Multi-Stage Defect Rate Optimization Using a Hybrid Model of Simulated Annealing and Monte Carlo Simulation

Abstract

Amid global value chain restructuring and Industry 4.0 transformation, manufacturers face significant challenges in controlling defect rates across multi-stage production processes. This study develops a hybrid optimization model combining Simulated Annealing and Monte Carlo Simulation to balance quality control and cost efficiency. The framework incorporates a comprehensive cost structure covering components, semi-finished/finished products, and defect-related losses, while introducing a dynamic defect rate adjustment coefficient to quantify inspection-skipping risks at different stages.The model employs Simulated Annealing to optimize inspection strategies, with Monte Carlo methods verifying solution robustness. Sampling inspection techniques are integrated to enhance defect detection efficiency. Experimental results demonstrate that the optimized strategy reduces total production costs substantially, with the best-performing solution achieving a unit cost of 145 CNY while maintaining defect rates below 2.25%. Compared to conventional approaches, the new model cuts unnecessary inspection costs by approximately 14% without compromising quality standards.This research provides manufacturing enterprises, particularly in complex sectors like electronics, with a practical decision-making tool for dynamic production environments. The framework's adaptability supports supply chain quality management in the era of smart manufacturing, offering both methodological innovation and operational value for industry applications.