Research on Production Process Problems Based on Dynamic Programming

Abstract

This paper addresses the quality inspection and cost optimization issues faced by an electronic product manufacturing enterprise by proposing a hybrid decision-making model based on sequential detection and dynamic programming. A truncated sequential confidence inspection model is constructed by integrating sequential sampling and likelihood ratio test methods. Under the condition of a nominal defective rate of 10%, the model enables dynamic adjustment of the sampling volume. When the actual defective rate is 20%, the model can reject an entire batch of spare parts with a relatively small number of samples. When the defective rate is close to the nominal value, it can reduce the number of samples to avoid excessive inspection. Meanwhile, a multi-stage dynamic programming model is established to minimize costs, and the optimal decision-making schemes for six typical production scenarios are derived and verified. This model provides decision-making support for enterprises that balances quality control and cost-effectiveness.