Operations Research

最新文章

• Foresee the Next Line: Customer Strategies and Information Disclosure in Tandem Queues AI翻译
  作者：Snitkovsky, Ran; Roet-Green, Ricky; Ji, Jingwei
  作者单位：Tel Aviv University; University of Rochester; Stanford University
  摘要：Many services consist of multiple stages, where each stage requires some waiting before completion. For example, customers who visit the Apple Store join the check-in queue first and then wait in another queue to be served by the Genius Bar technician. In such settings, customers often see the queue directly ahead of them but not the one in the next stage. Our paper aims to examine the impact of queue-length information on customers' strategic behavior in such systems. We assume a two-stage ta...
• A Low-Rank Augmented Lagrangian Method for Doubly Nonnegative Relaxations of Mixed-Binary Quadratic Programs AI翻译
  作者：Hou, Di; Tang, Tianyun; Toh, Kim-Chuan
  作者单位：National University of Singapore; National University of Singapore
  摘要：Doubly nonnegative (DNN) programming problems are challenging to solve because of their huge number of ohm(n2) constraints and ohm(n2) variables. In this work, introduce RiNNAL, a method for solving DNN relaxations of large-scale mixed-binary quadratic programs by leveraging their solutions' possible low-rank property. RiNNAL a globally convergent Riemannian augmented Lagrangian method (ALM) that penalizes the nonnegativity and complementarity constraints while preserving all other constraints...
• Dynamic Pricing for a Multiproduct Consumer Electronics Trade-in Program AI翻译
  作者：Zhang, Zhuoluo; Lei, Yanzhe (Murray); Zhou, Sean X.
  作者单位：Xiamen University; Queens University - Canada; Chinese University of Hong Kong
  摘要：We consider a dynamic pricing problem for a consumer electronics trade-in program where a firm acquires and resells multiple types of preowned (used) products over a finite selling horizon. The trade-in program offers two options: trade in for cash, where customers sell their products to the firm and receive a cash payment, and trade in for upgrade, where customers exchange their products for new products at discounted prices. The firm sets trade-in prices (both cash rewards and new products' ...
• Capacity Planning in Stable Matching AI翻译
  作者：Bobbio, Federico; Carvalho, Margarida; Lodi, Andrea; Ricos, Ignacio; Torrico, Alfredo
  作者单位：Universite de Montreal; Universite de Montreal; Technion Israel Institute of Technology; University of Texas System; University of Texas Dallas; Cornell University
  摘要：Motivated by the shortage of seats that the Chilean school choice system is facing, we introduce the problem of jointly increasing school capacities and finding a studentoptimal assignment in the expanded market. Because of the theoretical and practical complexity of the problem, we provide a comprehensive set of tools to solve the problem, including different mathematical programming formulations, a cutting-plane algorithm, and two heuristics that allow obtaining near-optimal solutions quickl...
• Dynamic Pricing for Reusable Resources: The Power of Two Prices AI翻译
  作者：Balseiro, Santiago R.; Ma, Will; Zhang, Wenxin
  作者单位：Columbia University
  摘要：Motivated by real-world applications, such as rental and cloud computing services, we investigate pricing for reusable resources. We consider a system where a single resource with a fixed number of identical copies serves customers with heterogeneous willingness to pay (WTP), and the usage duration distribution is general. Optimal dynamic policies are computationally intractable when usage durations are not memoryless, so the existing literature has focused on static pricing, which incurs a st...
• Dual-Directed Algorithm Design for Efficient Pure Exploration AI翻译
  作者：Qin, Chao; You, Wei
  作者单位：Stanford University; Hong Kong University of Science & Technology
  摘要：Although experimental design often focuses on selecting the single best alternative from a finite set (e.g., in ranking and selection or best-arm identification), many pureexploration problems pursue richer goals. Given a specific goal, adaptive experimentation aims to achieve it by strategically allocating sampling effort, with the underlying sample complexity characterized by a maximin optimization problem. By introducing dual variables, we derive necessary and sufficient conditions for an o...
• Online Learning with Sample Selection Bias AI翻译
  作者：Singhvi, Divya; Singhvi, Somya
  作者单位：New York University; University of Southern California
  摘要：We consider the problem of personalized recommendations on online platforms, where user preferences are unknown, and users interact with the platform through a series of sequential decisions (such as clicking to watch on video platforms or clicking to donate on donation platforms). The platform aims to maximize the final outcome (e.g., viewing duration on video platforms or donations on donation platforms). However, the platform only observes the final outcome for users who complete the first ...
• Unit Commitment Without Commitment: A Dynamic Programming Approach for Managing an Integrated Energy System Under Uncertainty AI翻译
  作者：Brown, David B.; Smith, James E.
  作者单位：Duke University; Dartmouth College
  摘要：Though variability and uncertainty have always posed challenges for power systems, the increasing use of renewable energy sources has exacerbated these issues. At a vertically integrated utility, the system operator manages many generation units- renewable and otherwise-and storage units to ensure that the total energy produced matches contemporaneous demand. Current industry practice at these utilities involves solving unit commitment and economic dispatch optimization problems to choose prod...
• The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model AI翻译
  作者：Shi, Laixi; Li, Gen; Wei, Yuting; Chen, Yuxin; Geist, Matthieu; Chi, Yuejie
  作者单位：Johns Hopkins University; Chinese University of Hong Kong; University of Pennsylvania; Yale University
  摘要：This paper investigates model robustness in reinforcement learning (RL) to reduce the sim-to-real gap in practice. We adopt the framework of distributionally robust Markov decision processes (RMDPs), aimed at learning a policy that optimizes the worst-case performance when the deployed environment falls within a prescribed uncertainty set around the nominal Markov decision process (MDP). Despite recent efforts, the sample complexity of RMDPs remained mostly unsettled regardless of the uncertai...
• Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives AI翻译
  作者：Behdin, Kayhan; Chen, Wenyu; Mazumder, Rahul
  作者单位：Massachusetts Institute of Technology (MIT)
  摘要：We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, which is a key problem in statistical machine learning. Given n samples from a multivariate Gaussian distribution with p variables, the goal is to estimate the p x p inverse covariance matrix (aka precision matrix), assuming it is sparse (i.e., has a few nonzero entries). We propose GraphL0BnB, a new estimator based on an & euro;0-penalized version of the pseudo-likelihood function; most earli...