2023

最新文章

• Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Trade-off Curve AI翻译
  作者：Sinclair, Sean R.; Jain, Gauri; Banerjee, Siddhartha; Yu, Christina Lee
  作者单位：Cornell University
  摘要：We consider the problem of dividing limited resources to individuals arriving over T rounds. Each round has a random number of individuals arrive, and individuals can be characterized by their type (i.e., preferences over the different resources). A standard notion of fairness in this setting is that an allocation simultaneously satisfy envy-freeness and efficiency. The former is an individual guarantee, requiring that each agent prefers the agent's own allocation over the allocation of any ot...
• Preface to Special Issue on Computational Advances in Short-Term Power System Operations AI翻译
  作者：Baldick, Ross; Low, Steven; O'Neill, Richard; Ralph, Daniel; Zakeri, Golbon
  作者单位：University of Texas System; University of Texas Austin; California Institute of Technology; United States Department of Energy (DOE); Advanced Research Projects Agency-Energy (ARPA-E); University of Cambridge; University of Massachusetts System; University of Massachusetts Amherst
• Unit Commitment Problem with Energy Storage Under Correlated Renewables Uncertainty AI翻译
  作者：Cordera, Felipe; Moreno, Rodrigo; Ordonez, Fernando
  作者单位：Universidad de Chile; Massachusetts Institute of Technology (MIT)
  摘要：The extensive integration of renewable generation in electricity systems is significantly increasing the variability and correlation in power availability and the need for energy storage capacity. This increased uncertainty and storage capacity should be considered in operational decisions such as the short-term unit commitment (UC) problem. In this work, we formulate a day-ahead UC problem with energy storage, considering multistage correlated uncertainty on renewables' power availability. We...
• Nonconvex Piecewise Linear Functions: Advanced Formulations and Simple Modeling Tools AI翻译
  作者：Huchette, Joey; Vielma, Juan Pablo
  作者单位：Rice University; Massachusetts Institute of Technology (MIT)
  摘要：We present novel mixed-integer programming (MIP) formulations for optimization over nonconvex piecewise linear functions. We exploit recent advances in the systematic construction of MIP formulations to derive new formulations for univariate functions using a geometric approach and for bivariate functions using a combinatorial approach. All formulations are strong, small (so-called logarithmic formulations), and have other desirable computational properties. We present extensive experiments in...
• Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition AI翻译
  作者：Aravena, Ignacio; Molzahn, Daniel K.; Zhang, Shixuan; Petra, Cosmin G.; Curtis, Frank E.; Tu, Shenyinying; Wachter, Andreas; Wei, Ermin; Wong, Elizabeth; Gholami, Amin; Sun, Kaizhao; Sun, Xu Andy; Elbert, Stephen T.; Holzer, Jesse T.; Veeramany, Arun
  作者单位：United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University System of Georgia; Georgia Institute of Technology; Lehigh University; Northwestern University; University of California System; University of California San Diego; Massachusetts Institute of Technology (MIT); United States Department of Energy (DOE); Pacific Northwest National Laboratory
  摘要：The optimal power-flow problem is central to many tasks in the design and operation of electric power grids. This problem seeks the minimum-cost operating point for an electric power grid while satisfying both engineering requirements and physical laws describing how power travels through the electric network. By additionally considering the possibility of component failures and using an accurate alternating current (AC) power-flow model of the electric network, the security-constrained AC opt...
• A Data-Driven Approach to Beating SAA Out of Sample AI翻译
  作者：Gotoh, Jun-ya; Kim, Michael Jong; Lim, Andrew E. B.
  作者单位：Chuo University; University of British Columbia; National University of Singapore; National University of Singapore
  摘要：Whereas solutions of distributionally robust optimization (DRO) problems can sometimes have a higher out-of-sample expected reward than the sample average approximation (SAA), there is no guarantee. In this paper, we introduce a class of distributionally optimistic optimization (DOO) models and show that it is always possible to beat SAA out-of-sample if we consider not just worst case (DRO) models but also best case (DOO) ones. We also show, however, that this comes at a cost: optimistic solu...
• Data-Driven Hospital Admission Control: A Learning Approach AI翻译
  作者：Zhalechian, Mohammad; Keyvanshokooh, Esmaeil; Shi, Cong; Van Oyen, Mark P.
  作者单位：Indiana University System; Indiana University Bloomington; IU Kelley School of Business; Texas A&M University System; Texas A&M University College Station; Mays Business School; University of Miami; University of Michigan System; University of Michigan
  摘要：The choice of care unit upon admission to the hospital is a challenging task because of the wide variety of patient characteristics, uncertain needs of patients, and limited number of beds in intensive and intermediate care units. The care unit placement decisions involve capturing the trade-off between the benefit of better health outcomes versus the opportunity cost of reserving higher level of care beds for potentially more complex patients arriving in the future. By focusing on reducing th...
• Technical Note-Data-Driven Profit Estimation Error in the Newsvendor Model AI翻译
  作者：Siegel, Andrew F.; Wagner, Michael R.
  作者单位：University of Washington; University of Washington Seattle
  摘要：In this note, we identify a statistically significant error in naively estimating the expected profit in a data-driven newsvendor model, and we show how to correct the error. In particular, we analyze a newsvendor model where the continuous demand distribution is not known, and only a sample of demand data is available. In this context, an empirical demand distribution, that is induced by the sample of data, is used in place of the (unknown) true distribution. The quantity at the critical perc...
• Inventory Control and Learning for One-Warehouse Multistore System with Censored Demand AI翻译
  作者：Bekci, Recep Yusuf; Gumus, Mehmet; Miao, Sentao
  作者单位：University of Waterloo; McGill University; University of Colorado System; University of Colorado Boulder
  摘要：Motivated by our collaboration with one of the largest fast-fashion retailers in Europe, we study a two-echelon inventory control problem called the one-warehouse multistore (OWMS) problem when the demand distribution is unknown. This system has a central warehouse that receives an initial replenishment and distributes its inventory to multiple stores in each time period during a finite horizon. The goal is to minimize the total expected cost, which consists of shipment, holding, lost-sales, a...
• Portfolio Selection, Periodic Evaluations and Risk Taking AI翻译
  作者：Tse, Alex S. L.; Zheng, Harry
  作者单位：University of London; University College London; Imperial College London
  摘要：We present a continuous-time portfolio selection problem faced by an agent with S-shaped preference who maximizes the utilities derived from the portfolio's periodic performance over an infinite horizon. The periodic reward structure creates subtle incentive distortion. In some cases, local risk aversion is induced, which discourages the agent from risk taking in the extreme bad states of the world. In some other cases, eventual ruin of the portfolio is inevitable, and the agent underinvests i...