Monte Carlo Simulation-Based Production Decision Optimization Research in Uncertain Environments

Abstract

Facing intensified competition in electronics manufacturing, this study develops an integrated decision-making framework to optimize inspection strategies and defect rate control. Leveraging binomial distribution, hypothesis testing, and multi-objective optimization, a hierarchical cost model is constructed for components, semi-finished products, and finished goods. Genetic algorithms identify an optimal solution—inspecting critical component 8 and semi-finished product 2 while eliminating redundant inspections and disassembly—reducing total costs by 18.7%. To address process variability, Monte Carlo dynamically assesses batch-specific defect rate fluctuations across six intervals, enabling adaptive re-optimization for Scenarios 2 and 3. Results show that tiered detection strategies (prioritizing key components 1/4 and enforcing full finished-product inspection) cut quality loss costs by 23.4% while preventing over-inspection waste. This work advances a data-driven theoretical foundation for intelligent quality-cost synergies in electronics production, offering actionable insights for sustainable manufacturing competitiveness.