Intelligent Warehousing using IoT and Multi-Agent Reinforcement Learning for Dynamic Storage and Resource Optimization
Intelligent warehousing uses artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), robotics to automate and enhance logistics. It helps in changing conventional storage into efficient data-enabled ecosystems. When we think of any warehouse operations, Dynamic demand, high product variety, and complex resource coordination requirements are considered as key factors. In such dynamic environments, Conventional rule-based and centralized optimization methods seem to have limited adaptability. This paper presents an intelligent warehousing framework that integrates IoT sensing with Multi-Agent Reinforcement Learning (MARL) to enable dynamic storage allocation and resource optimization. Real-time data from IoT devices is used to create environmental states that show the state of the inventory, where items are, and how materials are being handled in the warehouse. Multiple autonomous agents corresponding to warehouse entities collaboratively learn decision policies to optimize storage assignment and task allocation. The proposed approach is implemented and evaluated in a simulated warehouse environment. System performance is assessed by using operational metrics including order fulfillment time, storage utilization, travel distance, average queue length and throughput. Experimental results demonstrate that the proposed IoT-enabled MARL framework improves adaptability and operational efficiency compared to static and rule-based strategies, highlighting its potential for next-generation intelligent warehouse systems.