Simulation of Manufacturing Procurement Cost Control Model Based on Hybrid Genetic Algorithm

In today's fierce market competition, almost all enterprises will pay attention to purchasing cost control, and purchasing activities are the areas with the greatest cost, especially in manufacturing, the value of purchased raw materials usually exceeds half of product sales. In this paper, the simulation research of manufacturing procurement cost control model based on hybrid GA (Genetic algorithm) is carried out. The hybrid GA used in this paper is a serial structure, and the initial population of ACO (Ant colony optimization) is generated by using the suboptimal solution of GA, and finally the optimal solution is found through ACO. The local search ability of GA is improved, and the diversity of population is improved by the use of elite preservation strategy, so that the degradation of offspring is also well avoided. The simulation results show that this algorithm improves the optimization ability and efficiency of the algorithm and reduces its own volatility through niche technology and adaptive crossover strategy. The algorithm is superior to GA in performance. Hybrid GA is effective and can solve the optimization problem of manufacturing procurement cost control with multiple cycles and multiple raw materials.