Operations Research

最新文章

• New Mixed-Integer Nonlinear Programming Formulations for the Unit Commitment Problems with Ramping Constraints AI翻译
  作者：Bacci, Tiziano; Frangioni, Antonio; Gentile, Claudio; Tavlaridis-Gyparakis, Kostas
  作者单位：Consiglio Nazionale delle Ricerche (CNR); Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti (IASI-CNR)
  摘要：The unit commitment (UC) problem in electrical power production requires to optimally operate a set of power generation units over a short time horizon. Operational constraints of each unit depend on its type and can be rather complex. For thermal units, typical ones concern minimum and maximum power output, minimum up-and down-time, startup and shut-down limits, ramp-up and ramp-down limits, and nonlinear objective function. In this work, we present the first mixed-integer nonlinear program f...
• Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis AI翻译
  作者：Balseiro, Santiago R.; Besbes, Omar; Pizarro, Dana
  作者单位：Columbia University; Universidad de O'Higgins
  摘要：Dynamic resource allocation problems arise under a variety of settings and have been studied across disciplines such as operations research and computer science. The present paper introduces a unifying model for a very large class of dynamic optimization problems that we call dynamic resource-constrained reward collection (DRC2) problems. We show that this class encompasses a variety of disparate and classical dynamic optimization problems such as dynamic pricing with capacity constraints, dyn...
• Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-Order Convergence AI翻译
  作者：Luo, Yuetian; Huang, Wen; Li, Xudong; Zhang, Anru
  作者单位：University of Chicago; Xiamen University; Fudan University; Duke University
  摘要：In this paper, we propose a recursive importance sketching algorithm for rank constrained least squares optimization (RISRO). The key step of RISRO is recursive importance sketching, a new sketching framework based on deterministically designed recursive projections, and it significantly differs from the randomized sketching in the literature. Several existing algorithms in the literature can be reinterpreted under this new sketching framework, and RISRO offers clear advantages over them. RISR...
• Technical Note-Asymptotically Optimal Control of Omnichannel Service Systems with Pick-up Guarantees AI翻译
  作者：Gao, Xuefeng; Huang, Junfei; Zhang, Jiheng
  作者单位：Chinese University of Hong Kong; Chinese University of Hong Kong; Hong Kong University of Science & Technology
  摘要：Motivated by the recent popularity of omnichannel service systems, we analyze the joint admission and scheduling control of a queueing system with two classes of customers: online and walk-in. Unlike walk-in customers, online customers are given a target time for pick up upon placing an order. Thus, in addition to minimizing the waiting costs of walk-in customers and the rejection cost of both classes, we need to minimize the earliness and tardiness costs of online customers. Such a distinctiv...
• Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model AI翻译
  作者：Li, Gen; Wei, Yuting; Chi, Yuejie; Chen, Yuxin
  作者单位：University of Pennsylvania; Carnegie Mellon University; University of Pennsylvania
  摘要：This paper is concerned with the sample efficiency of reinforcement learning, assuming access to a generative model (or simulator). We first consider gamma-discounted infinite-horizon Markov decision processes (MDPs) with state space S and action space A. Despite a number of prior works tackling this problem, a complete picture of the trade-offs between sample complexity and statistical accuracy has yet to be determined. In particular, all prior results suffer from a severe sample size barrier...
• Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis AI翻译
  作者：Li, Gen; Cai, Changxiao; Chen, Yuxin; Wei, Yuting; Chi, Yuejie
  作者单位：University of Pennsylvania; University of Pennsylvania; Carnegie Mellon University
  摘要：Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP) in a model-free fashion, lies at the heart of reinforcement learning. When it comes to the synchronous setting (such that independent samples for all state-action pairs are drawn from a generative model in each iteration), substantial progress has been made toward understanding the sample efficiency of Q-learning. Consider a gamma-discounted infinite-horizon MDP with state space S and action space A: to ...
• Minkowski Centers via Robust Optimization: Computation and Applications AI翻译
  作者：den Hertog, Dick; Pauphilet, Jean; Soali, Mohamed Yahya
  作者单位：University of Amsterdam; University of London; Columbia University
  摘要：Centers of convex sets are geometric objects that have received extensive attention in the mathematical and optimization literature, both from a theoretical and practical standpoint. For instance, they serve as initialization points for many algorithms such as interior-point, hit-and-run, or cutting-planes methods. First, we observe that computing a Minkowski center of a convex set can be formulated as the solution of a robust optimization problem. As such, we can derive tractable formulations...
• A Planner-Trader Decomposition for Multimarket Hydro Scheduling AI翻译
  作者：Schindler, Kilian; Rujeerapaiboon, Napat; Kuhn, Daniel; Wiesemann, Wolfram
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; National University of Singapore; Imperial College London
  摘要：Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany and the steady growth in photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage forces hydropower producers to recover part of their original profitability on the reserve markets. We propose a bilayer stochastic programming framework for the optimal operation of a fleet of interconnected hydropower plants that sells energy on both the spot and t...
• Resource-Aware Cost-Sharing Methods for Scheduling Games AI翻译
  作者：Christodoulou, George; Gkatzelis, Vasilis; Sgouritsa, Alkmini
  作者单位：Aristotle University of Thessaloniki; Drexel University; Athens University of Economics & Business
  摘要：We study the performance of cost-sharing methods in a selfish scheduling setting where a group of users schedule their jobs on machines with load-dependent cost functions, aiming to minimize their own cost. Anticipating this user behavior, the system designer chooses a decentralized protocol that defines how the cost generated on each machine is to be shared among its users, and the performance of the protocol is evaluated over the Nash equilibria of the induced game. Previous work on selfish ...
• Bonferroni-Free and Indifference-Zone-Flexible Sequential Elimination Procedures for Ranking and Selection AI翻译
  作者：Wang, Wenyu; Wan, Hong; Chen, Xi
  作者单位：North Carolina State University; Virginia Polytechnic Institute & State University
  摘要：This paper proposes two fully sequential procedures for selecting the best system with a guaranteed probability of correct selection (PCS). The main features of the proposed procedures include the following: (1) adopting a Bonferroni-free model that overcomes the conservativeness of the Bonferroni correction and delivers the exact probabilistic guarantee without overshooting; (2) conducting always valid and fully sequential hypothesis tests that enable continuous monitoring of each candidate s...