NSGA‐II algorithm‐based automated cigarette finished goods storage level optimization research

With the growth of Internet of Things technology, more and more businesses are implementing automated cargo storage systems. By using an appropriate automated storage space allocation model, these businesses can significantly reduce their storage pressure while saving money on logistics and increasing the effectiveness of their product distribution. Therefore, the study is based on the non‐dominated sorting genetic algorithms II (non‐dominated sorting genetic algorithm, NSGA II), which combines the three basic principles of space allocation as the objective function applied to the allocation model of the algorithm, in order to optimize the space model for automated storage of finished cigarettes. The algorithm is run to obtain 20 Pareto solutions and examine their three objective functions. The experiment's findings revealed, after optimizing the NSGA‐II algorithm in this study, the average reduction rate of shipping efficiency is 32%, the average reduction rate of shelf stability is 54%, and the average reduction rate of product correlation is about 77%, indicating that the algorithm optimization is highly effective. The NSGA‐II algorithm produces a more compact and organised arrangement of the final cigarettes than the previous solution, utilising storage space to the tune of 34.7% more than before. Overall, the distribution of products on each shelf is reasonable, following the storage principle of “heavy bottom and light top”. The arrangement of similar finished cigarettes is also more concentrated, which is in line with the relevance of products.