Research on Production Process Decision-Making Based on Simulated Annealing and Monte Carlo Algorithms

Abstract

In the production process, the decision - making regarding component inspection exerts a crucial influence on the overall profit. This paper applies a variety of statistical methods to construct models for different production and assembly scenarios, aiming to derive decision - making schemes that achieve optimal profit. Specifically, by utilizing the Central Limit Theorem and the OC curve, an efficient sampling inspection method for determining the component defect rate in the actual production process is proposed. Meanwhile, with the aid of the dynamic programming algorithm and the Monte Carlo simulation algorithm, the simplified production and assembly process is modeled and solved to obtain the optimal decisions under different component defect rates and inspection costs. Subsequently, the simulated annealing algorithm is employed to model and solve the complex multi - stage assembly, and the optimal decisions in different situations are obtained. Moreover, the relationships among the defect rate, inspection cost, and inspection decision - making are visually presented.