Optimization of multi-stage production inspection based on a dynamic decision tree
DOI:
https://doi.org/10.62051/stnv1033Keywords:
decision optimization; hypothesis testing; dynamic decision tree models; mixed integer programming.Abstract
This study proposes an optimized production monitoring solution to address these challenges. By employing hypothesis testing, binomial distribution, and Poisson distribution, a sampling inspection method is designed to determine the minimum sample size and defect threshold for detecting defective products. Subsequently, a multi-stage dynamic decision tree model is developed to decompose the complex production process into multiple decision nodes, enabling the identification of the optimal decision sequence and improving process efficiency. Furthermore, considering more complex real-world production scenarios involving various processes and components, the concept of semi-finished products is introduced. A mixed-integer programming model is constructed, integrating defect rates and process performance, to evaluate the overall performance under different strategies. The results demonstrate that comprehensive inspection of components, avoidance of redundant inspections for semi-finished and finished products, and non-disassembly of non-conforming products can significantly reduce the inspection workload while ensuring quality and efficiency.
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