Optimization Algorithm of Intelligent Warehouse Management System Based on Reinforcement Learning

Abstract

An Intelligent Warehouse Management System (IWMS) represents a technological leap forward in the realm of logistics and supply chain management. This sophisticated system integrates a suite of cutting-edge technologies, including artificial intelligence, machine learning, and the Internet of Things, to revolutionize the way warehouses operate. The primary focus is on the construction and performance evaluation of a robust big data prediction model within a cloud computing environment. The advent of big data and cloud computing has revolutionized the field of Logistics, offering immense potential for advanced data analysis and prediction. This research presents the development and evaluation of a robust prediction model for IWMS in Logistics applications. The proposed model incorporation of Reliable Discrete Variable Topology (RDVT) into the prediction model. RDVT introduces a topological data structure that enhances data reliability and ensures the integrity of Logistics information. The construction and training of the prediction model are meticulously detailed, encompassing data preprocessing, feature extraction, clustering, classification, and model evaluation. Additionally, the integration of fuzzy clustering with a reinforcement learning algorithm enhances the model's ability to handle uncertainty and imprecision in logistics management data. The advancement of Logistics in warehouses introduces the Reliable Discrete Variable Topology (RDVT) and a big data prediction model based on fuzzy clustering with a reinforcement learning algorithm in a cloud computing environment. The model's performance is rigorously assessed through extensive experimentation, including accuracy, precision, recall, and F1-score measurements.