Research on Production Decision Making Based on Monte Carlo Algorithm and Simulated Annealing Algorithm Modeling

In this paper, a dynamic planning study is conducted based on heuristic algorithms to investigate the decision-making process of spare parts assembly in an electronic enterprise. Firstly, according to the dynamic sampling and testing strategy, Bayes' theorem and confidence interval calculation method in statistics [1], a sampling and testing model based on Monte Carlo algorithm is established to determine whether to accept the batch of spare parts or not [2]. Then, by comparing the two decision-making methods of probabilistic decision-making and binary variable decision-making, an expected profit model is established, and a simulated annealing model is built using a heuristic algorithm to find the globally optimal solution in each scenario [3], [4]. Finally, considering the defective rate of spare parts, semi-finished products and finished products in the production process of the enterprise, as well as inspection, assembly and exchange losses and other factors, the model of simulated annealing algorithm based on dynamic planning is established, the total profit of each decision combination is calculated through the dynamic planning cache mechanism [5], and the simulated annealing algorithm is utilized for the global search to find a solution close to the global optimum. The proposed heuristic algorithm can better balance the ability of global search and local search, can effectively avoid falling into the local optimal solution too early, and improve the solution efficiency.