Research on production process decision model based on Monte Carlo simulation and multi-objective optimization

This study proposes a production decision framework integrating Monte Carlo simulation and multi-objective optimization to optimize electronics manufacturing processes. Through systematic evaluation of 16 strategy combinations, we identify strategies 1100 and 1101 as optimal solutions. Strategy 1100 implements component-level inspections for both parts, skips product testing, and discards defective products, achieving an 8% market defect rate with 18.7% cost reduction. Strategy 1101 maintains identical inspections but incorporates disassembly of defective products, reducing market defects to 5% at a 14.5% cost increase relative to 1100. A dynamic weighted scoring function adaptively balances profit maximization, defect rate control, and cost constraints, validated through 10,000-cycle probabilistic simulations. These strategies reduce downstream defect propagation by 61-63% compared to terminal inspection approaches. Sensitivity analysis confirms component defect rates dominate system performance with a normalized index of 0.71, while maintaining solution stability within ±2% parameter variations. Benchmark comparisons demonstrate 9-14% improvement in profit-cost ratios over traditional methods. The framework enables manufacturers to strategically select between cost-driven 1100 and quality-driven 1101 based on market requirements, establishing an equilibrium between preventive quality assurance and operational efficiency. This approach provides actionable insights for industrial decision-making in stochastic production environments.