Umělá inteligence v konceptu WMS a Industry 4.0

Abstract

The dissertation addresses the design of an intelligent 3D system for online monitoring and control of warehouse racks using modern optimization methods and machine learning. The primary goal is to enhance warehouse logistics within the Industry 4.0 framework by utilizing 3D cameras and AI technologies. These technologies eliminate storage errors, optimize warehouse processes, and increase efficiency, thereby reducing operational costs. Errors most commonly occur in inventory and rack placement, making the use of 3D cameras crucial for easy creation of warehouse models and monitoring of missing racks. The proposed methodology overcomes the practical implementation barriers of existing models and provides managers with a decision support tool, enabling optimal resource allocation and improvement of logistics processes. The model is open and widely applicable across various industries, addressing the limitations of current non-public and difficult-to-implement models. The system uses artificial intelligence that continuously learns and improves its ability to recognize different types of racks and optimize warehouse processes. AI analyzes data from cameras, identifies specific types of racks, and in the future, will be able to suggest improvements to the warehouse system, further increasing efficiency and reducing costs. This comprehensive tool will provide users with a methodology for optimizing warehouse processes, implementing new spatial arrangements, and updating based on the collected data, significantly contributing to the automation and optimization of warehouse logistics.