Optimization study of production decision based on Monte Carlo simulation and particle swarm optimization algorithm

In this paper, a solution based on Monte Carlo simulation and particle swarm optimization algorithm is proposed for the problems of spare parts monitoring and production process optimization in the production process of enterprises. A Monte Carlo simulation-based sampling and testing method is designed for spare parts incoming inspection decision, which evaluates the inspection accuracy and cost under different sample sizes by simulating a large number of random samples through a large number of random simulations. Thus, the optimal sampling scheme is determined. For the multi-stage decision optimization in the whole production process, an integer linear programming model is constructed and optimized and solved using particle swarm optimization algorithm. A large number of decision combinations and the foraging behavior of bird flocks are simulated to find the optimal detection, dismantling and processing strategies to minimize the total cost and improve the product quality. The final analysis verifies the effectiveness of the proposed method and provides scientific and reasonable decision support for enterprises in complex production environments.