检索结果-内蒙古大学图书馆

On-line Energy Allocation Based on approximate dynamic programming for Iron and Steel Industry

ISIJ INTERNATIONAL 2016年第12期56卷 2214-2223页

作者： Zhang, Yanyan Guo, Qingxin Tang, Lixin Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Inst Ind Engn & Logist Optimizat Shenyang 110819 Peoples R China

Energy allocation in iron and steel industry is the assignment of available energy to various production users. With the increasing price of energy, a perfect allocation plan should ensure that nothing gets wasted and no shortage. This is challenging because the energy demand is dynamic due to the changes of orders, production environment, technological level, etc. This paper try to realize on-line energy resources allocation under the situation of dynamic production plan and environment based on typical energy consumption process of steel enterprises. Without definite analytical model, it is a tough task to make the energy allocation plan tracks the dynamic change of production environment in real time. This paper proposes to deal with dynamic energy allocation problem by interactive learning with time-varying environment using approximate dynamic programming method. The problem is formulated as a dynamic model with variable right-hand items, which is an updated energy demand obtained by on-line learning. Reinforcement learning method is designed to learn the energy consumption principle from the historical data to predict energy consumption level corresponding to current production environment and the production plan in future horizon. Using the prediction results, on-line energy allocation plan is made and its performance is demonstrated by comparison with static allocation method.

关键词： approximate dynamic programming energy prediction on-line energy allocation

来源：评论

学校读者我要写书评

暂无评论

An approximate dynamic programming method for multi-input multi-output nonlinear system

引用

OPTIMAL CONTROL APPLICATIONS & METHODS 2013年第1期34卷 80-95页

作者： Huang, Zhijian Ma, Jie Huang, He Shanghai Jiao Tong Univ Ocean Engn State Key Lab Shanghai 200240 Peoples R China Shanghai Maritime Univ Merchant Marine Coll Shanghai 200135 Peoples R China IMS Automot Elect Syst Co Ltd Shanghai 200335 Peoples R China

approximate dynamic programming is a useful tool in solving multi-stage decision optimal control problems. In this work, we first promote the action-dependent heuristic dynamic programming method to multi-input multi-output control system by extending its action network to multi-output form. The detailed derivation is also given. We then apply this method to the fluctuation control of a spark engine idle speed. An engine idling model is set up to verify the control effect of this method. Results here show that this method requires several iterations to suppress unbalanced combustion by manipulating spark ignition timing. This method provides an alternative for a simpler multi-input multi-output approximate dynamic programming scheme. Moreover, it has a faster iteration convergence effect. The derivation of this method also has a rigorous mathematical basis. Although illustrated for engines, this control system framework should also be applicable to general multi-input multi-output nonlinear system. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： approximate dynamic programming adaptive critic designs fluctuation control of an engine idling neural network multi-input multi-output system

来源：评论

学校读者我要写书评

暂无评论

Computationally efficient approximate dynamic programming for multi-site production capacity planning with uncertain demands

引用

FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2023年第3期35卷 797-837页

作者： Cheng, Chen-Yang Pourhejazy, Pourya Chen, Tzu-Li Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 10608 Taiwan UiT Arctic Univ Norway Dept Ind Engn Lodve Langsgate 2 N-8514 Narvik Norway

With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile and diverse. In this situation, capacity adjustment as an operational strategic decision plays a major role to ensure supply chain responsiveness while maintaining costs at a reasonable norm. This study contributes to the literature by developing computationally efficient approximate dynamic programming approaches for production capacity planning considering uncertainties and demand interdependence in a multi-factory multi-product supply chain setting. For this purpose, the k-Nearest-Neighbor-based approximate dynamic programming and the Rolling-Horizon-based approximate dynamic programming are developed to enable real-time decision support while ensuring the robustness of the outcomes in stochastic decision environments. Given the market volatilities in the Thin Film Transistor-Liquid Crystal Display industry, a real case from this sector is investigated to evaluate the applicability of the developed approach and provide insights for other industry situations. The developed method is less complex to implement, and numerical experiments showed that it is also computationally more efficient compared to Stochastic dynamic programming.

关键词： Production management Capacity planning approximate dynamic programming Stochastic dynamic programming Optimization

来源：评论

学校读者我要写书评

暂无评论

FINITE-HORIZON ε-OPTIMAL TRACKING CONTROL OF DISCRETE-TIME LINEAR SYSTEMS USING ITERATIVE approximate dynamic programming

引用

ASIAN JOURNAL OF CONTROL 2015年第1期17卷 176-189页

作者： Tan, Fuxiao Luo, Bin Guan, Xinping Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei Anhui Peoples R China Fuyang Teachers Coll Sch Comp & Informat Anhui Fuyang Peoples R China Shanghai Jiao Tong Univ Sch Elect & Elect Engn Shanghai 200030 Peoples R China

In this paper, we propose a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time linear systems. In applying the iterative approximate dynamic programming (ADP) algorithm to determine the optimal tracking control law for linear systems, we need finite iterations to obtain the result in practical applications, instead of infinite iterations. An epsilon-error bound is introduced into the ADP algorithm to determine the number of iteration steps. The approximation optimal tracking control law will approach the solution of the Hamilton-Jacobi-Bellman (HJB) equation through a self-adaptive iteration within the given value of epsilon-error bound. epsilon error bound is used to stop the iteration process. So, we can obtain the epsilon-approximation tracking control law in a finite number of iterations. Nevertheless, different epsilon will produce different control performances. Furthermore, we will find an optimal epsilon error bound, which can obtain optimal performance of the ADP algorithm on the basis of the controlled system tracking the desired trajectory. One example is included to complete the ADP algorithm under different error bounds. From the simulation results, we can find the optimal epsilon error bound. Finally, the simulation validates the efficiency of the proposed algorithm.

关键词： Error bound approximate dynamic programming neural networks optimal tracking control finite iterations

来源：评论

学校读者我要写书评

暂无评论

An approximate dynamic programming approach to project scheduling with uncertain resource availabilities

引用

APPLIED MATHEMATICAL MODELLING 2021年 97卷 226-243页

作者： Xie, Fang Li, Haitao Xu, Zhe Shandong Technol & Business Univ Coll Finance Yantai 264005 Peoples R China Shandong Technol & Business Univ Collaborat Res Ctr Financial Serv Transformat & I Yantai Peoples R China Univ Missouri Coll Business Adm St Louis MO 63121 USA Beihang Univ Coll Econ & Management Beijing 100191 Peoples R China

We study the stochastic resource-constrained project scheduling problem with uncertain resource availability, called SRCPSP-URA, and model it as a sequential decision problem. A new Markov decision process (MDP) model is developed for the SRCPSP-URA. It dynamically and adaptively determines not only which activity to start at a stage, but also which to interrupt and delay when there is not sufficient resource capacity. To tackle the curse-of-dimensionality of an exact solution approach, we devise and implement a rollout-based approximate dynamic programming (ADP) algorithm with priority-rule heuristic as the base policy, for which theoretical sequential improvement property is proved. Computational results show that with moderately more computational time, our ADP algorithm significantly outperforms the priority-rule heuristics for test instances up to 120 activities. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Stochastic resource-constrained project scheduling Uncertain resource availability Markov decision process approximate dynamic programming Rollout policy

来源：评论

学校读者我要写书评

暂无评论

A simulation-based approximate dynamic programming approach to dynamic and stochastic resource-constrained multi-project scheduling problem

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期315卷 454-469页

作者： Satic, U. Jacko, P. Kirkbride, C. Lancaster Univ Management Sch Lancaster LA1 4YX England Abdullah Gul Univ Fac Engn TR-38080 Kayseri Turkiye Berry Consultants Abingdon OX14 5EG England

We consider the dynamic and stochastic resource -constrained multi -project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing. The problem is modelled as an infinite -horizon discrete -time Markov decision process and seeks to maximise the expected discounted long -run profit. We use an approximate dynamic programming algorithm (ADP) with a linear approximation model which can be used for online decision making. Our approximation model uses project elements that are easily accessible by a decision -maker, with the model coefficients obtained offline via a combination of Monte Carlo simulation and least squares estimation. Our numerical study shows that ADP often statistically significantly outperforms the optimal reactive baseline algorithm (ORBA). In experiments on smaller problems however, both typically perform suboptimally compared to the optimal scheduler obtained by stochastic dynamic programming. ADP has an advantage over ORBA and dynamic programming in that ADP can be applied to larger problems. We also show that ADP generally produces statistically significantly higher profits than common algorithms used in practice, such as a rule -based algorithm and a reactive genetic algorithm.

关键词： Project scheduling Markov decision processes approximate dynamic programming dynamic resource allocation dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Global optimality of approximate dynamic programming and its use in non-convex function minimization

引用

APPLIED SOFT COMPUTING 2014年第0期24卷 291-303页

作者： Heydari, Ali Balakrishnan, S. N. South Dakota Sch Mines & Technol Dept Mech Engn Rapid City SD 57701 USA Missouri Univ Sci & Technol Mech & Aerosp Engn Dept Rolla MO 65409 USA

This study investigates the global optimality of approximate dynamic programming (ADP) based solutions using neural networks for optimal control problems with fixed final time. Issues including whether or not the cost function terms and the system dynamics need to be convex functions with respect to their respective inputs are discussed and sufficient conditions for global optimality of the result are derived. Next, a new idea is presented to use ADP with neural networks for optimization of non-convex smooth functions. It is shown that any initial guess leads to direct movement toward the proximity of the global optimum of the function. This behavior is in contrast with gradient based optimization methods in which the movement is guided by the shape of the local level curves. Illustrative examples are provided with single and multi-variable functions that demonstrate the potential of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： approximate dynamic programming Fixed final time optimal control Neural networks Non-convex function minimization

来源：评论

学校读者我要写书评

暂无评论

Stochastic Transactive Control for Electric Vehicle Aggregators Coordination: A Decentralized approximate dynamic programming Approach

引用

IEEE TRANSACTIONS ON SMART GRID 2020年第5期11卷 4261-4277页

作者： Pan, Zhenning Yu, Tao Li, Jie Qu, Kaiping Chen, Lvpeng Yang, Bo Guo, Wenxin South China Univ Technol Coll Elect Power Guangzhou 510640 Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Yunnan Peoples R China Guangdong Power Grid Co Ltd Elect Dispatch & Control Ctr Guangzhou 510600 Peoples R China

With the increasing penetration of renewable energy sources and electric vehicles (EVs), coordinated operation of EV aggregator (EVA) and distribution system operation (DSO) becomes a complex multistage and multidimensional stochastic problem. The motivation behind this paper is to develop a decentralized mechanism to offer a computationally efficient and almost optimal on-line policy for such problem under the framework of transactive energy control (TEC). First, a heterogeneous decomposition-based TEC is designed by utilizing the heterogeneous interactions between DSO and EVA. Then, a decentralized approximate dynamic programming-based algorithm is proposed to offer almost optimal dynamic TEC policies. A decentralized value function approximation approach with temporal difference learning is further employed for entities to learn how to utilize the flexibilities of their resources in response to the stochastic exogenous information. Case studies demonstrate the effectiveness of the proposed algorithm in terms of optimality, robustness, computation efficiency, and scalability.

关键词： Electric vehicle transactive energy control heterogeneous decomposition decentralized optimization stochastic optimization approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Demand side energy management of EV charging stations by approximate dynamic programming

引用

ENERGY CONVERSION AND MANAGEMENT 2019年 196卷 878-890页

作者： Wu, Yu Ravey, Alexandre Chrenko, Daniela Miraoui, Abdellatif Univ Bourgogne Franche Comte UTBM CNRS FEMTO ST Inst Belfort France Univ Bourgogne Franche Comte UTBM CNRS FCLAB Belfort France

The high operation cost of the EV charging station (EVCS) is a severe challenge for the development of electric vehicles, which lead to the general shortage of the EVCS. In order to reduce the operation costs of the EVCS, an approximate dynamic programming (ADP) based energy management system (EMS) is proposed for the EVCS equipped with multiple types of chargers (EVCS-MTC). A fuzzy logic guiding system has been designed to allocate each vehicle an appropriate charging spot based on its charging urgency. Multiple EVs can acquire the charging service through a common charger in the EVCS-MTC. In the proposed EMS, the approximate dynamic programming (ADP) and the evolution algorithm (EA) are combined to determine the optimal charging start time for each EV. This characteristic provides the charging device with the maximum autonomy to select the preferred flexible charging pattern, which can prolong the battery lifetime and reduce the communication requirements of the control system. With taking the dynamic electricity price and uncertain future charging demand into account, the proposed EMS can achieve a total cost reduction of over 50% compared with the conventional charging scheme in the numerical studies.

关键词： EV charging station Demand side energy management approximate dynamic programming Flexible charging pattern Multiple types of EV charger

来源：评论

学校读者我要写书评

暂无评论

A new strategy based on approximate dynamic programming to maximize the net revenue of IaaS cloud providers with limited resources

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 101卷 1168-1186页

作者： Yue, Wenying Du, Peng Quan, Xiongwen Chen, Ying Hebei Univ Coll Math & Informat Sci Hebei Key Lab Machine Learning & Computat Intelli Baoding 071002 Peoples R China Tianjin Univ Technol Sch Management Tianjin Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China China Mobile Online Serv Co LTD Zhejiang Branch Hangzhou Zhejiang Peoples R China

With rapid development of cloud computing, more conventional Internet Data Centers get involved into this business model. Providers of small and medium sized data centers have relatively limited computing resources compared to global IaaS providers such as Amazon. The demand for computing at these service providers may be above the available capacity especially during peak shopping periods (e.g. mother's day or singles' day). However, it is undesirable to blindly expand capacity, which will not only incur the purchase cost of new machines but also the maintenance costs associated with resource over-provisioning. For these service providers, the most important thing is how to effectively manage resources during peak demand periods to maximize their net revenue. In this context, we focus on the joint admission control and virtual machine placement problem, and propose a dynamic optimization model considering that customers dynamically create and terminate virtual machines in cloud computing environment. In terms of problem complexity, the developed dynamic optimization model typically suffers from "curse of dimensionality" and is computationally intractable. So we resort to approximate dynamic programming framework and propose a new strategy to yield a tractable model for real-time decision-making. Extensive simulations are conducted to compare our proposed strategy with other strategies including existing methods of virtual machine placement. The simulation results demonstrate that our customized strategy achieves substantial revenue improvements and the improvement is more significant as resource provisioning is tighter. In addition, it can be scalable to solve large cases up to 1000 physical machines and yield a real-time decision in a reasonable time. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Cloud computing Net revenue maximization Admission control Virtual machine placement approximate dynamic programming

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：