Research on Intelligent Optimisation Decision Making of Production Process Based on Adaptive Sampling and Genetic Algorithm

This paper focuses on the application of optimisation algorithms for intelligent optimisation decisions in production processes. Focusing on adaptive sampling methods, 0-1 planning models and genetic algorithms construct mathematical models to optimise the decision-making of inspection and disassembly and maximise the revenue index. Firstly, the simulation generates spare parts data and compares the four sampling and detection methods, and finally identifies adaptive stepwise sampling as the best choice; secondly, the decision variables are analysed by the 0-1 planning model to establish a functional relationship between the revenue index and the cost and sales, and to identify the key influencing factors; and finally, the 0-1 integer planning model is optimised to search for the optimal decision combinations by using the global optimisation algorithm. Sampling errors are introduced to verify the stability of the adaptive stepwise sampling method in decision making and its effect on the revenue index.