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

Abstract

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.