Optimisation of Spare Parts Quality Inspection Cost Based on Simulated Annealing and Genetic Algorithm

This paper provides an in-depth study on the impact of spare parts quality inspection on production costs in the electronics industry. By establishing a model based on binomial distribution, central limit theorem and right-hand side hypothesis testing, the costs, profits and losses of enterprises under different testing strategies are analysed. Firstly, for the inspection cost problem of accessory products, the minimum number of inspections is determined by assuming the number of sample defective products using binomial distribution and approximating the probability by De Moivre-Laplace theorem. Second, the simulated annealing algorithm is combined to derive 16 decision alternatives based on the analysis of control variables, and the best decision with the lowest total cost is finally determined. Finally, for the case of increasing number of spare parts and the appearance of semi-finished products, a mathematical model with minimising cost as the objective function is developed and solved using a genetic algorithm to evaluate the total cost under different inspection strategies. The study shows that the model proposed in this paper can reasonably solve the cost optimisation problem in electronic product quality inspection with high efficiency and practicality.