5期

最新文章

• Non-Stationary Stochastic Optimization AI翻译
  作者：Besbes, Omar; Gur, Yonatan; Zeevi, Assaf
  作者单位：Columbia University; Stanford University
  摘要：We consider a non-stationary variant of a sequential stochastic optimization problem, in which the underlying cost functions may change along the horizon. We propose a measure, termed variation budget, that controls the extent of said change, and study how restrictions on this budget impact achievable performance. We identify sharp conditions under which it is possible to achieve long-run average optimality and more refined performance measures such as rate optimality that fully characterize t...
• Automated Design of Revenue-Maximizing Combinatorial Auctions AI翻译
  作者：Sandholm, Tuomas; Likhodedov, Anton
  作者单位：Carnegie Mellon University; Deutsche Bank
  摘要：Designing optimal-that is, revenue-maximizing-combinatorial auctions (CAs) is an important elusive problem. It is unsolved even for two bidders and two items for sale. Rather than pursuing the manual approach of attempting to characterize the optimal CA, we introduce a family of CAs and then seek a high-revenue auction within that family. The family is based on bidder weighting and allocation boosting; we coin such CAs virtual valuations combinatorial auctions (VVCAs). VVCAs are the Vickrey-Cl...
• Optimal Sparse Designs for Process Flexibility via Probabilistic Expanders AI翻译
  作者：Chen, Xi; Zhang, Jiawei; Zhou, Yuan
  作者单位：New York University; New York University; New York University; NYU Shanghai; Massachusetts Institute of Technology (MIT)
  摘要：We study the problem of how to design a sparse flexible process structure in a balanced and symmetrical production system to match supply with random demand more effectively. Our goal is to provide a sparsest design to achieve (1 - epsilon)-optimality relative to the fully flexible system. In a balanced system with n plants and n products, Chou et al. (2011) proved that there exists a graph expander with Omicron(n/epsilon) arcs to achieve (1 - epsilon)-optimality for every demand realization. ...
• Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support AI翻译
  作者：Helm, Jonathan E.; Lavieri, Mariel S.; Van Oyen, Mark P.; Stein, Joshua D.; Musch, David C.
  作者单位：Indiana University System; Indiana University Bloomington; IU Kelley School of Business; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：In managing chronic diseases such as glaucoma, the timing of periodic examinations is crucial, as it may significantly impact patients' outcomes. We address the question of when to monitor a glaucoma patient by integrating a dynamic, stochastic state space system model of disease evolution with novel optimization approaches to predict the likelihood of progression at any future time. Information about each patient's disease state is learned sequentially through a series of noisy medical tests....
• Risk Estimation via Regression AI翻译
  作者：Broadie, Mark; Du, Yiping; Moallemi, Ciamac C.
  作者单位：Columbia University; Columbia University; Columbia University
  摘要：We introduce a regression-based nested Monte Carlo simulation method for the estimation of financial risk. An outer simulation level is used to generate financial risk factors and an inner simulation level is used to price securities and compute portfolio losses given risk factor outcomes. The mean squared error (MSE) of standard nested simulation converges at the rate k(-2/3), where k measures computational effort. The proposed regression method combines information from different risk factor...
• Penalty Function with Memory for Discrete Optimization via Simulation with Stochastic Constraints AI翻译
  作者：Park, Chuljin; Kim, Seong-Hee
  作者单位：Hanyang University; University System of Georgia; Georgia Institute of Technology
  摘要：We consider a discrete optimization via simulation (DOvS) problem with stochastic constraints on secondary performance measures in which both objective and secondary performance measures need to be estimated by stochastic simulation. To solve the problem, we develop a new method called the Penalty Function with Memory (PFM). It is similar to an existing penalty-type method-which consists of a penalty parameter and a measure of violation of constraints-in a sense that it converts a DOvS problem...
• Adaptive Execution: Exploration and Learning of Price Impact AI翻译
  作者：Park, Beomsoo; Van Roy, Benjamin
  作者单位：Stanford University; Stanford University; Stanford University
  摘要：We consider a model in which a trader aims to maximize expected risk-adjusted profit while trading a single security. In our model, each price change is a linear combination of observed factors, impact resulting from the trader's current and prior activity, and unpredictable random effects. The trader must learn coefficients of a price impact model while trading. We propose a new method for simultaneous execution and learning-the confidence-triggered regularized adaptive certainty equivalent (...
• On Dynamic Decision Making to Meet Consumption Targets AI翻译
  作者：Chen, Lucy Gongtao; Long, Daniel Zhuoyu; Sim, Melvyn
  作者单位：National University of Singapore; Chinese University of Hong Kong
  摘要：We investigate a dynamic decision model that facilitates a target-oriented decision maker in regulating her risky consumption based on her desired target consumption level in every period in a finite planning horizon. We focus on dynamic operational decision problems of a firm where risky cash flows are being resolved over time. The firm can finance consumption by borrowing or saving to attain prescribed consumption targets over time. To evaluate the ability of the consumption in meeting respe...
• Fully Sequential Procedures for Large-Scale Ranking-and-Selection Problems in Parallel Computing Environments AI翻译
  作者：Luo, Jun; Hong, L. Jeff; Nelson, Barry L.; Wu, Yang
  作者单位：Shanghai Jiao Tong University; City University of Hong Kong; City University of Hong Kong; Northwestern University
  摘要：Fully sequential ranking-and-selection (R&S) procedures to find the best from a finite set of simulated alternatives are often designed to be implemented on a single processor. However, parallel computing environments, such as multi-core personal computers and many-core servers, are becoming ubiquitous and easily accessible for ordinary users. In this paper, we propose two types of fully sequential procedures that can be used in parallel computing environments. We call them vector-filling proc...
• Merchant Commodity Storage Practice Revisited AI翻译
  作者：Secomandi, Nicola
  作者单位：Carnegie Mellon University
  摘要：Commodity merchants use various heuristics to value leasing contracts on storage facilities as real options and make inventory trading decisions. Two prominent heuristics sequentially reoptimize simple models, leading to the so-called rolling intrinsic (RI) policy and rolling basket of spread options (RSO) policy. The extant literature numerically demonstrates that these two policies are nearly optimal in many realistic settings and can be used with Monte Carlo simulation to obtain fairly accu...