Multi-Stage Decision Optimization for Electronics Assembly using Statistical and Simulation Methods

Electronics manufacturers have a hard time keeping up with quality control and costs during production. This includes everything from buying the materials to checking that everything is correct to putting the materials together and dealing with problems. This study uses math models to make the best choices. Dealing with Problem 1, we use hypothesis testing and the binomial distribution to come up with sampling inspection plans. For Problem 2, we're using decision trees and Monte Carlo simulation to figure out the best ways to inspect and rework or scrap parts in a two-component assembly. For Problem 3, we use Simulated Annealing to make decisions in complex, multi-stage projects. Helps us find a balance between making money and having a good reputation. The study also found some key things, like the best ways to collect samples, strategies for making a profit, and ways to cut costs in complex assemblies using simulated annealing. This research offers a detailed, data-driven plan for making decisions to improve the production process in electronics manufacturing.