检索结果-内蒙古大学图书馆

FROM INFINITE TO FINITE PROGRAMS: EXPLICIT ERROR BOUNDS WITH APPLICATIONS TO approximate dynamic programming

SIAM JOURNAL ON OPTIMIZATION 2018年第3期28卷 1968-1998页

作者： Esfahani, Peyman Mohajerin Sutter, Tobias Kuhn, Daniel Lygeros, John Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland Ecole Polytech Fed Lausanne Risk Analyt & Optimizat Chair Lausanne Switzerland

We consider linear programming (LP) problems in infinite dimensional spaces that are in general computationally intractable. Under suitable assumptions, we develop an approximation bridge from the infinite dimensional LP to tractable finite convex programs in which the performance of the approximation is quantified explicitly. To this end, we adopt the recent developments in two areas of randomized optimization and first-order methods, leading to a priori as well as a posteriori performance guarantees. We illustrate the generality and implications of our theoretical results in the special case of the long-run average cost and discounted cost optimal control problems in the context of Markov decision processes on Borel spaces. The applicability of the theoretical results is demonstrated through a fisheries management problem.

关键词： infinite dimensional linear programming Markov decision processes approximate dynamic programming randomized and convex optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal Management for Parking-Lot Electric Vehicle Charging by Two-Stage approximate dynamic programming

引用

IEEE TRANSACTIONS ON SMART GRID 2017年第4期8卷 1722-1730页

作者： Zhang, Lei Li, Yaoyu Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA Walmart Labs Sunnyvale CA 94085 USA Univ Texas Dallas Dept Mech Engn Richardson TX 75080 USA

This paper targets the day-time charging scenario for plug-in electric vehicles at parking-lots near commercial places, where most vehicles have extended parking time. Compared with night-time charge scenarios for residential buildings, commercial building parking-lot charging during day-time feature significant stochastic vehicle arrival and departure, as well as highly dynamic electricity price. A two-stage approximate dynamic programming framework is proposed to determine the optimal charging strategy, utilizing the predicted short-term future information and long-term estimation from historical data. All the vehicles are desired to be charged to full prior to the departure time specified under constrained total charging capacity. The uncharged amount is subject to a significant penalty cost. Simulation scenarios are created by modeling the vehicle arrival behavior as Poisson process, including arrival time, departure time, and arrival state of charge. The simulation results show that the proposed method can significantly decrease the energy cost.

关键词： Charging management demand response electric vehicle approximate dynamic programming smart grid

来源：评论

学校读者我要写书评

暂无评论

Stochastic dynamic resource allocation for HIV prevention and treatment: An approximate dynamic programming approach

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2018年 118卷 423-439页

作者： Cosgun, Ozlem Buyuktahtakin, I. Esra Wichita State Univ Dept Ind & Mfg Engn Wichita KS 67260 USA New Jersey Inst Technol Dept Mech & Ind Engn Newark NJ 07102 USA

Human immunodeficiency virus (HIV) is a key global health concern, with 33 million people living with HIV worldwide and 2.7 million new infections occurring annually. To prevent the spread of this widely prevalent epidemic disease, prevention and treatment intervention strategies urgently need to be implemented. The goal of this study is to propose stochastic dynamic programming (SDP) and approximate dynamic programming (ADP) algorithms that will optimally allocate the limited intervention budget among the HIV disease compartments and determine the best set of interventions that should be applied to each disease compartment, while minimizing the number of HIV-infected and people diagnosed with acquired immune deficiency syndrome (AIDS) as well as related deaths over a multi-year planning horizon. A compartmental model is constructed and formulated as a nonstationary Markov decision process (MDP) in order to capture the progression of the disease among the highest risk group-African American/black men who have sex with men (BMSM). In order to alleviate the computational difficulties arising from the exponentially growing state space in the SDP, we propose ADP algorithms that determine the approximately optimal budget allocation policies over six years. Our results suggest a greater allocation of the limited budget to prevention measures rather than treatment interventions, such as antiretroviral therapy (ART). As opposed to traditional policies that allocate the budget only once at the beginning of the time horizon, the ADP model suggests using a dynamic proportional budget strategy, allocating the budget dynamically over a multi-period planning period as the uncertainty in disease transmission is revealed. Results show that our ADP approach provides significant increases in health benefits and cost savings in HIV prevention and intervention.

关键词： HIV epidemic disease Resource allocation Interventions approximate dynamic programming Stochastic dynamic programming Disease transmission uncertainty Markov decision process (MDP)

来源：评论

学校读者我要写书评

暂无评论

approximate dynamic programming with policy-based exploration for microgrid dispatch under uncertainties

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年第PartB期142卷

作者： Das, Avijit Wu, Di Ni, Zhen Pacific Northwest Natl Lab Richland WA 99352 USA Atlantic Univ Boca Raton FL 33431 USA

approximate dynamic programming (ADP) is a promising approach for power system scheduling and dispatch under uncertainties. This paper presents an innovative ADP-based dispatch method for a microgrid with intermittent renewable generation, battery energy storage systems, and controllable distributed generators. The proposed ADP algorithm is based on a double-pass value iteration approach and takes advantage of the underlying properties of the microgrid dispatch problem. In the forward pass, decision variables are updated moving forward in time using an ��-greedy strategy to balance exploitation and exploration. In particular, an approximate optimization method is proposed to speed up exploitation. In addition to random exploration, a policy is designed to guide the algorithm to explore some promising solution space in a probabilistic manner. In the backward pass, the value function is updated moving backward in time using the trajectory of states, decisions, and outcomes of the sample path in the forward pass. The proposed method is evaluated through numerical experiments in both deterministic and stochastic environments. Case study results show that the proposed method demonstrates improved performance in both optimization gap and computation time in comparison to conventional methods.

关键词： approximate dynamic programming Battery energy storage systems Control policy design Microgrid Stochastic optimization

来源：评论

学校读者我要写书评

暂无评论

Anticipatory scheduling of synchromodal transport using approximate dynamic programming

引用

ANNALS OF OPERATIONS RESEARCH 2022年 1-35页

作者： Rivera, Arturo E. Perez Mes, Martijn R. K. Univ Twente High Tech Business & Entrepreneurship Dept POB 217 NL-7500 AE Enschede Netherlands

We study the problem of scheduling container transport in synchromodal networks considering stochastic demand. In synchromodal networks, the transportation modes can be selected dynamically given the actual circumstances and performance is measured over the entire network and over time. We model this problem as a Markov Decision Process and propose a heuristic solution based on approximate dynamic programming (ADP). Due to the multi-period nature of the problem, the one-step look-ahead perspective of the traditional approximate value-iteration approach can make the heuristic flounder and end in a local-optimum. To tackle this, we study the inclusion of Bayesian exploration using the Value of Perfect Information (VPI). In a series of numerical experiments, we show how VPI significantly improves a traditional ADP algorithm. Furthermore, we show how our proposed ADP-VPI combination achieves significant gains over common practice heuristics.

关键词： Synchromodal transport Intermodal transport Anticipatory scheduling approximate dynamic programming Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Markdown Optimization via approximate dynamic programming

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第1期6卷 64-78页

作者： Cosgun, Ozlem Kula, Ufuk Kahraman, Cengiz Fatih Univ Dept Ind Engn TR-34500 Istanbul Turkey Sakarya Univ Dept Ind Engn Sakarya Turkey Istanbul Tech Univ Dept Ind Engn TR-80626 Istanbul Turkey

We consider the markdown optimization problem faced by the leading apparel retail chain. Because of substitution among products the markdown policy of one product affects the sales of other products. Therefore, markdown policies for product groups having a significant crossprice elasticity among each other should be jointly determined. Since the state space of the problem is very huge, we use approximate dynamic programming. Finally, we provide insights on the behavior of how each product price affects the markdown policy.

关键词： approximate dynamic programming Multinomial logit model Cross-price elasticity Markdown optimization Pricing

来源：评论

学校读者我要写书评

暂无评论

Lookahead approximate dynamic programming for stochastic aircraft maintenance check scheduling optimization

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第3期299卷 814-833页

作者： Deng, Qichen Santos, Bruno F. Delft Univ Technol Air Transport & Operat Fac Aerosp Engn Delft Netherlands

This paper proposes a lookahead approximate dynamic programming methodology for aircraft maintenance check scheduling, considering the uncertainty of aircraft daily utilization and maintenance check elapsed time. It adopts a dynamic programming framework, using a hybrid lookahead scheduling policy. The hybrid lookahead scheduling policy makes the one-step optimal decision for heavy aircraft maintenance based on deterministic forecasts and then determines the light maintenance according to stochastic forecasts. The objective is to minimize the total wasted utilization interval between maintenance checks while reducing the need for additional maintenance slots. By achieving this goal, one is also reducing the number of maintenance checks and increasing aircraft availability while respecting airworthiness regulations. We validate the proposed methodology using the fleet maintenance data from a major European airline. The descriptive statistics of several test runs show that, when compared with the current practice, the proposed methodology potentially reduces the number of A-checks by 1.9%, the number of C-checks by 9.8%, and the number of additional slots by 78.3% over four years.

关键词： Scheduling approximate dynamic programming Lookahead policy Stochastic optimization

来源：评论

学校读者我要写书评

暂无评论

approximate dynamic programming Algorithms for Multidimensional Inventory Optimization Problems

引用

IFAC Proceedings Volumes 2013年第9期46卷 2015-2020页

作者： Mustafa Cimen Christopher Kirkbride Lancaster University Management School Lancaster LA1 4YX UK

An important issue in the supply chain literature concerns the optimization of inventory decisions. Single-product inventory problems are widely studied and have been optimally solved under a variety of assumptions. However, as supply chain systems become more complex, inventory decisions become more complicated for which the methods/approaches for optimizing single-product inventory systems are incapable of deriving optimal policies. Manufacturing process flexibility provides an example of such complex application areas. Interrelated products and production facilities form a highly multidimensional, non-decomposable system for which optimal policies cannot be obtained by classical methods. We propose the methodology of approximate dynamic programming (ADP) to overcome the computational challenge imposed by this multidimensionality. Incorporating a sample backup approach, ADP develops policies by utilizing only a fraction of the computations required by classical dynamic programming. However, there are no studies in the literature that optimize production decisions in a stochastic, multifactory, multiproduct inventory system of this complexity. This paper aims to explore the feasibility of ADP algorithms for this application. We present the results from a series of numerical experiments that establish the strong performance of policies developed via temporal difference ADP algorithms in comparison to optimal policies.

关键词： approximate dynamic programming dynamic programming flexible manufacturing systems inventory control Markov decision problems process flexibility simulation optimization

来源：评论

学校读者我要写书评

暂无评论

An approximate dynamic programming approachto decision making in the presence of uncertainty for surfactant-polymer flooding

引用

COMPUTATIONAL GEOSCIENCES 2014年第2期18卷 243-263页

作者： Alkhatib, Ali King, Peter Univ London Imperial Coll Sci Technol & Med London England

The least squares Monte Carlo method is a decision evaluation method that can capture the effect of uncertainty and the value of flexibility of a process. The method is a stochastic approximate dynamic programming approach to decision making. It is based on a forward simulation coupled with a recursive algorithm which produces the near-optimal policy. It relies on the Monte Carlo simulation to produce convergent results. This incurs a significant computational requirement when using this method to evaluate decisions for reservoir engineering problems because this requires running many reservoir simulations. The objective of this study was to enhance the performance of the least squares Monte Carlo method by improving the sampling method used to generate the technical uncertainties used in obtaining the production profiles. The probabilistic collocation method has been proven to be a robust and efficient uncertainty quantification method. By using the sampling methods of the probabilistic collocation method to approximate the sampling of the technical uncertainties, it is possible to significantly reduce the computational requirement of running the decision evaluation method. Thus, we introduce the least squares probabilistic collocation method. The decision evaluation considered a number of technical and economic uncertainties. Three reservoir case studies were used: a simple homogeneous model, the PUNQ-S3 model, and a modified portion of the SPE10 model. The results show that using the sampling techniques of the probabilistic collocation method produced relatively accurate responses compared with the original method. Different possible enhancements were discussed in order to practically adapt the least squares probabilistic collocation method to more realistic and complex reservoir models. Furthermore, it is desired to perform the method to evaluate high-dimensional decision scenarios for different chemical enhanced oil recovery processes using real reservoir data.

关键词： Decision making Surfactant-polymer flooding Uncertainty PCM LSM approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

approximate dynamic programming approach to efficient metro train timetabling and passenger flow control strategy with stop-skipping

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期127卷

作者： Zhang, Yunfeng Li, Shukai Yuan, Yin Zhang, Jinlei Yang, Lixing Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China

Urban metro systems continuously face high travel demand during rush hours, which brings excessive energy waste and high risk to passengers. In order to alleviate passenger congestion, improve train service levels and reduce energy consumption, a nonlinear dynamic programming (DP) model of efficient metro train timetabling and passenger flow control strategy with stop-skipping is presented, which consists of state transition equations concerning train traffic and passenger load. To overcome the curse of dimensionality, the formulated nonlinear DP problem is transformed into a discrete Markov decision process, and a novel approximate dynamic programming (ADP) approach is designed based on the lookahead policy and linear parametric value function approximation. Finally, the effectiveness of this method is verified by three groups of numerical experiments. Compared with Particle Swarm Optimization (PSO) and Simulated Annealing (SA), the designed ADP approach could obtain high-quality solutions quickly, which makes it applicable to the practical implementation of metro operations.

关键词： Metro train timetabling Passenger flow control strategy Stop-skipping approximate dynamic programming Energy consumption

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：