Research on Multi-Stage Optimization of Assembly and Disassembly on Statistical Methods and Intelligent Algorithms

This study proposes a decision optimization framework that combines statistical methods and intelligent algorithms to enhance quality control and modular operations in a multi-stage process. The framework focuses on optimizing inspection strategies in multiple stages, including whether to inspect unit modules, whether to reconfigure failed modules, and the combination and integration between modules. The study employs a count-based stepwise sampling approach, which demonstrates a more efficient solution in defect detection compared to traditional binomial distribution sampling. In a system scenario with multiple sub-processes and modular components, this paper explores and optimizes the combination strategy of detection and reconfiguration using dynamic programming with genetic algorithms. The results show that by selectively detecting intermediate modules, targeted reconfiguration of some failed components, and targeted monitoring of the overall structure, the effectiveness and accuracy of decision-making are significantly improved. This study demonstrates the feasibility of stepwise sampling with intelligent algorithms in complex processes and proposes robust optimization strategies through dynamic programming and genetic algorithms. This study provides an effective decisionmaking framework for inspection and module management in complex processes, which can improve the accuracy and efficiency of related engineering operations and has wide application value.