Based on Monte Carlo Simulation Optimization Research in Corporate Production Decision-Making

In this paper, we first select relevant data, comprehensively use dynamic programming combined with Monte Carlo simulation to establish a model, and use Python programming to realize and visualize the entire calculation process. For the metrics of each situation, we use the entropy weight method to make scoring decisions on the indicators of different dimensions. Finally, it is concluded that the cost price required for production in case 1 is the most reasonable, the defective rate is the lowest, and the comprehensive score is the highest. It also provides a reference for the production decision-making of the enterprise, which can be flexibly adjusted according to the situation to achieve the best production management effect, reduce the production loss of the enterprise, and bring greater benefits to the enterprise.