3期

最新文章

• Optimal Stopping and Early Exercise: An Eigenfunction Expansion Approach AI翻译
  作者：Li, Lingfei; Linetsky, Vadim
  作者单位：Chinese University of Hong Kong; Northwestern University
  摘要：This paper proposes a new approach to solve finite-horizon optimal stopping problems for a class of Markov processes that includes one-dimensional diffusions, birth-death processes, and jump diffusions and continuous-time Markov chains obtained by time-changing diffusions and birth-and-death processes with Levy subordinators. When the expectation operator has a purely discrete spectrum in the Hilbert space of square-integrable payoffs, the value function of a discrete optimal stopping problem ...
• Optimal Sequential Exploration: Bandits, Clairvoyants, and Wildcats AI翻译
  作者：Brown, David B.; Smith, James E.
  作者单位：Duke University
  摘要：This paper was motivated by the problem of developing an optimal policy for exploring an oil and gas field in the North Sea. Where should we drill first? Where do we drill next? In this and many other problems, we face a trade-off between earning (e.g., drilling immediately at the sites with maximal expected values) and learning (e.g., drilling at sites that provide valuable information) that may lead to greater earnings in the future. These sequential exploration problems resemble a multiarme...
• Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network AI翻译
  作者：Papavasiliou, Anthony; Oren, Shmuel S.
  作者单位：Universite Catholique Louvain; University of California System; University of California Berkeley
  摘要：In this paper we present a unit commitment model for studying the impact of large-scale wind integration in power systems with transmission constraints and system component failures. The model is formulated as a two-stage stochastic program with uncertain wind production in various locations of the network as well as generator and transmission line failures. We present a scenario selection algorithm for selecting and weighing wind power production scenarios and composite element failures, and ...
• Simple Policies for Dynamic Pricing with Imperfect Forecasts AI翻译
  作者：Chen, Yiwei; Farias, Vivek F.
  作者单位：Renmin University of China; Massachusetts Institute of Technology (MIT)
  摘要：We consider the classical single-product dynamic pricing problem allowing the scale of demand intensity to be modulated by an exogenous market size stochastic process. This is a natural model of dynamically changing market conditions. We show that for a broad family of Gaussian market-size processes, simple dynamic pricing rules that are essentially agnostic to the specification of this market-size process perform provably well. The pricing policies we develop are shown to compensate for forec...
• Optimal Cardinality Constrained Portfolio Selection AI翻译
  作者：Gao, Jianjun; Li, Duan
  作者单位：Shanghai Jiao Tong University; Chinese University of Hong Kong
  摘要：One long-standing challenge in both the optimization and investment communities is to devise an efficient algorithm to select a small number of assets from an asset pool such that a portfolio objective is optimized. This cardinality constrained investment situation naturally arises due to the presence of various forms of market friction, such as transaction costs and management fees, or even due to the consideration of mental cost. Unfortunately, the combinatorial nature of such a portfolio se...
• Robust Partitioning for Stochastic Multivehicle Routing AI翻译
  作者：Carlsson, John Gunnar; Delage, Erick
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Universite de Montreal; HEC Montreal
  摘要：The problem of coordinating a fleet of vehicles so that all demand points on a territory are serviced and the workload is most evenly distributed among the vehicles is a hard one. For this reason, it is often an effective strategy to first divide the service region and impose that each vehicle is only responsible for its own subregion. This heuristic also has the practical advantage that over time, drivers become more effective at serving their territory and customers. In this paper, we assume...
• Approximation Algorithms for the Stochastic Lot-Sizing Problem with Order Lead Times AI翻译
  作者：Levi, Retsef; Shi, Cong
  作者单位：Massachusetts Institute of Technology (MIT); University of Michigan System; University of Michigan
  摘要：We develop new algorithmic approaches to compute provably near-optimal policies for multiperiod stochastic lot-sizing inventory models with positive lead times, general demand distributions, and dynamic forecast updates. The policies that are developed have worst-case performance guarantees of 3 and typically perform very close to optimal in extensive computational experiments. The newly proposed algorithms employ a novel randomized decision rule. We believe that these new algorithmic and perf...
• Optimal Budget Allocation for Sample Average Approximation AI翻译
  作者：Royset, Johannes O.; Szechtman, Roberto
  作者单位：United States Department of Defense; United States Navy; Naval Postgraduate School
  摘要：The sample average approximation approach to solving stochastic programs induces a sampling error, caused by replacing an expectation by a sample average, as well as an optimization error due to approximating the solution of the resulting sample average problem. We obtain estimators of an optimal solution and the optimal value of the original stochastic program after executing a finite number of iterations of an optimization algorithm applied to the sample average problem. We examine the conve...
• The Robust Capacitated Vehicle Routing Problem Under Demand Uncertainty AI翻译
  作者：Gounaris, Chrysanthos E.; Wiesemann, Wolfram; Floudas, Christodoulos A.
  作者单位：Princeton University; Imperial College London; Princeton University
  摘要：The robust capacitated vehicle routing problem (CVRP) under demand uncertainty is studied to address the minimum cost delivery of a product to geographically dispersed customers using capacity-constrained vehicles. Contrary to the deterministic CVRP, which postulates that the customer demands for the product are deterministic and known, the robust CVRP models the customer demands as random variables, and it determines a minimum cost delivery plan that is feasible for all anticipated demand rea...
• Joint Optimization of Sampling and Control of Partially Observable Failing Systems AI翻译
  作者：Kim, Michael Jong; Makis, Viliam
  作者单位：University of Toronto; National University of Singapore
  摘要：Stochastic control problems that arise in reliability and maintenance optimization typically assume that information used for decision-making is obtained according to a predetermined sampling schedule. In many real applications, however, there is a high sampling cost associated with collecting such data. It is therefore of equal importance to determine when information should be collected and to decide how this information should be utilized for maintenance decision-making. This type of joint ...