Hybrid Intelligent Framework for Logistics Routing and Transportation Optimization in Supply Chain Management

Abstract

In order to make modern supply chains more profitable and environmentally sustainable, logistics routing and transportation optimisation are crucial. Efficient transportation and routing strategies are crucial in logistics operations because they cover the whole economic lifecycle, from sourcing raw materials to ultimate delivery. Improved data quality was achieved in this study by the use of data preparation techniques like feature engineering, encoding categorical variables, addressing missing values, and transformation. Hierarchical clustering and K-means were two of the clustering approaches used for comparative analysis in order to classify logistics providers. In addition, Differential Evolution (DE), GA, Simulated Annealing (SA), and Prism Refraction Search (PRS) were employed as optimisation methods to enhance transportation and logistics routing. To strike a better balance between global and local search capabilities, a new hybrid approach called BiPRS-SA was created by combining the strengths of these algorithms. A high accuracy of 97.19% was attained using an ensemble modelling technique, suggesting steady and robust prediction performance, and the results show that the suggested hybrid method greatly enhances optimisation efficiency. The combination of ensemble methodologies with modern optimisation algorithms improves transportation and logistics optimisation decision-making, which in turn leads to higher sustainability, lower costs, and operational efficiency.