Research on Dual Objectives Optimization of Quality and Cost Based on MCMC-ACO and Dynamic Bayesian Integration in Multi-Process Electronics Manufacturing

This study proposes a dual-objective optimization framework for quality and cost control in electronics manufacturing, integrating Bayesian inference and discrete optimization. A binomial-Beta hierarchical model combined with the Metropolis-Hastings algorithm enables accurate posterior estimation of defect rates, particularly under small-sample conditions. An ant colony optimization (ACO) algorithm is introduced to improve inspection path planning, reducing inter-process defect transmission. The Metropolis-Hastings–ACO hybrid architecture achieves efficient uncertainty quantification and resource allocation through MCMC sampling and pheromone-based feedback. Experimental validation demonstrates an 18.8% cost reduction at 90% confidence, with performance metrics including F1 score (0.892), runtime (14.2 s), and convergence rate (92.4%) confirming the method's applicability to industrial settings. The framework supports robust decision-making in multi-process systems through statistically grounded, computationally scalable modeling.