Research on the Optimization of Quality Inspection Processes Based on the SPRT-GA Algorithm

With the rapid advancement of the big data era, algorithmic technology has become deeply integrated into various industries, particularly in the field of product quality testing. To address this trend, this paper proposes an innovative approach that combines the Sequential Probability Ratio Test (SPRT) with a Genetic Algorithm (GA) to optimize the inspection process and develop an optimal quality management strategy. Grounded in dynamic optimization theory, this paper presents a new quality inspection framework: SPRT is employed for real-time sampling tests, while the GA performs global optimization of key parameters. By simulating biological evolutionary mechanisms, the genetic algorithm adaptively adjusts critical decision-making parameters throughout the inspection and disassembly processes, ensuring optimal operation under variable production environments. Experimental results demonstrate that the SPRT-GA algorithm effectively reduces inspection frequency and optimizes resource allocation, significantly enhancing production efficiency and process stability. The results confirm the algorithm's high efficiency and broad applicability, particularly in complex production processes. The combination of intelligent decision-making algorithms for sampling-optimisation established in this paper has the potential to be applied to a wide range of operations research domains in order to solve various types of optimisation problems and provide effective decision support.