Research on production decision problem based on dynamic programming and genetic algorithm

With the rapid development of the manufacturing industry and the increasingly fierce market competition, enterprises face numerous complex decision-making challenges while pursuing efficient and high-quality production. This paper systematically analyzes the optimal strategies under different production scenarios using methods such as dynamic programming and genetic algorithms, aiming to help enterprises achieve optimal resource allocation, thereby maximizing production benefits and competitiveness. The production process of enterprises involves multiple components and multiple processes. To optimize the multi-stage decision-making process in the production of a certain product, this paper first adopts a dynamic programming model, expanding the decision-making stages to five stages: component inspection, semi-finished product inspection, disassembly of defective semi-finished products, finished product inspection, and handling of defective finished products. Through the method of backward analysis, a decision-making model is constructed that can capture the chain reactions between decisions at each stage and the long-term optimal solution, ensuring the efficiency and optimization of the entire production process. Finally, based on dynamic programming, a genetic algorithm is used to solve the problem, selecting the optimal production plan with the lowest cost and highest efficiency.