AI-Powered Warehouse Management: Autonomous Mobile Robots and Advanced Optimization with NFC, MILP, and Swarm Algorithms

摘要

Warehouse automation, coupled with slightly increased outlay executions and a task distribution process, has become an exceedingly fundamental supply chain operation in modern-day reality. In this article, an AI-Refined framework is proposed that combines Automated Mobile Robots, Near-field Communication, Mixed Integer Linear Programming, and swarm algorithms that would result in maximal efficiency within the warehouse. The proposed design examines swarm techniques for task distribution, and NFC for seamless data sharing, MILP for tactical optimization, combined with AMRs for automation; all directed toward reducing inefficiencies and enabling real-time decision-making. The main objectives of this framework include maximization of resource utilization, enhancement of efficiency, scalability, adaptability, and support for dynamic supply chain management. Experimental results showed that the proposed framework performed better than conventional techniques, with an accuracy of 94%, an F1 score of 95%, and a scalability score of 92%. This gives an insight that integrated methods can really help ameliorate the operations in the warehouse. Finally, this research has shown the enormous capability of AI automation with regards to the resource management, process enhancement, and solving critical issues set in warehousing that will start offering a more intelligent and flexible logistics solution.