A Novel Intelligent Agent Equipped with Machine Learning for Route Optimization in Effective Supply Chain Management for Seasonal Agricultural Products

Abstract

The ever-changing context of contemporary supply chain management, effective route optimization has emerged as a crucial component for companies looking to improve their operational efficiency. A challenging issue is presented by the complicated web of transportation networks, a variety of delivery places, and constantly shifting parameters like Temperature, Humidity etc. The combination of cutting-edge technology, particularly Machine learning (ML), deep learning (DL) and Artificial intelligence (AI), has emerged as a game-changing option to solve. This project presents the comparative analysis of machine learning including deep learning model for reference of accuracy, the models specifically designed for crop prediction are implied and artificial intelligence algorithms for supply chain management route optimization. This method seeks to transform the way and distribution networks are managed by utilizing historical route data, real-time information, and sophisticated learning algorithms. The ultimate goal is to introduce efficiencies that transcend traditional approaches, resulting in improved efficiency in delivery times, and optimal management of agricultural seasonal inventory. Through the seamless integration of ML, DL and AI technologies, this innovative solution endeavors to break the complexity level of contemporary supply chain management, ushering in a new era of operational excellence and efficiency.