Dynamic Warehouse Workflow Optimization via Reinforcement Learning: Integrating Q-Learning and Priority-Based Routing

This paper presents the development of a workflow optimization algorithm for warehouse environments, based on reinforcement learning techniques. The objective is to improve the efficiency of operations such as order picking, routing, and storage by allowing the system to learn optimal strategies through interaction with its environment. This approach utilizes reward-based learning to dynamically adapt to variations in demand, warehouse layout, and resource availability. Experimental results demonstrate significant improvements in throughput and resource utilization compared to traditional heuristic methods, highlighting the potential of reinforcement learning for intelligent and adaptive warehouse management systems.