6期

最新文章

• 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...
• 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...
• On the Structure of Decision Diagram-Representable Mixed-Integer Programs with Application to Unit Commitment AI翻译
  作者：Salemi, Hosseinali; Davarnia, Danial
  作者单位：Iowa State University
  摘要：Over the past decade, decision diagrams (DDs) have been used to model and solve integer programming and combinatorial optimization problems. Despite successful performance of DDs in solving various discrete optimization problems, their extension to model mixed-integer programs (MIPs), such as those appearing in energy applications, is lacking. More broadly, the question of which problem structures admit a DD representation is still open in the DD community. In this paper, we address this quest...
• Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality AI翻译
  作者：Gao, Rui
  作者单位：University of Texas System; University of Texas Austin
  摘要：Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable solutions by hedging against data perturbations in Wasserstein distance. Despite its recent empirical success in operations research and machine learning, existing performance guarantees for generic loss functions are either overly conservative because of the curse of dimensionality or plausible only in large sample asymptotics. In this paper, we develop a nonasymptotic framework for analyzing the out-...
• 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...
• A Surrogate-Based Asynchronous Decomposition Technique for Realistic Security-Constrained Optimal Power Flow Problems AI翻译
  作者：Petra, Cosmin G.; Aravena, Ignacio
  作者单位：United States Department of Energy (DOE); Lawrence Livermore National Laboratory
  摘要：We present a decomposition approach for obtaining good feasible solutions for the security-constrained, alternating-current, optimal power flow (SC-AC-OPF) problem at an industrial scale and under real-world time and computational limits. The approach was designed while preparing and participating in ARPA-E's Grid Optimization Competition (GOC) Challenge 1. The challenge focused on a near-real-time version of the SC-AC-OPF problem, where a base operating point is optimized, taking into account...
• The Anchor-Robust Project Scheduling Problem AI翻译
  作者：Bendotti, Pascale; Chretienne, Philippe; Fouilhoux, Pierre; Pass-Lanneau, Adele
  作者单位：Electricite de France (EDF); Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite
  摘要：In project scheduling with uncertain processing times, the decision maker often needs to compute a baseline schedule in advance while guaranteeing that some jobs will not be rescheduled later. Standard robust approaches either produce a schedule with a very large makespan or offer no guarantee on starting times of the jobs. The concept of anchor-robustness is introduced as a middle ground between these approaches. A subset of jobs is said to be anchored if the starting times of its jobs in the...
• Optimizing Risk-Balancing Return Under Discrete Choice Models AI翻译
  作者：Li, Hongmin; Webster, Scott
  作者单位：Arizona State University; Arizona State University-Tempe
  摘要：We examine a firm's pricing decision when managing a broad product line with the goal of optimally balancing the expected return on product investment with the revenue or profit risk associated with uncertain customer choices. We consider the multinomial logit (MNL) model and the mean-variance objective function and illustrate how the level of risk tolerance influences the firm's optimal markups. We show that the solution approach and results generalize to the nested logit (NL) choice model an...
• Asymptotic Optimality of Semi-Open-Loop Policies in Markov Decision Processes with Large Lead Times AI翻译
  作者：Bai, Xingyu; Chen, Xin; Li, Menglong; Stolyar, Alexander
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University System of Georgia; Georgia Institute of Technology; City University of Hong Kong; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We consider a generic Markov decision process (MDP) with two controls: one control taking effect immediately and the other control whose effect is delayed by a positive lead time. As the lead time grows, one naturally expects that the effect of the delayed action only weakly depends on the current state, and decoupling the delayed action from the current state could provide good controls. The purpose of this paper is to substantiate this decoupling intuition by establishing asymptotic optimali...
• Scheduling Advertising on Cable Television AI翻译
  作者：Souyris, Sebastian; Seshadri, Sridhar; Subramanian, Sriram
  作者单位：Rensselaer Polytechnic Institute; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Advertisement scheduling is a daily essential operational process in the television business. Efficient distribution of viewers among advertisers allows the television network to satisfy contracts and increase ad sale revenues. Ad scheduling is a challenging multiperiod, mixed-integer programming problem in which the network must create schedules to meet advertisers' campaign goals and maximize ad revenues. Each campaign must meet a specific target group of viewers and a unique set of constrai...