Optimizing Complex Process Decisions Using Pipeline and Genetic Algorithms

This paper aims to explore effective strategies for improving product quality and reducing costs by optimizing production decisions in the global manufacturing industry, particularly focusing on China, a prominent manufacturing hub. In this study, a pipeline algorithm was developed and genetic algorithms were integrated, leveraging MATLAB's efficient computing and simulation capabilities, to construct a comprehensive decision-making optimization model for enterprise production. The research addresses critical challenges faced by manufacturers in the pursuit of quality assurance and cost optimization, which is vital for maintaining international standards and securing a competitive edge in the global market. By examining the defective rates of spare parts and finished products, the model comprehensively evaluates quality inspection and handling strategies throughout the production chain. Additionally, the study focuses on optimizing multi-process operations and managing multiple parts, employing a genetic algorithm to screen out optimal solutions efficiently. The results demonstrate that the proposed model can significantly enhance decision-making efficiency and economic benefits, providing theoretical support and practical guidance for the optimization and upgrading of the manufacturing industry.