An Algorithmic Model Building Scheme Based on Dynamic Programming Algorithms

Dynamic programming algorithm is a basic tool for solving multi-level decision-making problems, which is widely used in social economy, engineering technology and optimal control. Aiming at the incommensurability of multi-objective programming indices, the relative membership degree matrix of alloca...

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Veröffentlicht in:Journal of physics. Conference series Jg. 1345; H. 5; S. 52080 - 52090
Hauptverfasser: Li, Zhiyong, Chen, Weiling
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.11.2019
Schlagworte:
Algorithms
Decision making
Dynamic programming
Indicators
Mathematical programming
Multiple objective analysis
Optimal control
Physics
Qualitative analysis
State variable
ISSN:1742-6588, 1742-6596
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Zusammenfassung:Dynamic programming algorithm is a basic tool for solving multi-level decision-making problems, which is widely used in social economy, engineering technology and optimal control. Aiming at the incommensurability of multi-objective programming indices, the relative membership degree matrix of allocation schemes for quantitative and qualitative indices is established, and the analysis method of multi-objective and multi-stage dynamic programming problems is given by using vector and matrix unification. The decision maker determines the decision coefficients of each constraint indicator according to the specific problem and performs weighting processing. The weights of different state variables for different constraint indicators are integrated, the comprehensive weights of state variables are solved, and the original static constraint indicators are transformed into dynamic variables that can influence the decision results. The research shows that the algorithm transforms the multi-stage decision problem into a single-stage decision problem, and solves the optimal value of the expected value through the mixture of random expectation models in a single stage. As long as it is used properly, dynamic planning of this traditional method can be a better way to solve complex problems.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1345/5/052080