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A Unifying approximate dynamic programming Model for the Economic Lot Scheduling Problem

MATHEMATICS OF OPERATIONS RESEARCH 2014年第2期39卷 374-402页

作者： Adelman, Daniel Barz, Christiane Univ Chicago Booth Sch Business Chicago IL 60637 USA Univ Calif Los Angeles Anderson Sch Management Los Angeles CA 90095 USA

We formulate the well-known economic lot scheduling problem (ELSP) with sequence-dependent setup times and costs as a semi-Markov decision process. Using an affine approximation of the bias function, we obtain a semi-infinite linear program determining a lower bound for the minimum average cost rate. Under a very mild condition, we can reduce this problem to a relatively small convex quadratically constrained linear problem by exploiting the structure of the objective function and the state space. This problem is equivalent to the lower bound problem derived by Dobson [Dobson G (1992) The cyclic lot scheduling problem with sequence-dependent setups. Oper. Res. 40:736-749] and reduces to the well-known lower bound problem introduced in Bomberger [Bomberger EE (1966) A dynamic programming approach to a lot size scheduling problem. Management Sci. 12: 778-784] for sequence-dependent setups. We thus provide a framework that unifies previous work, and opens new paths for future research on tighter lower bounds and dynamic heuristics.

关键词： economic lot scheduling problem sequence-dependent setups semi-Markov decision process approximate dynamic programming

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

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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

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An approximate dynamic programming Model for Link Scheduling in WMNs with Gateway Design Constraint 9

An Approximate Dynamic Programming Model for Link Scheduling...

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9th International Conference on Communications and Networking in China (CHINACOM 2014)

作者： Chen, Chien-Liang Liu, Wan-Yu Chang, Shu-Huai Lin, Chun-Cheng Aletheia Univ Dept Comp Sci & Informat Engn New Taipei 251 Taiwan Aletheia Univ Dept Tourism Informat New Taipei 251 Taiwan Natl Chiao Tung Univ Dept Ind Engn & Management Hsinchu 300 Taiwan

ISBN: (纸本)9781479959709

The design objective of wireless mesh networks is generally focused on transmitting data packets as many as possible in a given time. Wireless mesh network models may be categorized into many different types according to different programming objectives, wherein the link scheduling model achieves the purpose of effective transmission of packets by certain transmission constraints and the programming of the open status of packet transmission links in each stage. In this paper, the approximate dynamic programming approach for a new wireless mesh network scheduling model having node programming information saving locations and network link gateway designs is orchestrated. The experiment results show that, in addition to maintaining many wireless network characteristics, the scheduling algorithm is effectively executed and approximate dynamic programming effectively simulates dynamic programming and has performances superior to genetic algorithm.

关键词： wireless mesh network link scheduling gateway genetic algorithm dynamic programming approximate dynamic programming

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Transfer-based approximate dynamic programmingfor Rolling Security-constrained Unit Commitment with Uncertainties

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Protection and Control of Modern Power Systems 2024年第5期9卷 42-53页

作者： Jianquan Zhu Kai Zeng Jiajun Chen Wenmeng Zhao Wenhao Liu Wenkai Zhu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China Electric Power Research Institute China Southern Power GridGuangzhou 510700China

This paper studies the rolling security-constrained unit commitment(RSCUC)problem with AC power flow and *** this NP-hard problem,it is modeled as a Markov decision process,which is then solved by a transfer-based approximate dynamic programming(TADP)algorithm proposed in this *** from traditional approximate dynamic programming(ADP)algorithms,TADP can obtain the commitment states of most units in advance through a decision transfer technique,thus reducing the action space of TADP ***,compared with traditional ADP algorithms,which require to determine the commitment state of each unit,TADP only needs determine the unit with the smallest on-state probability among all on-state units,thus further reducing the action *** proposed algorithm can also prevent the iter-ative update of value functions and the reliance on rolling forecast information,which makes more sense in the rolling decision-making process of ***,nu-merical simulations are carried out on a modified IEEE 39-bus system and a real 2778-bus system to demonstrate the effectiveness of the proposed algorithm.

关键词： Rolling security-constrained unit com-mitment approximate dynamic programming decision transfer probability-based decision priority criterion uncertainty

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A Comparison of approximate dynamic programming Techniques on Benchmark Energy Storage Problems: Does Anything Work?

A Comparison of Approximate Dynamic Programming Techniques o...

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IEEE Symposium on Adaptive dynamic programming and Reinforcement Learning (ADPRL)

作者： Jiang, Daniel R. Pham, Thuy V. Powell, Warren B. Salas, Daniel F. Scott, Warren R.

ISBN: (纸本)9781479945528

As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for energy storage is becoming increasingly important. These sequential decision problems are often modeled as stochastic dynamic programs, but when the state space becomes large, traditional (exact) techniques such as backward induction, policy iteration, or value iteration quickly become computationally intractable. approximate dynamic programming (ADP) thus becomes a natural solution technique for solving these problems to near-optimality using significantly fewer computational resources. In this paper, we compare the performance of the following: various approximation architectures with approximate policy iteration (API), approximate value iteration (AVI) with structured lookup table, and direct policy search on a benchmarked energy storage problem (i.e., the optimal solution is computable).

关键词： dynamic programming energy storage power engineering computing power system management renewable energy sources table lookup ADP API AVI approximate dynamic programming approximate policy iteration approximate value iteration backward induction dynamic programming techniques energy storage control policy lookup table natural solution technique solar energy stochastic dynamic programs wind energy Approximation algorithms Benchmark testing Energy storage Equations Function approximation Mathematical model Table lookup dynamic programming energy storage Power system management AVI Benchmark testing Power engineering computing function approximation Approximation algorithms Adenosine Diphosphate Automatic data processing Renewable energy renewable energy sources Wind energy Solar Energy

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The Operation Optimization Model of Pumped-Hydro Power Storage Station Based on approximate dynamic programming

The Operation Optimization Model of Pumped-Hydro Power Stora...

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International Conference on Power System Technology (PowerCon)

作者： Liang, Zhencheng Li, Yu Wei, Hua Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Guangxi Key Lab Power Syst Optimizat & Energy Tec Nanning Peoples R China

ISBN: (纸本)9781479950324

Based on the hypothesis that pumped-hydro power storage (PHPS) station is available for multi-day optimization and adjustment, the paper has proposed a long-term operation optimization model of PHPS station based on approximate dynamic programming (ADP). In this multistage decision model, across the stages, value function approximation (VFA) of the reservoir energy storage was used to keep the overall optimization characteristics;During the stages, generated energy & generating periods, and electricity consumption for pumping & pumping periods are used as decision variables to conduct daily optimization operation. The paper got the approximate optimal solution through iteration solution decision variable and value function so as to avoid "curse of dimensionality" in conventional multistage decision model. According to the experiment, the ADP-based model can accurately describe the long-term operation modes of PHPS station, and its calculation methods are more appropriate for this kind of large-scale optimized decision problem than dynamic programming (DP) and conventional mathematic planning methods.

关键词： pumped-hydro power storage station long-term operation optimization value function approximation approximate dynamic programming

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Self-learning PD Algorithms Based on approximate dynamic programming for Robot Motion Planning

Self-learning PD Algorithms Based on Approximate Dynamic Pro...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yang, Huiyuan Guo, Qi Xu, Xin Lian, Chuanqiang Natl Univ Def Technol Coll Mechatron & Automat Inst Unmanned Syst Changsha Hunan Peoples R China

ISBN: (纸本)9781479914845

Motion planning is a key technology of the navigation and control for mobile robots. However, when considering the complexity of exterior environment and mobile robot's kinematics and dynamics, the motion planning results obtained by some traditional methods are often hard to optimize. In this paper, we propose two self-learning PD algorithms to solve motion planning for mobile robots. We firstly utilize a virtual Proportional Derivative (PD) control strategy to transform the motion planning problem into an optimization problem of the virtual control policy. Afterwards, two approximate dynamic programming algorithms, which are the Least Squares Policy Iteration (LSPI) algorithm and the Dual Heuristic programming (DHP) algorithm, are incorporated into the virtual control strategy to tune the PD parameters automatically, namely the LSPI-PD algorithm and the DHP-PD algorithm. Simulations have been performed to validate the effectiveness of the two algorithms, where the LSPI-PD algorithm is suitable for solving problems with discrete action spaces while the DHP-PD algorithm has an advantage in solving problems with continuous action spaces.

关键词： approximate dynamic programming mobile robot motion planning

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Online Optimization of Collaborative Web Service QoS Prediction Based on approximate dynamic programming

Online Optimization of Collaborative Web Service QoS Predict...

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International Conference on Identification, Information and Knowledge in the Internet of Things

作者： Luo, Xiong Luo, Hao Chang, Xiaohui Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781479980031

More recently, with the increasing demand of web services on the World Wide Web used in the Internet of Things (IoTs), there has been a growing interest in the study of efficient web service quality evaluation approaches through the use of prediction strategies to obtain accurate quality-of-service (QoS) values. The unpredictable network environment imposes very challenging obstacles to web service QoS prediction. Most of the traditional web service QoS prediction approaches are implemented only using a set of static model parameters with the help of designer's priori knowledge. Unlike the traditional QoS prediction approaches, our algorithm in this paper is realized by incorporating approximate dynamic programming (ADP)-based online parameter tuning strategy into the QoS prediction approach. Through online learning and optimization, the proposed approach implements the QoS prediction with automatic parameter tuning capability, and the prior knowledge or identification of the prediction model is not required. The near-optimal performance of QoS prediction can therefore be achieved. Experiment studies are carried out to demonstrate the effectiveness of the proposed ADP-based prediction approach.

关键词： approximate dynamic programming QoS prediction online learning parameter tuner

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An approximate dynamic programming Approach for Path Following Control of an Autonomous Vehicle 11

An Approximate Dynamic Programming Approach for Path Followi...

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11th World Congress on Intelligent Control and Automation

作者： Zhao, Kun Wang, Jian Xu, Xin Huang, Zhenhua Natl Univ Def Technol Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479958252

Intelligent control of autonomous vehicles has been an important research topic due to the model uncertainties and complexities of vehicle dynamics. In this paper, we proposed an approximate dynamic programming (ADP) approach for path following control of an autonomous vehicle. The idea is to use the Dual Heuristic programming (DHP) algorithm, which is an efficient class of ADP methods, to directly control the steer angle of the vehicle's wheels. In order to perform data-driven simulation studies, we established the kinematic model of the vehicle and the model of the steering wheels. Then, the DHP algorithm was designed for path following control by using a Markov decision process model. Simulation results illustrated that the proposed method can achieve good performance for the path following control problem.

关键词： Autonomous Vehicles Dual Heuristic programming approximate dynamic programming Path Following Control

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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 Power and Energy Society General Meeting

作者： Wentao Guo Feng Liu Shengwei Mei Jennie Si Dawei He Ronald Harley State Key Lab. of Power Systems Dept. of Electrical Engineering Tsinghua University Dept. of Electrical Engineering Arizona State University School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9781479964161

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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