Modern warehouse operations need rapid, reliable, and adaptable systems to respond to the dynamic order quantity and scarce resources. However, current robotic warehouse systems are prone to collisions, inefficient task scheduling, and real-time learning flexibility issues. This paper introduces the Warehouse Management and Handling System (WMHS) framework, which integrates bull-optimized enhanced neural networks to improve collision-free scheduling and routing. The system includes an optimization layer for order batching, multi-agent coordination, adaptive path planning, and intelligent task robotics, which are able to process the changing demands and supply conditions. During the allocation, the robots are selected based on their bull characteristics, which help minimize congestion and downtime. According to the chosen scheduling procedure, the path and effective energy scheduling are predicted with a minimum error rate. The core process employs demand-aware learning techniques to order complexity and the available robots. Experiment results proved to be surpassing on critical benchmarks in which the introduced system accomplished order completion in 4.2 min, 98.8% on-time delivery, and 98.2% batch efficiency. The system additionally upholds a high throughput of 325 items per unit time, robot utilization above 97%, low convergence time, and stable learning across training episodes. The proposed WMHS serves e-commerce, retail distribution, and inventory hubs, providing a fully automated intelligent solution for next-generation warehouse automation.
Xijing Ou (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: