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Journal articles on the topic 'Dynamic Programming: models'

Author: Grafiati

Published: 5 September 2021

Last updated: 25 July 2025

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1

Poch, Leslie A., and R. T. Jenkins. "4.4. Dynamic programming models." Energy 15, no. 7-8 (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 (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 (2017): 28–58. http://dx.doi.org/10.1111/jere.12169.

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4

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

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5

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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6

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 (1992): 870–78. http://dx.doi.org/10.2307/1243184.

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7

Wang, Chung-Lie. "The principle and models of dynamic programming." Journal of Mathematical Analysis and Applications 118, no. 2 (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 (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 (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 (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 (1994): 965–67. http://dx.doi.org/10.2307/1243759.

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12

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

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13

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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14

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

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15

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

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16

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

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17

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

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18

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

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19

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

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20

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

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21

Kuroiwa, Ryo, and J. Christopher Beck. "Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial Optimization." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (2023): 236–44. http://dx.doi.org/10.1609/icaps.v33i1.27200.

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For combinatorial optimization problems, model-based approaches such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the `holy grail' of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a new model-based paradigm based on dynamic programming (DP). While DP is not new, it has typically been implemented as a problem-specific method. We propose Dynamic Programming Description Language (DyPDL), a formalism to define DP models, and develop Cost-Algebraic A* Solver for DyPDL (CAASDy), a generic s

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22

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 (1988): 847. http://dx.doi.org/10.2307/2583527.

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23

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

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24

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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25

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 (1988): 847–53. http://dx.doi.org/10.1057/jors.1988.144.

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26

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

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27

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 (2001): 161–67. http://dx.doi.org/10.1016/s0098-1354(00)00639-6.

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28

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

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29

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

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30

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 (2004): 209–17. http://dx.doi.org/10.1016/s0895-7177(04)90008-8.

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31

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 (2012): 687–97. http://dx.doi.org/10.1016/j.ejor.2011.11.040.

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32

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

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33

Fleck, Philipp, Bernhard Werth, and Michael Affenzeller. "Population Dynamics in Genetic Programming for Dynamic Symbolic Regression." Applied Sciences 14, no. 2 (2024): 596. http://dx.doi.org/10.3390/app14020596.

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This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of a genetic algorithm (GA) as a traditional evolutionary algorithm and an age-layered population structure (ALPS) as an open-ended evolutionary algorithm for dynamic symbolic regression. This study analyzes population dynamics by examining variable frequencies and impact changes over

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34

Kubsch, Marcus, and Paul C. Hamerski. "Dynamic Energy Transfer Models." Physics Teacher 60, no. 7 (2022): 583–85. http://dx.doi.org/10.1119/5.0037727.

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Energy is a disciplinary core idea and a cross-cutting concept in the K-12 Framework for Science Education and the Next Generation Science Standards (NGSS). As numerous authors point out, the energy model in these standards emphasizes the connections between energy and systems. Using energy ideas to interpret or make sense of phenomena means tracking transfers of energy across systems (including objects and fields) as phenomena unfold. To support students in progressing towards this goal, numerous representations—both static and dynamic—that describe the flow of energy across systems exist. St

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35

Meijer, Erik. "Virtual Machinations: Using Large Language Models as Neural Computers." Queue 22, no. 3 (2024): 25–52. http://dx.doi.org/10.1145/3676287.

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We explore how Large Language Models (LLMs) can function not just as databases, but as dynamic, end-user programmable neural computers. The native programming language for this neural computer is a Logic Programming-inspired declarative language that formalizes and externalizes the chain-of-thought reasoning as it might happen inside a large language model.

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36

Li, Ji-Qing, Yu-Shan Zhang, Chang-Ming Ji, Ai-Jing Wang, and Jay R. Lund. "Large-scale hydropower system optimization using dynamic programming and object-oriented programming: the case of the Northeast China Power Grid." Water Science and Technology 68, no. 11 (2013): 2458–67. http://dx.doi.org/10.2166/wst.2013.528.

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This paper examines long-term optimal operation using dynamic programming for a large hydropower system of 10 reservoirs in Northeast China. Besides considering flow and hydraulic head, the optimization explicitly includes time-varying electricity market prices to maximize benefit. Two techniques are used to reduce the ‘curse of dimensionality’ of dynamic programming with many reservoirs. Discrete differential dynamic programming (DDDP) reduces the search space and computer memory needed. Object-oriented programming (OOP) and the ability to dynamically allocate and release memory with the C++

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37

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 (1994): 397–413. http://dx.doi.org/10.1007/bf01099265.

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38

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.

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39

Trinh, Truong Hong. "The Dynamic Programming Models for Inventory Control System with Time-varying Demand." Business and Economic Research 7, no. 1 (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 oppo

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40

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 (1999): 536. http://dx.doi.org/10.2307/3010003.

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41

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 (1999): 536–45. http://dx.doi.org/10.1057/palgrave.jors.2600705.

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42

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

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43

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

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44

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 (2016): 971–80. http://dx.doi.org/10.1080/10298436.2016.1138113.

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45

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 (2011): 450–62. http://dx.doi.org/10.1016/j.jmaa.2010.08.073.

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46

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

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47

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

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48

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 episo

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49

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 (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

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50

Jdid, Maissam, and Florentin Smarandache. "Converting Some Zero-One Neutrosophic Nonlinear Programming Problems into Zero-One Neutrosophic Linear Programming Problems." Neutrosophic Optimization and Intelligent Systems 1 (January 21, 2024): 39–45. http://dx.doi.org/10.61356/j.nois.2024.17489.

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The science of operations research is the applied aspect of mathematics and one of the most important modern sciences that is concerned with practical issues and meets the desire and request of decision makers to obtain ideal decisions through the methods it presents that are appropriate for all issues, such as linear programming, nonlinear programming, dynamic programming, integer programming, etc. The basic essence of this science is to build mathematical models consisting of an objective function and constraints. In these models, the objective function is a maximization function or a minimi

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