Research On Decision Optimization of Multi-Stage Production Process Based on Decision Tree Model and Simulated Annealing Algorithm

Abstract

The control of the defective rate of spare parts and finished products in the production process of enterprises is very important for product quality and economic benefit. This paper probes into whether the optimal decision scheme is made for the products generated at each stage in the production process after an enterprise gets the defective rate from sampling inspection, and comprehensively uses the multi-stage decision model and simulated annealing algorithm to discuss and answer. Firstly, a decision tree model is established for a simple process, an objective function is set up to maximize profit, and the possible decisions and results of each link are calculated. Then the optimal decision scheme and results are obtained by simulated annealing algorithm. Secondly, for more complex processes, a comprehensive multi-stage decision model is established, the possible decisions and results of each part and each process are calculated, the objective function of total profit is set up, and the optimal scheme is solved by simulated annealing algorithm. The results show that the decision tree model and simulated annealing algorithm can effectively solve the optimization problem of multi-stage detection decision in the production process of enterprises, so as to help enterprises achieve profit maximization.