Research on Production Decision Making Based on Genetic Algorithm and Simulated Annealing Algorithm

In this paper, the design of sampling and testing scheme, the decision of production process, the decision of multi-process production and the decision optimization of the effect of sampling error are studied in the context of an electronic product enterprise [1]. Firstly, the sampling and testing scheme is designed, the optimal sample size is determined by normal distribution Z-test, and the cost-effectiveness of different sampling strategies is verified [2]; secondly, a multi-stage decision-making model based on cost-effectiveness is constructed [3], and the optimal decision of a single process is sought by using the 0–1 integer programming and the genetic algorithm [4]; finally, the model is extended to the multi-process production, and combined with the simulated annealing algorithm to optimize the decision-making scheme for complex production processes [5]. The key calculation results show that the optimization scheme significantly improves the production efficiency and product quality.