检索结果-内蒙古大学图书馆

Generalized maximum entropy estimation

JOURNAL OF MACHINE LEARNING RESEARCH 2019年 20卷

作者： Sutter, Tobias Sutter, David Esfahani, Peyman Mohajerin Lygeros, John Ecole Polytech Fed Lausanne Risk Analyt & Optimizat Chair Lausanne Switzerland Swiss Fed Inst Technol Inst Theoret Phys Zurich Switzerland Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands Swiss Fed Inst Technol Dept Elect Engn & Informat Technol Zurich Switzerland

We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. We further demonstrate how the presented scheme can be used for approximating the chemical master equation through the zero-information moment closure method, and for an approximate dynamic programming approach in the context of constrained Markov decision processes with uncountable state and action spaces.

关键词： Entropy maximization convex optimization relative entropy minimization fast gradient method approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Heuristics for the stochastic dynamic task-resource allocation problem with retry opportunities

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第1期266卷 291-303页

作者： Gulpinar, Nalan Canakoglu, Ethem Branke, Juergen Univ Warwick Warwick Business Sch Coventry CV4 7AL W Midlands England Bahgesehir Univ Ind Engn Istanbul Turkey

This paper deals with a stochastic multi-period task-resource allocation problem. A team of agents with a set of resources is to be deployed on a multi-period mission with the goal to successfully complete as many tasks as possible. The success probability of an agent assigned to a task depends on the resources available to the agent. Unsuccessful tasks can be tried again at later periods. While the problem can in principle be solved by dynamic programming, in practice this is computationally prohibitive except for tiny problem sizes. To be able to tackle also larger problems, we propose a construction heuristic that assigns agents and resources to tasks sequentially, based on the estimated marginal utility, Based on this heuristic, we furthermore propose various approximate dynamic programming approaches and an Evolutionary Algorithm. All suggested approaches are empirically compared on a number of randomly generated problem, instances. We show that the construction heuristic is very fast and provides good results. For even better results, at the expense of longer computational time, approximate dynamic programming seems a suitable alternative. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Task-resource allocation approximate dynamic programming Heuristics Retry opportunities

来源：评论

学校读者我要写书评

暂无评论

The One-Dimensional dynamic Dispatch Waves Problem

引用

TRANSPORTATION SCIENCE 2018年第2期52卷 402-415页

作者： Klapp, Mathias A. Erera, Alan L. Toriello, Alejandro Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Pontificia Univ Catolica Chile Engn Sch Santiago Chile

We study same-day delivery systems by formulating the dynamic dispatch waves problem (DDWP), which models a depot where delivery requests arrive dynamically throughout a service day. At any dispatch epoch (wave), the information available to the decision maker is (1) a set of known, open requests that remain unfulfilled, and (2) a set of potential requests that may arrive later in the service day. At each wave, the decision maker decides whether or not to dispatch a vehicle, and if so, which subset of open requests to serve, with the objective of minimizing expected vehicle operating costs and penalties for unserved requests. We consider the DDWP with a single delivery vehicle and request destinations on a line, where vehicle operating times and costs depend only on the distance between points. We propose an efficient dynamic programming approach for the deterministic variant, and leverage it to design an optimal a priori policy with predetermined routes for the stochastic case. We then show that fully dynamic policies may perform arbitrarily better than a priori ones, and propose heuristics and dual bounds for this case.

关键词： same-day delivery dynamic dispatch approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

dynamic bus substitution strategy for bunching intervention

引用

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2018年 115卷 1-16页

作者： Petit, Antoine Ouyang, Yanfeng Lei, Chao Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA

Bus headways are typically susceptible to external disturbances (e.g., due to traffic congestion, clustered passenger arrivals, and special passenger needs), which create gaps in the system that grow eventually into bunching. Although many control strategies, such as static and dynamic holding strategies, have been implemented to mitigate the effects of unreliable bus schedules, most of them would impose longer dwell times on the passengers. In this paper, we investigate the potential of an alternative bus substitution strategy that is currently implemented by some transit agencies in an ad-hoc manner. In this strategy, the agency deploys a fleet of standby buses to take over service from any early or late buses so as to contain deviations from schedule, and the intention is to impose minimum penalties on the onboard passengers. We develop a discrete-time infinite-horizon approximate dynamic programming approach to find the optimal policy to minimize the overall agency and passenger costs. It is shown through numerical examples that schedule deviations can be controlled by regularly inserting standby buses as substitutions. In some implementation scenarios, the proposed strategy holds the potential to achieve comparable performance with some of the most advanced strategies, and to outperform the conventional slack-based schedule control scheme. In light of the emerging opportunities associated with autonomous driving, the performance of the proposed strategy can become even stronger due to the reduction in costs for keeping the fleet of standby buses. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Bus bunching Transit operations approximate dynamic programming Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Model predictive control under forecast uncertainty for optimal operation of buildings with integrated solar systems

引用

SOLAR ENERGY 2018年 171卷 953-970页

作者： Liu, Xiaoqi Paritosh, Parth Awalgaonkar, Nimish M. Bilionis, Ilias Karava, Panagiota Purdue Univ Lyles Sch Civil Engn 550 Stadium Mall Dr W Lafayette IN 47907 USA Purdue Univ Ctr High Performance Bldg Ray W Herrick Labs 177 S Russell St W Lafayette IN 47907 USA Purdue Univ Sch Mech Engn 585 Purdue Mall W Lafayette IN 47907 USA

In this paper, we explore intelligent operation strategies, based on stochastic model predictive control (SMPC), for optimal utilization of solar energy in buildings with integrated solar systems. Our approach takes into account the uncertainty in solar irradiance forecast over a prediction horizon, using a new probabilistic time series autoregressive model, calibrated on the sky-cover forecast from a weather service provider. In the optimal control formulation, we model the effect of solar irradiance as non-Gaussian stochastic disturbance affecting the cost and constraints, and the nonconvex cost function is an expectation over the stochastic process. To solve this complex optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process regression, and achieves good solution quality. In the final step, we use an emulator that couples physical system models in TRNSYS with the SMPC controller developed using Python and MATLAB to evaluate the closed-loop operation of a building-integrated system with a solar-assisted heat pump coupled with radiant floor heating. For the system and climate under consideration, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a baseline well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.

关键词： Autoregressive model approximate dynamic programming Building-integrated solar energy systems Forecast uncertainty

来源：评论

学校读者我要写书评

暂无评论

OPTIMALLY REPLACING MULTIPLE SYSTEMS IN A SHARED ENVIRONMENT

引用

PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2018年第2期32卷 179-206页

作者： Abdul-Malak, David T. Kharoufeh, Jeffrey P. Univ Pittsburgh Dept Ind Engn 1025 Benedum Hall3700 O Hara St Pittsburgh PA 15261 USA

We consider the problem of optimally replacing multiple stochastically degrading systems using condition-based maintenance. Each system degrades continuously at a rate that is governed by the current state of the environment, and each fails once its own cumulative degradation threshold is reached. The objective is to minimize the sum of the expected total discounted setup, preventive replacement, reactive replacement, and downtime costs over an infinite horizon. For each environment state, we prove that the cost function is monotone nondecreasing in the cumulative degradation level. Additionally, under mild conditions, these monotonicity results are extended to the entire state space. In the case of a single system, we establish that monotone policies are optimal. The monotonicity results help facilitate a tractable, approximate model with state-and action-space transformations and a basis-function approximation of the action-value function. Our computational study demonstrates that high-quality, near-optimal policies are attainable and significantly outperform heuristic policies.

关键词： maintenance optimization approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

A Fast Technique for Smart Home Management: ADP With Temporal Difference Learning

引用

IEEE TRANSACTIONS ON SMART GRID 2018年第4期9卷 3291-3303页

作者： Keerthisinghe, Chanaka Verbic, Gregor Chapman, Archie C. Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

This paper presents a computationally efficient smart home energy management system (SHEMS) using an approximate dynamic programming (ADP) approach with temporal difference learning for scheduling distributed energy resources. This approach improves the performance of an SHEMS by incorporating stochastic energy consumption and PV generation models over a horizon of several days, using only the computational power of existing smart meters. In this paper, we consider a PV-storage (thermal and battery) system, however, our method can extend to multiple controllable devices without the exponential growth in computation that other methods such as dynamic programming (DP) and stochastic mixed-integer linear programming (MILP) suffer from. Specifically, probability distributions associated with the PV output and demand are kernel estimated from empirical data collected during the Smart Grid Smart City project in NSW, Australia. Our results show that ADP computes a solution much faster than both DP and stochastic MILP, and provides only a slight reduction in quality compared to the optimal DP solution. In addition, incorporating a thermal energy storage unit using the proposed ADP-based SHEMS reduces the daily electricity cost by up to 263% without a noticeable increase in the computational burden. Moreover, ADP with a two-day decision horizon reduces the average yearly electricity cost by a 4.6% over a daily DP method, yet requires less than half of the computational effort.

关键词： Demand response smart home energy management distributed energy resources approximate dynamic programming dynamic programming stochastic mixed-integer linear programming value function approximation temporal difference learning

来源：评论

学校读者我要写书评

暂无评论

Stochastic Optimization for Controlling the End State of Hydrogenation of Edible Oil in the Presence of Process Failure

引用

International Journal of Operations Research 2019年第4期16卷 91-104页

作者： Soham Chakraborty Pathik Mandal

Controlling the end state of a growth process by terminating it at the right time is defined as a problem of passive regulation. Within this class, the periodic sampling based methods are used in many practical situations. However, it appears that this type of passive regulation system has received no systematic attention in the past. The problem of controlling the final melting point of a batch of edible oil during hydrogenation belongs to this category. This problem is formulated in terms of two stochastic optimization problems. The first problem is solved following a scenario tree based approach, while the second one is formulated as a Stochastic dynamic Program (SDP). The SDP is solved by evaluating a quality cost function through simulation. It is shown that the performance of the hydrogenation process is expected to improve significantly under the proposed passive regulation scheme.

关键词： Simulation Hydrogenation of edible oil Growth model Control chart approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Relationship between least squares Monte Carlo and approximate linear programming

引用

OPERATIONS RESEARCH LETTERS 2017年第5期45卷 409-414页

作者： Nadarajah, Selvaprabu Secomandi, Nicola Univ Illinois Coll Business Adm 601 South Morgan St Chicago IL 60607 USA Carnegie Mellon Univ Tepper Sch Business 5000 Forbes Ave Pittsburgh PA 15213 USA

Least squares Monte Carlo (LSM) is commonly used to manage and value early or multiple exercise financial or real options. Recent research in this area has started applying approximate linear programming (ALP) and its relaxations, which aim at addressing a possible ALP drawback. We show that regress-later LSM is itself an ALP relaxation that potentially corrects this ALP shortcoming. Our analysis consolidates two streams of research and supports using this LSM version rather than ALP on the considered models. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Markov decision processes approximate dynamic programming Least squares Monte Carlo approximate linear programming Financial and real options Energy storage

来源：评论

学校读者我要写书评

暂无评论

Budgeting Time for dynamic Vehicle Routing with Stochastic Customer Requests

引用

TRANSPORTATION SCIENCE 2018年第1期52卷 20-37页

作者： Ulmer, Marlin W. Mattfeld, Dirk C. Koester, Felix Tech Univ Carolo Wilhelmina Braunschweig D-38106 Braunschweig Germany

Parcel services route vehicles to pick up parcels in the service area. Pickup requests occur dynamically during the day and are unknown before their actual request. Because of working hour restrictions, service vehicles only have a limited time to serve dynamic requests. As a result, not all requests can be confirmed. To achieve an overall high number of confirmed requests, dispatchers have to budget their time effectively by anticipating future requests. To determine the value of a decision, i. e., the expected number of future confirmations given a point of time and remaining free time budget, we present an anticipatory time budgeting heuristic (ATB) drawing on methods of approximate dynamic programming. ATB frequently simulates a problem's realization to subsequently approximate the values for every vector of point of time and free time budget to achieve an approximation of an optimal decision policy. Since the number of vectors is vast, we introduce the dynamic lookup table (DLT), a general approach adaptively partitioning the vector space to the approximation process. Compared with state- of- the- art benchmark heuristics, ATB allows an effective use of the time budget resulting in anticipatory decision making and high solution quality. Additionally, the DLT significantly strengthens and accelerates the approximation process.

关键词： dynamic vehicle routing stochastic customer requests time budget subset selection approximate dynamic programming dynamic lookup table

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：