An Application of NB-GA Model: A Study of Logistics Performance and Economic Attributes

These days, the majority of logistics systems depend on AI strategies to enhance services and increase outcomes. Online marketplaces are an exception to this rule. The diversity and complexity of resource allocation and job scheduling, however, remain formidable challenges in dynamic systems. Therefore, solid algorithms supporting complex models are required. Adding sophisticated models to scheduling systems is one way to boost logistical efficiency. Therefore, controlling system resources is still crucial for improving job scheduling while considering the interaction implications and logistics system needs. Model training, feature selection, and preprocessing are all dependent on sequencing. Normalization is a part of the preprocessing phase. According to the Normalized Z-Scores and the Gaussian Distribution. PCA and correlation are the two main components of feature selection. One way statisticians quantify the degree of linear relationship between two variables in a set is with the Pearson correlation coefficient. When dealing with data that is multidimensional, principal component analysis (PCA) is one of the most popular multivariate statistical methods. Training an NB-GA requires exact regulation of attributes. Modern NB and GA algorithms are quaint when compared to this technique. The findings showed a significant improvement in accuracy, with a grade of 97.22%.