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Journal articles on the topic 'Dynamic Programming: models'

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Published: 5 September 2021
Last updated: 8 February 2022

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1

Poch, Leslie A., and R. T. Jenkins. "4.4. Dynamic programming models." Energy 15, no. 7-8 (July 1990): 573–81. http://dx.doi.org/10.1016/0360-5442(90)90006-n.

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2

Semmler, Willi. "Solving nonlinear dynamic models by iterative dynamic programming." Computational Economics 8, no. 2 (May 1995): 127–54. http://dx.doi.org/10.1007/bf01299714.

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3

Kasahara, Hiroyuki, and Katsumi Shimotsu. "Estimation of Discrete Choice Dynamic Programming Models." Japanese Economic Review 69, no. 1 (December 1, 2017): 28–58. http://dx.doi.org/10.1111/jere.12169.

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4

Krautkraemer, Jeffrey A., G. C. Kooten, and Douglas L. Young. "Incorporating Risk Aversion into Dynamic Programming Models." American Journal of Agricultural Economics 74, no. 4 (November 1992): 870–78. http://dx.doi.org/10.2307/1243184.

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5

Verdu, Sergio, and H. Vincent Poor. "Abstract Dynamic Programming Models under Commutativity Conditions." SIAM Journal on Control and Optimization 25, no. 4 (July 1987): 990–1006. http://dx.doi.org/10.1137/0325054.

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6

Stanfel, L. E. "New dynamic programming models of fisheries management." Mathematical and Computer Modelling 10, no. 8 (1988): 593–607. http://dx.doi.org/10.1016/0895-7177(88)90130-6.

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7

Wang, Chung-Lie. "The principle and models of dynamic programming." Journal of Mathematical Analysis and Applications 118, no. 2 (September 1986): 287–308. http://dx.doi.org/10.1016/0022-247x(86)90264-7.

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8

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 (June 2012): 253–82. http://dx.doi.org/10.1111/j.1756-2171.2012.00165.x.

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9

ASANO, TAKAO. "DYNAMIC PROGRAMMING ON INTERVALS." International Journal of Computational Geometry & Applications 03, no. 03 (September 1993): 323–30. http://dx.doi.org/10.1142/s0218195993000208.

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We consider problems on intervals which can be solved by dynamic programming. Specifically, we give an efficient implementation of dynamic programming on intervals. As an application, an optimal sequential partition of a graph G=(V, E) can be obtained in O(m log n) time, where n=|V| and m=|E|. We also present an O(n log n) time algorithm for finding a minimum weight dominating set of an interval graph G=(V, E), and an O(m log n) time algorithm for finding a maximum weight clique of a circular-arc graph G=(V, E), provided their intersection models of n intervals (arcs) are given.
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10

Kennedy, John O. S., J. Brian Hardaker, and John Quiggin. "Incorporating Risk Aversion into Dynamic Programming Models: Comment." American Journal of Agricultural Economics 76, no. 4 (November 1994): 960–64. http://dx.doi.org/10.2307/1243758.

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11

Krautkraemer, Jeffrey A., G. C. Kooten, and Douglas L. Young. "Incorporating Risk Aversion into Dynamic Programming Models: Reply." American Journal of Agricultural Economics 76, no. 4 (November 1994): 965–67. http://dx.doi.org/10.2307/1243759.

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12

TOKUNAGA, Yoshiyuki, and Hajime INAMURA. "Aircraft scheduling models by using dynamic programming approach." Doboku Gakkai Ronbunshu, no. 440 (1992): 109–16. http://dx.doi.org/10.2208/jscej.1991.440_109.

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13

Keane, Michael P., and Kenneth I. Wolpin. "Empirical applications of discrete choice dynamic programming models." Review of Economic Dynamics 12, no. 1 (January 2009): 1–22. http://dx.doi.org/10.1016/j.red.2008.07.001.

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14

Cardwell, Hal, and Hugh Ellis. "Stochastic dynamic programming models for water quality management." Water Resources Research 29, no. 4 (April 1993): 803–13. http://dx.doi.org/10.1029/93wr00182.

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15

Hutchinson, John M. C., and John M. McNamara. "Ways to test stochastic dynamic programming models empirically." Animal Behaviour 59, no. 4 (April 2000): 665–76. http://dx.doi.org/10.1006/anbe.1999.1362.

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16

Zarrop, M. B. "Book Review: Dynamic Programming: Deterministic and Stochastic Models." International Journal of Electrical Engineering & Education 25, no. 4 (October 1988): 376–77. http://dx.doi.org/10.1177/002072098802500429.

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17

Wang, Chung-Lie. "The principle and models of dynamic programming, II." Journal of Mathematical Analysis and Applications 135, no. 1 (October 1988): 268–83. http://dx.doi.org/10.1016/0022-247x(88)90153-9.

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18

Wang, Chung-lie. "The principle and models of dynamic programming, III." Journal of Mathematical Analysis and Applications 135, no. 1 (October 1988): 284–96. http://dx.doi.org/10.1016/0022-247x(88)90154-0.

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19

Wang, Chung-lie. "The principle and models of dynamic programming, IV." Journal of Mathematical Analysis and Applications 137, no. 1 (January 1989): 148–60. http://dx.doi.org/10.1016/0022-247x(89)90278-3.

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20

Wang, Chung-lie. "The principle and models of dynamic programming, V." Journal of Mathematical Analysis and Applications 137, no. 1 (January 1989): 161–67. http://dx.doi.org/10.1016/0022-247x(89)90279-5.

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21

Kreimer, Joseph, Dimitri Golenko-Ginzburg, and Avraham Mehrez. "Allocation of Control Points in Stochastic Dynamic-Programming Models." Journal of the Operational Research Society 39, no. 9 (September 1988): 847. http://dx.doi.org/10.2307/2583527.

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22

Rusnák, A., M. Fikar, M. A. Latifi, and A. Mészáros. "Receding horizon iterative dynamic programming with discrete time models." Computers & Chemical Engineering 25, no. 1 (January 2001): 161–67. http://dx.doi.org/10.1016/s0098-1354(00)00639-6.

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23

Kehagias, A., A. Nicolaou, V. Petridis, and P. Fragkou. "Text segmentation by product partition models and dynamic programming." Mathematical and Computer Modelling 39, no. 2-3 (January 2004): 209–17. http://dx.doi.org/10.1016/s0895-7177(04)90008-8.

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24

Kreimer, Joseph, Dimitri Golenko-Ginzburg, and Avraham Mehrez. "Allocation of Control Points in Stochastic Dynamic-Programming Models." Journal of the Operational Research Society 39, no. 9 (September 1988): 847–53. http://dx.doi.org/10.1057/jors.1988.144.

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25

Bertsekas, Dimitri P. "Affine Monotonic and Risk-Sensitive Models in Dynamic Programming." IEEE Transactions on Automatic Control 64, no. 8 (August 2019): 3117–28. http://dx.doi.org/10.1109/tac.2019.2896049.

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26

FABBRI, GIORGIO, SILVIA FAGGIAN, and FAUSTO GOZZI. "On Dynamic Programming in Economic Models Governed by DDEs." Mathematical Population Studies 15, no. 4 (November 5, 2008): 267–90. http://dx.doi.org/10.1080/08898480802440836.

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27

Hibiki, Norio. "Multi-Period Stochastic Programming Models for Dynamic Asset Allocation." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2000 (May 5, 2000): 37–42. http://dx.doi.org/10.5687/sss.2000.37.

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28

Beutler, Frederick J. "Dynamic Programming: Deterministic and Stochastic Models (Dimitri P. Bertsekas)." SIAM Review 31, no. 1 (March 1989): 132–33. http://dx.doi.org/10.1137/1031018.

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29

Cerisola, Santiago, Jesus M. Latorre, and Andres Ramos. "Stochastic dual dynamic programming applied to nonconvex hydrothermal models." European Journal of Operational Research 218, no. 3 (May 2012): 687–97. http://dx.doi.org/10.1016/j.ejor.2011.11.040.

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30

Faggian, Silvia, and Fausto Gozzi. "Optimal investment models with vintage capital: Dynamic programming approach." Journal of Mathematical Economics 46, no. 4 (July 2010): 416–37. http://dx.doi.org/10.1016/j.jmateco.2010.02.006.

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31

Taylor, William R. "Random structural models for double dynamic programming score evaluation." Journal of Molecular Evolution 44, S1 (January 1997): S174—S180. http://dx.doi.org/10.1007/pl00000069.

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32

Chen, Hsin-Der, Donald W. Hearn, and Chung-Yee Lee. "A dynamic programming algorithm for dynamic lot size models with piecewise linear costs." Journal of Global Optimization 4, no. 4 (June 1994): 397–413. http://dx.doi.org/10.1007/bf01099265.

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33

Clarke, S. R., and J. M. Norman. "To Run or Not?: Some Dynamic Programming Models in Cricket." Journal of the Operational Research Society 50, no. 5 (May 1999): 536. http://dx.doi.org/10.2307/3010003.

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34

Jaśkiewicz, Anna, and Andrzej S. Nowak. "Discounted dynamic programming with unbounded returns: Application to economic models." Journal of Mathematical Analysis and Applications 378, no. 2 (June 2011): 450–62. http://dx.doi.org/10.1016/j.jmaa.2010.08.073.

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35

Clarke, S. R., and J. M. Norman. "To run or not?: Some dynamic programming models in cricket." Journal of the Operational Research Society 50, no. 5 (May 1999): 536–45. http://dx.doi.org/10.1057/palgrave.jors.2600705.

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36

Taber, Christopher R. "Semiparametric identification and heterogeneity in discrete choice dynamic programming models." Journal of Econometrics 96, no. 2 (June 2000): 201–29. http://dx.doi.org/10.1016/s0304-4076(99)00057-3.

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37

van Dawen, Rolf. "Pointwise and Uniformly Good Stationary Strategies for Dynamic Programming Models." Mathematics of Operations Research 11, no. 3 (August 1986): 521–35. http://dx.doi.org/10.1287/moor.11.3.521.

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38

Liu, Jun, Kezhen Yan, Lingyun You, Pei Liu, and Kezhen Yan. "Prediction models of mixtures’ dynamic modulus using gene expression programming." International Journal of Pavement Engineering 18, no. 11 (January 23, 2016): 971–80. http://dx.doi.org/10.1080/10298436.2016.1138113.

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39

Lipnicka, Marta, and Andrzej Nowakowski. "On dual dynamic programming in shape optimization of coupled models." Structural and Multidisciplinary Optimization 59, no. 1 (August 7, 2018): 153–64. http://dx.doi.org/10.1007/s00158-018-2057-5.

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40

Esteban-Bravo, Mercedes, and Francisco J. Nogales. "Solving dynamic stochastic economic models by mathematical programming decomposition methods." Computers & Operations Research 35, no. 1 (January 2008): 226–40. http://dx.doi.org/10.1016/j.cor.2006.02.031.

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41

Logé, Frédéric, Erwan Le Pennec, and Habiboulaye Amadou-Boubacar. "Intelligent Questionnaires Using Approximate Dynamic Programming." i-com 19, no. 3 (December 1, 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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42

Trinh, Truong Hong. "The Dynamic Programming Models for Inventory Control System with Time-varying Demand." Business and Economic Research 7, no. 1 (March 21, 2017): 128. http://dx.doi.org/10.5296/ber.v7i1.10965.

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The concept of dependent and independent demand is important in planning and replenishment inventory that also requires different inventory control solutions. This paper employs the dynamic programming technique for inventory control system with time varying demand to propose the replenishment policy in terms of the economic order quantity, number of replenishment, and reorder point where total inventory cost is minimized. The study result indicates that the dynamic programming models outperform the traditional lot sizing models in term of total inventory cost. Moreover, the paper creates opportunities for extending further researches on dynamic inventory related to uncertainty conditions of demand, yield, lead time, and capacity constraints.
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43

Raksha, Serhii, Pavlo Anofriev, and Oleksii Kuropiatnyk. "Simulation modelling of the rolling stock axle test-bench." E3S Web of Conferences 123 (2019): 01032. http://dx.doi.org/10.1051/e3sconf/201912301032.

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Wheelset axles are essential parts of railway and mine site rolling stock. For fatigue testing of axles, various test-benches are designed to implement the cyclic loads. The effectiveness of test-bench vibration analysis grows with the use of numerical approach and simulation models created with the aid of visual programming tools. The purpose of the work is to develop and assess the proposed simulation models of test-bench dynamics created with the aid of visual programming tools. Based on mathematical models, the test-bench simulation models of the lever system vibration have been developed. Simulation models are created with the aid of Simulink visual programming tools running under Matlab system. For modelling the components of Simulink, the SimMechanics and DSP System Toolbox Libraries are used. The comparative analysis of proposed models has been made. For the first time, with the aid of Simulink visual programming tools, the set of test-bench vibration simulation models has been obtained in steady-state and transient motion modes for linear task formulation. The proposed S-models allow automation and visualization of the motion dynamics study for test-bench components in order to determine their rational elastic-weight, kinematic and dynamic behavior. Simulation of vibrations was carried out using design parameters of the test-bench metal framework.
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44

YAMADA, YUJI, and JAMES A. PRIMBS. "DISTRIBUTION-BASED OPTION PRICING ON LATTICE ASSET DYNAMICS MODELS." International Journal of Theoretical and Applied Finance 05, no. 06 (September 2002): 599–618. http://dx.doi.org/10.1142/s0219024902001572.

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In this paper, we propose a numerical option pricing method based on an arbitrarily given stock distribution. We first formulate a European call option pricing problem as an optimal hedging problem by using a lattice based incomplete market model. A dynamic programming technique is then applied to solve the mean square optimal hedging problem for the discrete time multi-period case by assigning suitable probabilities on the lattice, where the underlying stock price distribution is derived directly from empirical stock price data which may possess "heavy tails". We show that these probabilities are obtained from a network flow optimization which can be solved efficiently by quadratic programming. A computational complexity analysis demonstrates that the number of iterations for dynamic programming and the number of parameters in the network flow optimization are both of square order with respect to the number of periods. Numerical experiments illustrate that our methodology generates the implied volatility smile.
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45

Santos, Manuel S., and Jesus Vigo-Aguiar. "Analysis of a Numerical Dynamic Programming Algorithm Applied to Economic Models." Econometrica 66, no. 2 (March 1998): 409. http://dx.doi.org/10.2307/2998564.

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46

Augeraud-Veron, Emmanuelle, Mauro Bambi, and Fausto Gozzi. "Solving Internal Habit Formation Models Through Dynamic Programming in Infinite Dimension." Journal of Optimization Theory and Applications 173, no. 2 (February 10, 2017): 584–611. http://dx.doi.org/10.1007/s10957-017-1073-8.

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47

Mousavi, Seyed Jamshid, and Mohammad Karamouz. "Computational improvement for dynamic programming models by diagnosing infeasible storage combinations." Advances in Water Resources 26, no. 8 (August 2003): 851–59. http://dx.doi.org/10.1016/s0309-1708(03)00061-7.

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48

Eiswerth, Mark E., and G. Cornelis van Kooten. "Dynamic Programming and Learning Models for Management of a Nonnative Species." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 55, no. 4 (December 2007): 485–98. http://dx.doi.org/10.1111/j.1744-7976.2007.00104.x.

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49

Huang, Wen‐Cheng, Ricardo Harboe, and Janos J. Bogardi. "Testing Stochastic Dynamic Programming Models Conditioned on Observed or Forecasted Inflows." Journal of Water Resources Planning and Management 117, no. 1 (January 1991): 28–36. http://dx.doi.org/10.1061/(asce)0733-9496(1991)117:1(28).

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50

Mohamed, Ahmed S. "Broader dynamic load balancing for hybrid/multi-level parallel programming models." International Journal of High Performance Computing and Networking 3, no. 2/3 (2005): 171. http://dx.doi.org/10.1504/ijhpcn.2005.008034.

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