Large Scale Sampling Model and Optimization Algorithm Based On Dynamic Programming and Monte Carlo Simulation

The qualified rate of product parts to determine the excellence of the product, for large-scale parts sampling and inspection of the high cost, nowadays, large-scale parts sampling and testing methods are scarce, how to ensure that the qualified rate of parts and reduce the cost of sampling and inspection is the primary task. This paper proposes a sampling method for large-scale based on this. Firstly, this paper establishes a sequential sampling inspection method model based on the parts data of an electronic enterprise, secondly, establishes a cost-benefit model based on different sampling costs, and then introduces the method of dynamic programming on this basis to optimize the cost-benefit of sampling, and finally, conducts simulated sampling in the use of Monte Carlo simulation to obtain the sampling decision of the enterprise. This method overcomes the high-cost difficulties of large-scale sampling in reality and provides an effective method for sampling large totals.