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Journal articles on the topic 'Approximate dynamic programming'

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Published: 4 June 2021
Last updated: 20 February 2023

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1

Powell, Warren B. "Perspectives of approximate dynamic programming." Annals of Operations Research 241, no. 1-2 (2012): 319–56. http://dx.doi.org/10.1007/s10479-012-1077-6.

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2

Kulkarni, Sameer, Rajesh Ganesan, and Lance Sherry. "Dynamic Airspace Configuration Using Approximate Dynamic Programming." Transportation Research Record: Journal of the Transportation Research Board 2266, no. 1 (2012): 31–37. http://dx.doi.org/10.3141/2266-04.

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On the basis of weather and high traffic, the Next Generation Air Transportation System envisions an airspace that is adaptable, flexible, controller friendly, and dynamic. Sector geometries, developed with average traffic patterns, have remained structurally static with occasional changes in geometry due to limited forming of sectors. Dynamic airspace configuration aims at migrating from a rigid to a more flexible airspace structure. Efficient management of airspace capacity is important to ensure safe and systematic operation of the U.S. National Airspace System and maximum benefit to stakeholders. The primary initiative is to strike a balance between airspace capacity and air traffic demand. Imbalances in capacity and demand are resolved by initiatives such as the ground delay program and rerouting, often resulting in systemwide delays. This paper, a proof of concept for the dynamic programming approach to dynamic airspace configuration by static forming of sectors, addresses static forming of sectors by partitioning airspace according to controller workload. The paper applies the dynamic programming technique to generate sectors in the Fort Worth, Texas, Air Route Traffic Control Center; compares it with current sectors; and lays a foundation for future work. Initial results of the dynamic programming methodology are promising in terms of sector shapes and the number of sectors that are comparable to current operations.
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3

de Farias, D. P., and B. Van Roy. "The Linear Programming Approach to Approximate Dynamic Programming." Operations Research 51, no. 6 (2003): 850–65. http://dx.doi.org/10.1287/opre.51.6.850.24925.

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4

Logé, Frédéric, Erwan Le Pennec, and Habiboulaye Amadou-Boubacar. "Intelligent Questionnaires Using Approximate Dynamic Programming." i-com 19, no. 3 (2020): 227–37. http://dx.doi.org/10.1515/icom-2020-0022.

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Abstract Inefficient interaction such as long and/or repetitive questionnaires can be detrimental to user experience, which leads us to investigate the computation of an intelligent questionnaire for a prediction task. Given time and budget constraints (maximum q questions asked), this questionnaire will select adaptively the question sequence based on answers already given. Several use-cases with increased user and customer experience are given. The problem is framed as a Markov Decision Process and solved numerically with approximate dynamic programming, exploiting the hierarchical and episodic structure of the problem. The approach, evaluated on toy models and classic supervised learning datasets, outperforms two baselines: a decision tree with budget constraint and a model with q best features systematically asked. The online problem, quite critical for deployment seems to pose no particular issue, under the right exploration strategy. This setting is quite flexible and can incorporate easily initial available data and grouped questions.
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5

Ryzhov, Ilya O., Martijn R. K. Mes, Warren B. Powell, and Gerald van den Berg. "Bayesian Exploration for Approximate Dynamic Programming." Operations Research 67, no. 1 (2019): 198–214. http://dx.doi.org/10.1287/opre.2018.1772.

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6

Maxwell, Matthew S., Mateo Restrepo, Shane G. Henderson, and Huseyin Topaloglu. "Approximate Dynamic Programming for Ambulance Redeployment." INFORMS Journal on Computing 22, no. 2 (2010): 266–81. http://dx.doi.org/10.1287/ijoc.1090.0345.

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7

Coşgun, Özlem, Ufuk Kula, and Cengiz Kahraman. "Markdown Optimization via Approximate Dynamic Programming." International Journal of Computational Intelligence Systems 6, no. 1 (2013): 64–78. http://dx.doi.org/10.1080/18756891.2013.754181.

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8

El-Rayes, Khaled, and Hisham Said. "Dynamic Site Layout Planning Using Approximate Dynamic Programming." Journal of Computing in Civil Engineering 23, no. 2 (2009): 119–27. http://dx.doi.org/10.1061/(asce)0887-3801(2009)23:2(119).

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9

Lee, Jay H., and Wee Chin Wong. "Approximate dynamic programming approach for process control." IFAC Proceedings Volumes 42, no. 11 (2009): 26–35. http://dx.doi.org/10.3182/20090712-4-tr-2008.00006.

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10

McGrew, James S., Jonathon P. How, Brian Williams, and Nicholas Roy. "Air-Combat Strategy Using Approximate Dynamic Programming." Journal of Guidance, Control, and Dynamics 33, no. 5 (2010): 1641–54. http://dx.doi.org/10.2514/1.46815.

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11

Bertsekas, Dimitri P. "Separable Dynamic Programming and Approximate Decomposition Methods." IEEE Transactions on Automatic Control 52, no. 5 (2007): 911–16. http://dx.doi.org/10.1109/tac.2007.895901.

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12

Dai, J. G., and Pengyi Shi. "Inpatient Overflow: An Approximate Dynamic Programming Approach." Manufacturing & Service Operations Management 21, no. 4 (2019): 894–911. http://dx.doi.org/10.1287/msom.2018.0730.

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13

Lee, Jay H., and Weechin Wong. "Approximate dynamic programming approach for process control." Journal of Process Control 20, no. 9 (2010): 1038–48. http://dx.doi.org/10.1016/j.jprocont.2010.06.007.

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14

Heydari, Ali. "Revisiting Approximate Dynamic Programming and its Convergence." IEEE Transactions on Cybernetics 44, no. 12 (2014): 2733–43. http://dx.doi.org/10.1109/tcyb.2014.2314612.

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15

Wang, Yang, Brendan O'Donoghue, and Stephen Boyd. "Approximate dynamic programming via iterated Bellman inequalities." International Journal of Robust and Nonlinear Control 25, no. 10 (2014): 1472–96. http://dx.doi.org/10.1002/rnc.3152.

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16

Hulshof, Peter J. H., Martijn R. K. Mes, Richard J. Boucherie, and Erwin W. Hans. "Patient admission planning using Approximate Dynamic Programming." Flexible Services and Manufacturing Journal 28, no. 1-2 (2015): 30–61. http://dx.doi.org/10.1007/s10696-015-9219-1.

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17

Epelman, Marina, Archis Ghate, and Robert L. Smith. "Sampled fictitious play for approximate dynamic programming." Computers & Operations Research 38, no. 12 (2011): 1705–18. http://dx.doi.org/10.1016/j.cor.2011.01.023.

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18

Cervellera, Cristiano, and Marco Muselli. "Efficient sampling in approximate dynamic programming algorithms." Computational Optimization and Applications 38, no. 3 (2007): 417–43. http://dx.doi.org/10.1007/s10589-007-9054-8.

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19

Buşoniu, Lucian, Damien Ernst, Bart De Schutter, and Robert Babuška. "Approximate dynamic programming with a fuzzy parameterization." Automatica 46, no. 5 (2010): 804–14. http://dx.doi.org/10.1016/j.automatica.2010.02.006.

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20

Cai, Yongyang, Kenneth L. Judd, Thomas S. Lontzek, Valentina Michelangeli, and Che-Lin Su. "A NONLINEAR PROGRAMMING METHOD FOR DYNAMIC PROGRAMMING." Macroeconomic Dynamics 21, no. 2 (2016): 336–61. http://dx.doi.org/10.1017/s1365100515000528.

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A nonlinear programming formulation is introduced to solve infinite-horizon dynamic programming problems. This extends the linear approach to dynamic programming by using ideas from approximation theory to approximate value functions. Our numerical results show that this nonlinear programming is efficient and accurate, and avoids inefficient discretization.
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21

Farias, Vivek, Denis Saure, and Gabriel Y. Weintraub. "An approximate dynamic programming approach to solving dynamic oligopoly models." RAND Journal of Economics 43, no. 2 (2012): 253–82. http://dx.doi.org/10.1111/j.1756-2171.2012.00165.x.

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22

Xing, Xiaowei, and Dong Eui Chang. "The Adaptive Dynamic Programming Toolbox." Sensors 21, no. 16 (2021): 5609. http://dx.doi.org/10.3390/s21165609.

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The paper develops the adaptive dynamic programming toolbox (ADPT), which is a MATLAB-based software package and computationally solves optimal control problems for continuous-time control-affine systems. The ADPT produces approximate optimal feedback controls by employing the adaptive dynamic programming technique and solving the Hamilton–Jacobi–Bellman equation approximately. A novel implementation method is derived to optimize the memory consumption by the ADPT throughout its execution. The ADPT supports two working modes: model-based mode and model-free mode. In the former mode, the ADPT computes optimal feedback controls provided the system dynamics. In the latter mode, optimal feedback controls are generated from the measurements of system trajectories, without the requirement of knowledge of the system model. Multiple setting options are provided in the ADPT, such that various customized circumstances can be accommodated. Compared to other popular software toolboxes for optimal control, the ADPT features computational precision and time efficiency, which is illustrated with its applications to a highly non-linear satellite attitude control problem.
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23

Balakrishna, Poornima, Rajesh Ganesan, and Lance Sherry. "Airport Taxi-Out Prediction Using Approximate Dynamic Programming." Transportation Research Record: Journal of the Transportation Research Board 2052, no. 1 (2008): 54–61. http://dx.doi.org/10.3141/2052-07.

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24

Johannesson, Lars, and Bo Egardt. "Approximate Dynamic Programming Applied to Parallel Hybrid Powertrains." IFAC Proceedings Volumes 41, no. 2 (2008): 3374–79. http://dx.doi.org/10.3182/20080706-5-kr-1001.00573.

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25

Forootani, Ali, Raffaele Iervolino, Massimo Tipaldi, and Joshua Neilson. "Approximate dynamic programming for stochastic resource allocation problems." IEEE/CAA Journal of Automatica Sinica 7, no. 4 (2020): 975–90. http://dx.doi.org/10.1109/jas.2020.1003231.

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26

Kuhn, Kenneth D. "Network-Level Infrastructure Management Using Approximate Dynamic Programming." Journal of Infrastructure Systems 16, no. 2 (2010): 103–11. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000019.

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27

Flint, Matthew, and Emmanuel Fernandez. "APPROXIMATE DYNAMIC PROGRAMMING METHODS FOR COOPERATIVE UAV SEARCH." IFAC Proceedings Volumes 38, no. 1 (2005): 59–64. http://dx.doi.org/10.3182/20050703-6-cz-1902.00362.

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28

Lee, Jong Min, and Jay H. Lee. "APPROXIMATE DYNAMIC PROGRAMMING STRATEGY FOR DUAL ADAPTIVE CONTROL." IFAC Proceedings Volumes 38, no. 1 (2005): 459–64. http://dx.doi.org/10.3182/20050703-6-cz-1902.00938.

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29

Desai, Vijay V., Vivek F. Farias, and Ciamac C. Moallemi. "Approximate Dynamic Programming via a Smoothed Linear Program." Operations Research 60, no. 3 (2012): 655–74. http://dx.doi.org/10.1287/opre.1120.1044.

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30

Ferrari, S., J. E. Steck, and R. Chandramohan. "Adaptive Feedback Control by Constrained Approximate Dynamic Programming." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 4 (2008): 982–87. http://dx.doi.org/10.1109/tsmcb.2008.924140.

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31

Yijia Zhao, S. D. Patek, and P. A. Beling. "Decentralized Bayesian Search Using Approximate Dynamic Programming Methods." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 4 (2008): 970–75. http://dx.doi.org/10.1109/tsmcb.2008.928180.

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32

Parvez, Iram, and Jianjian Shen. "Algorithms of approximate dynamic programming for hydro scheduling." E3S Web of Conferences 144 (2020): 01001. http://dx.doi.org/10.1051/e3sconf/202014401001.

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In hydro scheduling, unit commitment is a complex sub-problem. This paper proposes a new approximate dynamic programming technique to solve unit commitment. A new method called Least Square Policy Iteration (LSPI) algorithm is introduced which is efficient and faster in convergence. This algorithm takes the properties of widely used algorithm least square temporal difference (LSTD), enhance it further and make it useful for optimization problems. First value function is to find a fixed policy by using least square temporal difference Q (LSTDQ) algorithm which is similar to LSTD, then LSPI is introduced for making the policy iteration algorithm by using the results of LSTDQ. It combines the data efficiency of LSTDQ and policy-search efficiency of policy iteration.
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33

Ryzhov, Ilya O., Peter I. Frazier, and Warren B. Powell. "A New Optimal Stepsize for Approximate Dynamic Programming." IEEE Transactions on Automatic Control 60, no. 3 (2015): 743–58. http://dx.doi.org/10.1109/tac.2014.2357134.

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34

Ghasempour, Taha, and Benjamin Heydecker. "Adaptive Railway Traffic Control using Approximate Dynamic Programming." Transportation Research Procedia 38 (2019): 201–21. http://dx.doi.org/10.1016/j.trpro.2019.05.012.

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35

Silva, Thiago A. O., and Mauricio C. de Souza. "Surgical scheduling under uncertainty by approximate dynamic programming." Omega 95 (September 2020): 102066. http://dx.doi.org/10.1016/j.omega.2019.05.002.

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36

Cai, Chen, Chi Kwong Wong, and Benjamin G. Heydecker. "Adaptive traffic signal control using approximate dynamic programming." Transportation Research Part C: Emerging Technologies 17, no. 5 (2009): 456–74. http://dx.doi.org/10.1016/j.trc.2009.04.005.

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37

Ghasempour, Taha, and Benjamin Heydecker. "Adaptive railway traffic control using approximate dynamic programming." Transportation Research Part C: Emerging Technologies 113 (April 2020): 91–107. http://dx.doi.org/10.1016/j.trc.2019.04.002.

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38

Bistritskas, V. B. "Approximate solution of the equations of dynamic programming." USSR Computational Mathematics and Mathematical Physics 25, no. 4 (1985): 107–13. http://dx.doi.org/10.1016/0041-5553(85)90153-3.

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39

Nosair, Hussam, Yu Yang, and Jong Min Lee. "Min–max control using parametric approximate dynamic programming." Control Engineering Practice 18, no. 2 (2010): 190–97. http://dx.doi.org/10.1016/j.conengprac.2009.09.001.

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40

Zéphyr, Luckny, Pascal Lang, Bernard F. Lamond, and Pascal Côté. "Approximate stochastic dynamic programming for hydroelectric production planning." European Journal of Operational Research 262, no. 2 (2017): 586–601. http://dx.doi.org/10.1016/j.ejor.2017.03.050.

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41

Chakrabarty, Ankush, Devesh K. Jha, Gregery T. Buzzard, Yebin Wang, and Kyriakos G. Vamvoudakis. "Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation." IEEE Transactions on Neural Networks and Learning Systems 32, no. 1 (2021): 405–19. http://dx.doi.org/10.1109/tnnls.2020.2978805.

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42

Pardalos, Panos M. "Approximate dynamic programming: solving the curses of dimensionality." Optimization Methods and Software 24, no. 1 (2009): 155. http://dx.doi.org/10.1080/10556780802583108.

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43

Keshavarz, Arezou, and Stephen Boyd. "Quadratic approximate dynamic programming for input-affine systems." International Journal of Robust and Nonlinear Control 24, no. 3 (2012): 432–49. http://dx.doi.org/10.1002/rnc.2894.

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44

Powell, Warren B. "What you should know about approximate dynamic programming." Naval Research Logistics (NRL) 56, no. 3 (2009): 239–49. http://dx.doi.org/10.1002/nav.20347.

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45

Björnberg, Jakob, and Moritz Diehl. "Approximate robust dynamic programming and robustly stable MPC." Automatica 42, no. 5 (2006): 777–82. http://dx.doi.org/10.1016/j.automatica.2005.12.016.

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46

Göçgün, Yasin. "Performance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 11, no. 2 (2021): 178–85. http://dx.doi.org/10.11121/ijocta.01.2021.00987.

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This paper focuses on the performance comparison of several approximate dynamic programming (ADP) techniques. In particular, we evaluate three ADP techniques through a class of dynamic stochastic scheduling problems: Lagrangian-based ADP, linear programming-based ADP, and direct search-based ADP. We uniquely implement the direct search-based ADP through basis functions that differ from those used in the relevant literature. The class of scheduling problems has the property that jobs arriving dynamically and stochastically must be scheduled to days in advance. Numerical results reveal that the direct search-based ADP outperforms others in the majority of problem sets generated.
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47

Cui, Chang Qing, Yi Qiang Wang, Chun Yan Yang, and Bao Sheng Yang. "On Iterative Adaptive Dynamic Programming." Applied Mechanics and Materials 380-384 (August 2013): 712–15. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.712.

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Linear system is very seldom in actual control works, so there is more engineering significant of researching on the actual nonlinear system. Time delay phenomenon is the objective phenomenon exists in nature. Neural network based on adaptive dynamic programming principle is selected to implement algorithm. The algorithm contains model network training, H network training for time delay function, critic network training. Before running this iterative algorithm, training the model network first, the model uses a three-layer BP network to realize. Time delay function network H(K) is to approximate the functional relationship between the current control input and the delayed input. The critic network is used to approximate system performance function. The simulation results show that the proposed iterative adaptive dynamic programming can solve for the optimal control of delay nonlinear systems.
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48

Park, Jooyoung, Dongsu Yang, and Kyungwook Park. "Approximate Dynamic Programming-Based Dynamic Portfolio Optimization for Constrained Index Tracking." International Journal of Fuzzy Logic and Intelligent Systems 13, no. 1 (2013): 19–30. http://dx.doi.org/10.5391/ijfis.2013.13.1.19.

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49

Erdelyi, Alexander, and Huseyin Topaloglu. "Approximate dynamic programming for dynamic capacity allocation with multiple priority levels." IIE Transactions 43, no. 2 (2010): 129–42. http://dx.doi.org/10.1080/0740817x.2010.504690.

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50

Sen, Suvrajeet, and Zhihong Zhou. "Multistage Stochastic Decomposition: A Bridge between Stochastic Programming and Approximate Dynamic Programming." SIAM Journal on Optimization 24, no. 1 (2014): 127–53. http://dx.doi.org/10.1137/120864854.

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