Research on Production Decision-Making Model Based on Monte Carlo Simulation Algorithm

Abstract

Reasonably determining the minimum sample size of electronic device sampling and testing has an important cost-effective value for enterprises, which is conducive to improving their economic efficiency and market competitiveness. In this paper, hypothesis testing and normal distribution approximation are used to determine the minimum sample size, then the sequential probability ratio (SPRT) model test is used to derive the sampling and testing scheme, and the stability of the decision is analyzed through the operating characteristic (OC) curve and the average sample size (ASN) curve, so as to further establish the cost-benefit analysis model to assess the impact of the testing cost, defective rate, market selling price and other factors on the profit, and finally the Monte Carlo simulation algorithm is used to predict the profit under different scenarios and strategies. Finally, Monte Carlo simulation algorithms are used to predict the cost-effectiveness under different situations and strategies, and to derive the optimal strategic plan, decision-making basis and corresponding index results for each production situation.This optimal production decision allows the enterprise to effectively manage inspection costs while maintaining product quality. It also contributes to a reduction in the rate of defective products, enhances market pricing strategies, and ultimately facilitates the efficient allocation of resources.