Establishment and Solution of a Multi-Stage Decision Model Based on Hypothesis Testing and Dynamic Programming Algorithm

This paper introduces a novel multi-stage decision-making model that integrates hypothesis testing and dynamic programming algorithms to address complex decision-making scenarios. Initially, we develop a sampling inspection scheme that controls for both Type I and Type II errors using a simple rando...

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Vydané v:2024 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC) s. 884 - 889
Hlavní autori: Liu, Ziyang, Hu, Yurui, Deng, Yihan
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 29.12.2024
Predmet:
Computational modeling
Decision making
Dynamic programming
Dynamic programming algorithms
Heuristic algorithms
Hypothesis testing
Linear programming
Multi-stage decision-making model
Robots
Sampling methods
Strategic planning
Systematics
Testing
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Shrnutí:This paper introduces a novel multi-stage decision-making model that integrates hypothesis testing and dynamic programming algorithms to address complex decision-making scenarios. Initially, we develop a sampling inspection scheme that controls for both Type I and Type II errors using a simple random sampling method without replacement, ensuring the randomness and representativeness of the sample while minimizing selection bias. Through the application of hypothesis testing theory, a hypothesis testing model concerning the defect rate is established, and formulas for the approximate distribution of the sample defect rate and the minimum sample size required under two different scenarios are derived. Subsequently, a multi-stage dynamic programming decision model is constructed. This involves defining the state transition functions T_{k}\left(n_{k}, s_{k}\right) and stage-specific objective functions V_{k, n} , followed by obtaining six optimal decision strategies under various conditions through backward recursion. The results demonstrate the model's potent capability for multi-stage decision-making and its high interpretability, offering significant advantages in practical applications.
DOI:10.1109/ICIRDC65564.2024.00163