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IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

作者： Lee, Jay H. Wong, Weechin Georgia Inst Technol Sch Chem & Biomol Engn Atlanta GA 30332 USA

We assess the potentials of the approximate dynamic programming (ADP) approach for process control, especially as a method to complement the model predictive control (MPC) approach. In the artificial intelligence (AI) and operations research (OR) research communities, ADP has recently seen significant activities as an effective method for solving Markov decision processes (MDPs), which represent a type of multi-stage decision problems under uncertainty. Process control problems are similar to MDPs with the key difference being the continuous state and action spaces as opposed to discrete ones. In addition, unlike in other popular ADP application areas like robotics or games, in process control applications first and foremost concern should be on the safety and economics of the on-going operation rather than on efficient learning. We explore different options within ADP design, such as the pre-decision state vs. post-decision state value function, parametric vs. nonparametric value function approximator, batch-mode vs. continuous-mode learning, and exploration vs. robustness. We argue that ADP possesses great potentials, especially for obtaining effective control policies for stochastic constrained nonlinear or linear systems and continually improving them towards optimality. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Stochastic process control Stochastic dynamic programming approximate dynamic programming Dual control Constrained control

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approximate dynamic programming Decompositions for Economic Dispatch in Microgrids 11th

Approximate Dynamic Programming Decompositions for Economic ...

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11th IFAC Symposium on Control of Power and Energy Systems (CPES)

作者： Goubko, Mikhail, V Shushko, Nikita, I RAS VA Trapeznikov Inst Control Sci Moscow Russia

Three novel approximate dynamic programming algorithms based on the temporal, spatial, and spatiotemporal decomposition are proposed for the economic dispatch problem (EDP) in a distribution energy system with complex topology and many non-dispatchable renewable energy sources and energy storage systems (ESS). Computational efficiency of the proposed algorithms is compared and convergence to the optimal solution is shown in numeric experiments on the example of the two-day hourly EDP for the IEEE 33bw test network having 200+ consumers, 150+ energy storages, and 1000+ consuming devices. Copyright (C) 2022 The Authors.

关键词： approximate dynamic programming distribution network economic dispatch energy storage system energy system planning linear programming renewables' generation spatial decomposition spatiotemporal decomposition temporal decomposition

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approximate dynamic programming Based Supplementary Frequency Control of Thermal Generators in Power Systems With Large-Scale Renewable Generation Integration

Approximate Dynamic Programming Based Supplementary Frequenc...

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IEEE PES General Meeting

作者： Guo, Wentao Liu, Feng Mei, Shengwei Si, Jennie He, Dawei Harley, Ronald Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

ISBN: (纸本)9781479964154

Intermittent electricity generation from renewable sources is characterized by a wide range of fluctuations in frequency spectrum. The medium-frequency component of 0.01 Hz - 1 Hz cannot be filtered out by system inertia and automatic generation control (AGC) and thus it results in deterioration of frequency quality. In this paper, an approximate dynamic programming (ADP) based supplementary frequency controller for thermal generators is developed to attenuate renewable generation fluctuation in medium-frequency range. A policy iteration based training algorithm is employed for online and model-free learning. Our simulation results demonstrate that the proposed supplementary frequency controller can effectively adapt to changes in the system and provide improved frequency control. Further sensitivity analysis validates that the supplementary frequency controller significantly attenuates the dependence of frequency deviation on the medium-frequency component of renewable generation fluctuation.

关键词： approximate dynamic programming frequency control optimal adaptive control renewable generation supplementary control

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approximate dynamic programming for patient admission scheduling 12

Approximate dynamic programming for patient admission schedu...

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12th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2018

作者： De Oliveira Silva, Thiago Augusto Vancroonenburg, Wim Berghe, Greet Vanden Departamento de Engenharia de Produgao Institute- de Ciencias Exatas e Aplicadas Universidade Federal de Ouro Preto Brazil Department of Computer Science CODeS and Imec KU Leuven Ghent Technology Campus Gebroeders De Smetstraat 1 Gent9000 Belgium

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approximate dynamic programming for the United States Air Force Officer Manpower Planning Problem

Approximate Dynamic Programming for the United States Air Fo...

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作者： Kimberly S. West Air Force Institute of Technology

学位级别：硕士

The United States Air Force (USAF) makes officer accession and promotion decisions annually. Optimal manpower planning of the commissioned officer corps is vital to ensuring a well-balanced manpower system. A manpower system that is neither over-manned nor under-manned is desirable as it is most cost effective. The Air Force Officer Manpower Planning Problem (AFO-MPP) is introduced, which models officer accessions, promotions, and the uncertainty in retention rates. The objective for the AFO-MPP is to identify the policy for accession and promotion decisions that minimizes expected total discounted cost of maintaining the required number of officers in the system over an infinite time horizon. The AFO-MPP is formulated as an infinite-horizon Markov decision problem, and a policy is found using approximate dynamic programming. A least-squares temporal differencing (LSTD) algorithm is employed to determine the best approximate policies. Six computational experiments are conducted with varying retention rates and officer manning starting conditions. The policies determined by the LSTD algorithm are compared to the benchmark policy, which is the policy currently practiced by the USAF. Results indicate that when the manpower system is in a starting state with on-target numbers of officers per rank, the ADP policy outperforms the benchmark policy. When the starting state is unbalanced, with more officers in junior ranking positions, the benchmark policy outperforms the ADP policy. When the starting state is unbalanced, with more officers in senior ranking positions, there is not statistical difference between the ADP and benchmark policy. In this starting state, ADP policy has smaller variance, indicating the ADP policy is more dependable than the benchmark policy.

关键词： manpower planning officer sustainment workforce planning Markov decision processes MDP approximate dynamic programming ADP approximate policy iteration least squares temporal differences LSTD

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MULTISTAGE STOCHASTIC DECOMPOSITION: A BRIDGE BETWEEN STOCHASTIC programming AND approximate dynamic programming

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SIAM JOURNAL ON OPTIMIZATION 2014年第1期24卷 127-153页

作者： Sen, Suvrajeet Zhou, Zhihong Univ So Calif Daniel J Epstein Dept Ind & Syst Engn Los Angeles CA 90089 USA Univ Arizona SIE Dept MORE Inst Tucson AZ 85721 USA

Multistate stochastic programs pose some of the more challenging optimization problems. Because such models can become rather intractable in general, it is important to design algorithms that can provide approximations which, in the long run, yield solutions that are arbitrarily close to an optimum. In this paper, we propose such a sequential sampling method which is applicable to multistage stochastic linear programs, and we refer to it as the multistage stochastic decomposition (MSD) algorithm. This algorithm represents a dynamic extension of a regularized version of stochastic decomposition (SD). While the method allows general correlation structures, specialized streamlined versions are also possible for special cases of stagewise independent and autoregressive processes commonly incorporated in stochastic programming. As with its two-stage counterpart, the MSD algorithm is shown to provide an asymptotically optimal solution, with probability one. As a by-product of this study, we also show that SD algorithms draw upon features of both approximate dynamic programming as well as stochastic programming.

关键词： multistage stochastic programming sequential sampling stochastic decomposition approximate dynamic programming

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Solving the Top-percentile traffic routing problem by approximate dynamic programming

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IMA JOURNAL OF MANAGEMENT MATHEMATICS 2012年第4期23卷 413-434页

作者： Yang, Xinan Grothey, Andreas Univ Edinburgh Coll Sci & Engn Sch Math Operat Res & Optimizat Grp Edinburgh EH9 3JZ Midlothian Scotland

Internet Service Providers (ISPs) have the ability to route their traffic over different network providers. This study investigates the optimal routing strategy under multihoming in the case where network providers charge ISPs according to top-percentile pricing (i.e. based on the theta th highest volume of traffic shipped). We call this problem the Top-percentile Traffic Routing Problem (TpTRP). The TpTRP is a multistage stochastic optimization problem. Routing decision for every time period should be made before knowing the amount of traffic that is to be sent. The stochastic nature of the problem forms the critical difficulty of this study. Solution approaches based on Stochastic Integer programming or Stochastic dynamic programming (SDP) suffer from the curse of dimensionality, which restricts their applicability. To overcome this, we suggest to use approximate dynamic programming, which exploits the structure of the problem to construct continuous approximations of the value functions in SDP. Thus, the curse of dimensionality is largely avoided.

关键词： top-percentile pricing multihoming stochastic routing policy approximate dynamic programming

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MDP-Based Distribution Network Reconfiguration With Renewable Distributed Generation: approximate dynamic programming Approach

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IEEE TRANSACTIONS ON SMART GRID 2020年第4期11卷 3620-3631页

作者： Wang, Chong Lei, Shunbo Ju, Ping Chen, Chen Peng, Chaoyi Hou, Yunhe Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China China Southern Power Grid Power Dispatch & Control Ctr Guangzhou 510670 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

Growing penetration of renewable distributed generation, a major concern nowadays, has played a critical role in distribution system operation. This paper develops a state-based sequential network reconfiguration strategy by using a Markov decision process (MDP) model with the objective of minimizing renewable distributed generation curtailment and load shedding under operational constraints. Available power outputs of distributed generators and the system topology in each decision time are represented as Markov states, which are driven to other Markov states in next decision time in consideration of uncertainties of renewable distributed generation. For each Markov state in each decision time, a recursive optimization model with a current cost and a future cost is developed to make state-based actions, including system reconfiguration, load shedding, and distributed generation curtailment. To address the curse of dimensionality caused by enormous states and actions in the proposed model, an approximate dynamic programming (ADP) approach, including post-decision states and forward dynamic algorithm, is used to solve the proposed MDP-based model. IEEE 33-bus system and IEEE 123-bus system are used to validate the proposed model.

关键词： Markov processes Uncertainty Generators Wind power generation Load modeling Distributed power generation Topology approximate dynamic programming distributed generation distribution systems Markov decision processes reconfiguration

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dynamic Energy Management of a Microgrid Using approximate dynamic programming and Deep Recurrent Neural Network Learning

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IEEE TRANSACTIONS ON SMART GRID 2019年第4期10卷 4435-4445页

作者： Zeng, Peng Li, Hepeng He, Haibo Li, Shuhui Chinese Acad Sci Shenyang Inst Automat Lab Networked Control Syst Shenyang 110016 Liaoning Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA

This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system is developed to incorporate efficient management of energy storage system into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price. The developed dynamic energy management mechanism does not require long-term forecast and optimization or distribution knowledge of the uncertainty, but can still optimize the long-term operational costs of MGs. First, the real-time scheduling problem is modeled as a finite-horizon Markov decision process over a day. Then, approximate dynamic programming and deep recurrent neural network learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California independent system operator, a detailed simulation study is carried out to validate the effectiveness of the proposed method.

关键词： Microgrid dynamic energy management system approximate dynamic programming recurrent neural network deep learning

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dynamic assignment of a multi-skilled workforce in job shops: An approximate dynamic programming approach

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期306卷 1109-1125页

作者： Annear, Luis Mauricio Akhavan-Tabatabaei, Raha Schmid, Verena Univ Los Andes Dept Ind Engn Cra 1 18A-12 Bogota Colombia Sabanci Univ Sabanci Business Sch Istanbul Turkiye Univ Vienna Dept Business Adm Christian Doppler Lab Efficient Intermodal Transpo Oskar Morgenstern Pl 1 A-1090 Vienna Austria

We propose an approximate algorithm to dynamically assign a multi-skilled workforce to the stations of a job shop, with demand uncertainty and variability in the availability of the resources, to maximize *** proposed model is inspired by automotive glass manufacturing, where maximizing the surface area of manufactured safety glass during a given time frame is the key performance measure. We first develop the model of a traditional job shop with a set of stations, each with a particular number of machines, with distinct production performance levels, according to their utilization stage. Each product type needs to be processed on a subset of these stations according to a predefined sequence. Customers place their orders independently over time, specifying the units required of each product type. The inter-arrival of orders (demand) and processing times are assumed to be stochastic. We also suppose that the techni-cians have varied skill sets, according to which they can only work at a certain subgroup of stations, and variable availability depending on sick leave, vacations, etc. Hence, in order to maximize the predefined productivity index, the optimal assignment of technicians to the stations based on their skill sets and availability during each shift becomes a complex decision-making process. Given the stochastic and dynamic nature of this problem, we model the setting as a Markov Decision Process (MDP).Given its size, we propose to solve it using approximate dynamic programming (ADP). We address the exponential growth of the action space by using a hill-climbing algorithm for action selection. To show the performance and effectiveness of the proposed algorithm, we use real company data and compare the results of the algorithm with the current policy in use, as well as other proposed policies. Applying our proposed method resulted in an average improvement of 15% in productivity compared to the best performing benchmark policy.(c) 2022 Elsevier B.V.

关键词： dynamic programming approximate dynamic programming Workforce planning Job-shops

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