An Investigation of Production Decision Making Based on Simulated Annealing Algorithm and Genetic Algorithm

The aim of this paper is to explore the production decision-making problem based on simulated annealing algorithm and genetic algorithm, especially for the design and optimisation of production processes in product manufacturing companies. Minimisation of production costs and optimisation of production strategies are achieved through algorithmic model analysis. In the study, hypothesis testing and binomial distribution model are firstly used to calculate the required sample size and analyse the sample requirements at different confidence levels. Then, a 0–1 integer planning model is constructed to optimise the total cost using genetic algorithm in combination with each cost in the production process. Subsequently, a simulated annealing algorithm is used for further optimisation in the case of multiple processes to compare the cost performance under different numbers of processes. In addition, random sampling and Bayesian optimisation are introduced in this study to rescale the model to reduce computational errors. The results of the study show that the proposed model can effectively support production decision-making under a variety of conditions and shows good adaptability.