Research on supply chain planning based on genetic algorithm and long short-term memory

With the integration of intelligent algorithm into supply chain process, fficiency planning has been further improved through automatic prediction and decision-making. Although algorithms are developing, their challenges including real-time nature complexity scenarios hinder true potential. In this study, we proposed an genetic (GA)-long short-term memory (LSTM) neural network to solve various optimization problems for from suppliers production enterprises. Specifically, determine stable suppliers, first constructed technique order preference by similarity ideal solution (TOPSIS) model quantitatively evaluate each supplier, rationality index weight TOPSIS can be enhanced entropy method. Finally, GA LSTM were used decision-making problem in raw material chain. Our results indicate that not only efficiently decision chain, but it also reasonably analyzes quantitatively.