Operations Research

最新文章

• A Unified Framework for Analyzing and Optimizing a Class of Convex Fairness Measures AI翻译
  作者：Tsang, Man Yiu; Shehadeh, Karmel S.
  作者单位：Texas Tech University System; Texas Tech University; University of Southern California
  摘要：We propose a new framework that unifies different fairness measures into a general, parameterized class of convex fairness measures suitable for optimization contexts. First, we propose a new class of order-based fairness measures, discuss their properties, and derive an axiomatic characterization for such measures. Then, we introduce the class of convex fairness measures, discuss their properties, and derive an equivalent dual representation of these measures as a robustified order-based fair...
• The Cut-and-Play Algorithm: Computing Nash Equilibria via Outer Approximations AI翻译
  作者：Carvalho, Margarida; Dragotto, Gabriele; Lodi, Andrea; Sankaranarayanan, Sriram
  作者单位：Universite de Montreal; Princeton University; Technion Israel Institute of Technology; Indian School of Business (ISB)
  摘要：We introduce Cut-and-Play, a practically efficient algorithm for computing Nash equilibria in simultaneous noncooperative games where players decide via nonconvex and possibly unbounded optimization problems with separable payoff functions. Our algorithm exploits an intrinsic relationship between the equilibria of the original nonconvex game and the ones of a convexified counterpart. In practice, Cut-and-Play formulates a series of convex approximations of the game and iteratively refines them...
• Joint Capacity Allocation and Job Assignment Under Uncertainty AI翻译
  作者：Wang, Peng; Lim, Yun Fong; Loke, Gar Goei
  作者单位：Singapore University of Social Sciences (SUSS); Singapore Management University; Durham University
  摘要：In this paper, we consider the multiperiod joint capacity allocation and job assignment problem. The goal of the planner is to simultaneously decide on allocating resources across the J different supply nodes and assigning jobs of I different demand origins to these J nodes, so as to maximize the reward for matching or minimize the cost of failure to match. We furthermore consider three features: (i) supply is replenishable after random time, (ii) demand is random, and (iii) demand can wait an...
• 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...
• Load Balancing Using Sparse Communication AI翻译
  作者：Mendelson, Gal; Kuang, Xu
  作者单位：Technion Israel Institute of Technology; Stanford University
  摘要：Load balancing across parallel servers is an important class of congestion control problems that arise in service systems. An effective load balancer relies heavily on accurate, real-time congestion information to make routing decisions. However, obtaining such information can impose significant communication overheads, especially in demanding applications such as those found in modern data centers. We introduce a framework for communication-aware load balancing and design new load balancing a...
• Algorithmic Challenges in Ensuring Fairness at the Time of Decision AI翻译
  作者：Salem, Tad; Gupta, Swati; Kamble, Vijay
  作者单位：United States Department of Defense; United States Navy; United States Naval Academy; Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：Algorithmic decision making in societal contexts, such as retail pricing, loan administration, recommendations on online platforms, etc., can be framed as stochastic optimization under bandit feedback, which typically requires experimentation with different decisions for the sake of learning. Such experimentation often results in perceptions of unfairness among people impacted by these decisions; for instance, there have been several recent lawsuits accusing companies that deploy algorithmic p...
• A Stability Principle for Learning Under Nonstationarity AI翻译
  作者：Huang, Chengpiao; Wang, Kaizheng
  作者单位：Columbia University; Columbia University
  摘要：We develop a versatile framework for statistical learning in nonstationary environments. In each time period, our approach applies a stability principle to select a look-back window that maximizes the utilization of historical data while keeping the cumulative bias within an acceptable range relative to the stochastic error. Our theory showcases the adaptivity of this approach to unknown nonstationarity. We prove regret bounds that are minimax optimal up to logarithmic factors when the populat...
• Distributed Speed Scaling in Large-Scale Service Systems AI翻译
  作者：Rutten, Daan; Zubeldia, Martin; Mukherjee, Debankur
  作者单位：University System of Georgia; Georgia Institute of Technology; University of Minnesota System; University of Minnesota Twin Cities
  摘要：We consider a large-scale parallel-server loss system with an unknown arrival rate, where each server is able to adjust its processing speed. The objective is to minimize the system cost, which consists of a power cost to maintain the servers' processing speeds and a quality of service cost depending on the tasks' processing times among others. We draw on ideas from stochastic approximation to design a novel speed-scaling algorithm and prove that the servers' processing speeds converge to the ...
• Optimal Dual-Souring Inventory Policies with Order Tracking: Backlogging and Lost Sales Under Uncertain Lead Times AI翻译
  作者：Song, Jing-Sheng; Xiao, Li; Zhang, Hanqin
  作者单位：Duke University; University of Macau; National University of Singapore
  摘要：This study explores the effective use of order-tracking information in dualsourcing inventory systems in both backlogging and lost-sales settings. Our inventory model features a normal source, comprising a two-stage tandem queue with Erlangdistributed processing times at each stage, and an emergency source that bypasses the first stage. We show that under certain conditions the optimal policy is characterized by two thresholds and one switching curve determined by the workload at the emergency...
• Optimal Abort Policy for Mission-Critical Systems Under Imperfect Condition Monitoring AI翻译
  作者：Sun, Qiuzhuang; Hu, Tiawen; Ye, Zhi-Sheng
  作者单位：University of Sydney; University of Electronic Science & Technology of China; National University of Singapore
  摘要：Although most on-demand mission-critical systems are engineered to be reliable to support critical tasks, occasional failures may still occur during missions. To increase system survivability, a common practice is to abort the mission before an imminent failure. We consider optimal mission abort for a system whose deterioration follows a general three-state (normal, defective, failed) semi-Markov chain. The failure is assumed selfrevealed, whereas the healthy and defective states have to be in...