximation scheme, based on overlapping linear decision rules, for solving data-driven two-stage distributionally robust optimization problems with the type-infinity Wasserstein ambiguity set. Our main result establishes that this approximation scheme is asymptotically optimal for two-stage stochastic linear optimization problems; that is, under mild assumptions, the optimal cost and optimal first-stage decisions obtained by approximating the robust optimization prob...
• Learning to Approximate Industrial Problems by Operations Research Classic Problems AI翻译
  作者：Parmentier, Axel
  作者单位：Institut Polytechnique de Paris; Ecole des Ponts ParisTech
  摘要：Practitioners of operations research often consider difficult variants of well-known optimization problems and struggle to find a good algorithm for their variants although decades of research have produced highly efficient algorithms for the well-known problems. We introduce a machine learning for operations research paradigm to build efficient heuristics for such variants: use a machine learning predictor to turn an instance of the variant into an instance of the well-known problem, then sol...
• Time-Varying Risk Aversion and Dynamic Portfolio Allocation AI翻译
  作者：Li, Haitao; Wu, Chongfeng; Zhou, Chunyang
  作者单位：Shanghai Jiao Tong University
  摘要：We study the implications of time-varying risk aversion for dynamic portfolio allocation under the framework of regime-switching models. In our model, both asset returns and investor risk aversion are regime dependent: In a bull regime, asset return is high, volatility is low, and risk aversion is low, and the opposite happens in a bear regime. We develop an efficient dynamic programming algorithm that overcomes the challenges imposed by regime-dependent preference in obtaining time-consistent...
• Mechanism Design for Correlated Valuations: Efficient Methods for Revenue Maximization AI翻译
  作者：Albert, Michael; Conitzer, Vincent; Lopomo, Giuseppe; Stone, Peter
  作者单位：University of Virginia; Duke University; Duke University; University of Texas System; University of Texas Austin
  摘要：Traditionally, the mechanism design literature has been primarily focused on settings where the bidders' valuations are independent. However, in settings where valuations are correlated, much stronger results are possible. For example, the entire surplus of efficient allocations can be extracted as revenue. These stronger results are true, in theory, under generic conditions on parameter values. However, in practice, they are rarely, if ever, implementable because of the stringent requirement ...
• A Restless Bandit Model for Resource Allocation, Competition, and Reservation AI翻译
  作者：Fu, Jing; Moran, Bill; Taylor, Peter G.
  作者单位：University of Melbourne; University of Melbourne
  摘要：We study a resource allocation problem with varying requests and with resources of limited capacity shared by multiple requests. It is modeled as a set of heterogeneous restless multiarmed bandit problems (RMABPs) connected by constraints imposed by resource capacity. Following Whittle's relaxation idea and Weber and Weiss' asymptotic optimality proof, we propose a simple policy and prove it to be asymptotically optimal in a regime where both arrival rates and capacities increase. We provide a...
• Network Pricing: How to Induce Optimal Flows Under Strategic Link AI翻译
  作者：Correa, Jose; Guzman, Cristobal; Lianeas, Thanasis; Nikolova, Evdokia; Schroder, Marc
  作者单位：Universidad de Chile; Pontificia Universidad Catolica de Chile; Pontificia Universidad Catolica de Chile; National Technical University of Athens; University of Texas System; University of Texas Austin; Maastricht University
  摘要：Network pricing games provide a framework for modeling real-world settings with two types of strategic agents: operators of a network and users of the network. Operators of the network post a price so as to attract users and maximize profit; users of the network select routes based on these prices and congestion from other users. Motivated by the fact that equilibrium in these games may not exist, may not be unique, and may induce an inefficient network performance, our main result is to obser...
• Scheduling to Differentiate Service in a Multiclass Service System AI翻译
  作者：Liu, Yunan; Sun, Xu; Hovey, Kyle
  作者单位：North Carolina State University; State University System of Florida; University of Florida; United States Department of Defense
  摘要：Motivated by large-scale service systems, we study a multiclass queueing system having class-dependent service rates and heterogeneous abandonment distributions. Our objective is to devise proper staffing and scheduling schemes to achieve differentiated services for each class. Formally, for a class-specific delay target w(i) > 0 and threshold alpha(i) is an element of (0,1), we concurrently determine an appropriate staffing level (number of servers) and a server-assignment rule (assigning new...
• Data-Driven Optimization: A Reproducing Kernel Hilbert Space Approach AI翻译
  作者：Bertsimas, Dimitris; Kodur, Nihal
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
  摘要：We present two methods, based on regression in reproducing kernel Hilbert spaces, for solving an optimization problem with uncertain parameters for which we have historical data, including auxiliary data. The first method approximates the objective function and the second approximates the optimizer. We provide finite sample guarantees and prove asymptotic optimality for both methods. Computational experiments suggest that at least the second method overcomes a curse of dimensionality that affl...
• Long-Term Behavior of Dynamic Equilibria in Fluid Networks AI翻译
  作者：Cominetti, Roberto; Correa, Jose; Olver, Neil
  作者单位：Universidad Adolfo Ibanez; Universidad de Chile; University of London; London School Economics & Political Science
  摘要：A fluid queuing network constitutes one of the simplest models in which to study flow dynamics over a network. In this model we have a single source-sink pair, and each link has a per-time-unit capacity and a transit time. A dynamic equilibrium (or equilibrium flow over time) is a flow pattern over time such that no flow particle has incentives to unilaterally change its path. Although the model has been around for almost 50 years, only recently results regarding existence and characterization...