Operations Research

最新文章

• Near-Optimal Pricing and Resource Allocation in a Large-Scale Service System AI翻译
  作者：Wu, Zerui; Liu, Ran; Sun, Xu
  作者单位：Shanghai Jiao Tong University; University of Miami
  摘要：We study dynamic pricing and resource allocation in large-scale service systems where multiple service units serve customers who are both price and delay sensitive. Customers are segmented into classes characterized by class-specific service rates and demand functions shaped by posted prices and estimated delays. To jointly optimize revenue and delay performance, we propose a family of state-dependent greedy heuristics that (i) assign dedicated service capacities to each customer class, and (i...
• Optimal Trade Execution Under Endogenous Order Flow AI翻译
  作者：Chen, Ying; Horst, Ulrich; Tran, Hoang Hai
  作者单位：National University of Singapore; Humboldt University of Berlin; National University of Singapore
  摘要：We consider an optimal liquidation model in which an investor is required to execute meta-orders during intraday trading periods, and his trading activity triggers child orders and endogenously affects future order flow, both instantaneously and permanently. Under the assumptions of risk neutrality and deterministic constants of the impact parameters, we provide closed-form solutions and illustrate the relationship between trading strategies and feedback effects. The optimal trading strategy i...
• Estimation of High-Dimensional Contextual Pricing Models with Nonparametric Price Confounders AI翻译
  作者：Wang, Yining; Liu, Quanquan
  作者单位：University of Texas System; University of Texas Dallas
  摘要：Personalized pricing with contextual information is a widespread practice in a number of revenue management problems. A pricing algorithm or platform utilizes users' personal data to make the most profitable pricing decisions, which could vary among individuals. In this paper, we study the question of estimating a contextual demand regression model with high-dimensional data, incorporating an unknown, nonparametric pricing function that acts as a confounding term to the demand model. We propos...
• Markdown Policies for Demand Learning with Forward-Looking Customers AI翻译
  作者：Birge, John R.; Chen, Hongfan (Kevin); Keskin, N. Bora
  作者单位：University of Chicago; Chinese University of Hong Kong; Duke University
  摘要：We consider the markdown pricing problem of a firm that sells a product to a mixture of myopic and forward-looking customers. The firm faces uncertainty about the customers' forward-looking behavior, arrival pattern, and valuations for the product, which we collectively refer to as the demand model. Over a multiperiod selling season, the firm sequentially marks down the product's price and makes demand observations to learn about the underlying demand model. Because forward-looking customers c...
• Tight Guarantees for Multiunit Prophet Inequalities and Online Stochastic Knapsack AI翻译
  作者：Jiang, Jiashuo; Ma, Will; Zhang, Jiawei
  作者单位：Hong Kong University of Science & Technology; Columbia University; Columbia University; New York University
  摘要：Prophet inequalities are a useful tool for designing online allocation procedures and comparing their performance to the optimal offline allocation. In the basic setting of k-unit prophet inequalities, a well-known procedure with its celebrated performance guarantee of 1-1 root k+3 has found widespread adoption in mechanism design and general online allocation problems in online advertising, healthcare scheduling, and revenue management. Despite being commonly used to derive approximately opti...
• Differential Privacy via Distributionally Robust Optimization AI翻译
  作者：Selvi, Aras; Liu, Huikang; Wiesemann, Wolfram
  作者单位：Imperial College London; Shanghai Jiao Tong University
  摘要：In recent years, differential privacy has emerged as the de facto standard for sharing statistics of data sets while limiting the disclosure of private information about the involved individuals. This is achieved by randomly perturbing the statistics to be published, which in turn, leads to a privacy-accuracy trade-off; larger perturbations provide stronger privacy guarantees, but they result in less accurate statistics that offer lower utility to the recipients. Of particular interest are, th...
• On Multivariate Singular Spectrum Analysis: Tensor and Matrix Variants AI翻译
  作者：Agarwal, Anish; Alomar, Abdullah; Shah, Devavrat
  作者单位：Columbia University; Massachusetts Institute of Technology (MIT)
  摘要：We introduce and analyze two extensions of singular spectrum analysis (SSA) to the multivariate setting: a new variant of the well-known matrix-based method (mSSA) and a novel tensor-based approach (tSSA). Under a spatio-temporal factor model, we establish prediction-error guarantees for mSSA for both imputation and out-of-sample forecasting. By exploiting both spatial and temporal structure, mSSA achieves better rates than univariate SSA and standard matrix estimation methods. The out-of-samp...
• Sinkhorn Distributionally Robust Optimization AI翻译
  作者：Wang, Jie; Gao, Rui; Xie, Yao
  作者单位：The Chinese University of Hong Kong, Shenzhen; University of Texas System; University of Texas Austin; University System of Georgia; Georgia Institute of Technology
  摘要：We study distributionally robust optimization with Sinkhorn distance: a variant of Wasserstein distance based on entropic regularization. We derive a convex programming dual reformulation for general nominal distributions, transport costs, and loss functions. To solve the dual reformulation, we develop a stochastic mirror descent algorithm with biased subgradient estimators and derive its computational complexity guarantees. Finally, we provide numerical examples using synthetic and real data ...
• Deterministic Budget-Feasible Clock Auctions AI翻译
  作者：Balkanski, Eric; Garimidi, Pranav; Gkatzelis, Vasilis; Schoepflin, Daniel; Tan, Xizhi
  作者单位：Columbia University; Drexel University; Rutgers University System; Rutgers University New Brunswick
  摘要：We revisit the well-studied problem of budget-feasible procurement, where a buyer with a strict budget constraint seeks to acquire services from a group of strategic providers (the sellers). During the last decade, several strategy-proof budget-feasible procurement auctions have been proposed, aiming to maximize the value of the buyer while eliciting each seller's true cost for providing their service. Our main result in this paper is a novel method for designing budget-feasible auctions, lead...
• Decision Making with Side Information: A Causal Transport Robust Approach AI翻译
  作者：Yang, Jincheng; Zhang, Luhao; Chen, Ningyuan; Gao, Rui; Hu, Ming
  作者单位：Johns Hopkins University; University of Toronto; University Toronto Mississauga; University of Toronto; University of Texas System; University of Texas Austin
  摘要：We consider stochastic optimization with side information where, prior to decision making, covariate data are available to inform better decisions. To hedge against data uncertainty while capturing the information structure revealed from the conditional distribution of random problem parameters given the covariate values, we propose a distributionally robust formulation based on causal transport distance. We derive a dual reformulation for evaluating the worst-case expected cost and show that ...