Operations Research

最新文章

• Maximum Entropy Distributions with Applications to Graph Simulation AI翻译
  作者：Glasserman, Paul; de Larrea, Enrique Lelo
  作者单位：Columbia University; Columbia University
  摘要：We study the problem of sampling uniformly from discrete or continuous product sets subject to linear constraints. This family of problems includes sampling weighted bipartite, directed, and undirected graphs with given degree sequences. We analyze two candidate distributions for sampling from the target set. The first one maximizes entropy subject to satisfying the constraints in expectation. The second one is the distribution from an exponential family that maximizes the minimum probability ...
• Efficient Resource Allocation Contracts to Reduce Adverse Events AI翻译
  作者：Liang, Yong; Sun, Peng; Tang, Runyu; Zhang, Chong
  作者单位：Tsinghua University; Duke University; Xi'an Jiaotong University; Tilburg University
  摘要：Motivated by the allocation of online visits to product, service, and content suppliers in the platform economy, we consider a dynamic contract design problem in which a principal constantly determines the allocation of a resource (online visits) to multiple agents. Although agents are capable of running the business, they introduce adverse events, the frequency of which depends on each agent???s effort level. We study continuous-time dynamic contracts that utilize resource allocation and mone...
• Stability and Sample-Based Approximations of Composite Stochastic Optimization Problems AI翻译
  作者：Dentcheva, Darinka; Lin, Yang; Penev, Spiridon
  作者单位：Stevens Institute of Technology; University of New South Wales Sydney; University of New South Wales Sydney
  摘要：Optimization under uncertainty and risk is indispensable in many practical situations. Our paper addresses stability of optimization problems using composite risk functionals that are subjected to multiple measure perturbations. Our main focus is the asymptotic behavior of data-driven formulations with empirical or smoothing estimators such as kernels or wavelets applied to some or to all functions of the compositions. We analyze the properties of the new estimators and we establish strong law...
• Every Choice Function Is Pro-Con Rationalizable AI翻译
  作者：Dogan, Serhat; Yildiz, Kemal
  作者单位：Ihsan Dogramaci Bilkent University
  摘要：We consider an agent who is endowed with two sets of orderings: pro-and con orderings. For each choice set, if an alternative is the top-ranked by a pro-ordering (con-ordering), then this is a pro (con) for choosing that alternative. The alternative with more pros than cons is chosen from each choice set. Each ordering may have a weight reflecting its salience. In this case, the probability that an alternative is chosen equals the difference between the total weights of its pros and cons. We s...
• Early Reservation for Follow-up Appointments in a Slotted-Service Queue AI翻译
  作者：Ding, Yichuan; Gupta, Diwakar; Tang, Xiaoxu
  作者单位：McGill University; University of Texas System; University of Texas Austin; Wells Fargo Company
  摘要：We study an appointment-based slotted-service queue with the goal of maximizing service volume. Returning customers prefer to be served by the same service agent as in their previous visit. This model captures aspects of a whole host of settings, including medical clinics, law firms, and tutoring services. We consider a simple strategy that a service provider may use to reduce balking among returning customers-designate some returning customers as high-priority customers. These customers are p...
• Lagrangian Inference for Ranking Problems AI翻译
  作者：Liu, Yue; Fang, Ethan X.; Lu, Junwei
  作者单位：Harvard University; Duke University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：We propose a novel combinatorial inference framework to conduct general uncertainty quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce (BTL) model, where each item is assigned a positive preference score that determines the Bernoulli distributions of pairwise comparisons' outcomes. Our proposedmethod aims to infer general ranking properties of the BTLmodel. The general ranking properties include the local properties such as if an item is preferred over anothe...
• Technical Note-Average Cost Optimality in Partially Observable Lost-Sales Inventory Systems AI翻译
  作者：Bai, Xingyu; Chen, Xin; Stolyar, Alexander L.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We consider a partially observable lost-sales inventory system, in which the inventory level is observed only when it reaches zero. We use the vanishing discount factor approach to prove the existence of a stationary optimal policy for the average cost minimization. As our main methodological contribution, we provide a way to verify the key condition of the vanishing discount factor approach???the uniform boundedness of the relative discounted value function. To accomplish that, we construct a...
• Forecasting COVID-19 and Analyzing the Effect of Government Interventions AI翻译
  作者：Li, Michael Lingzhi; Bouardi, Hamza Tazi; Lami, Omar Skali; Trikalinos, Thomas A.; Trichakis, Nikolaos; Bertsimas, Dimitris
  作者单位：Massachusetts Institute of Technology (MIT); Brown University; Massachusetts Institute of Technology (MIT)
  摘要：We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more than 200 geographical areas since early April 2020 and recorded 6% and 11% two-week, out-of-sample median mean absolute percentage error on predicting cases and deaths, respectively. DELPHI compares favorably with other top COVI...
• Offline Multi-Action Policy Learning: Generalization and Optimization AI翻译
  作者：Zhou, Zhengyuan; Athey, Susan; Wager, Stefan
  作者单位：New York University; Stanford University
  摘要：In many settings, a decision maker wishes to learn a rule, or policy, that maps from observable characteristics of an individual to an action. Examples include selecting offers, prices, advertisements, or emails to send to consumers, choosing a bid to submit in a contextual first-price auctions, and determining which medication to prescribe to a patient. In this paper, we study the offline multi-action policy learning problem with observational data and where the policy may need to respect bud...
• On the Strength of Relaxations of Weakly Coupled Stochastic Dynamic Programs AI翻译
  作者：Brown, David B.; Zhang, Jingwei
  作者单位：Duke University
  摘要：Many stochastic dynamic programs (DPs) have a weakly coupled structure in that a set of linking constraints in each period couples an otherwise independent collection of subproblems. Two widely studied approximations of such problems are approximate linear programs (ALPs), which involve optimizing value function approximations that additively separate across subproblems, and Lagrangian relaxations, which involve relaxing the linking constraints. It is well known that both of these approximatio...