Warehouse Automation

Warehouse automation aims to optimize efficiency and throughput in logistics operations through robotic systems and advanced algorithms. Current research heavily focuses on multi-agent systems, employing deep reinforcement learning, queueing theory, and novel pathfinding algorithms (like conflict-based search) to coordinate robots for tasks such as order picking, shelf rearrangement, and material handling, often considering heterogeneous robot capabilities and dynamic environments. These advancements promise significant improvements in warehouse productivity, cost reduction, and resilience to workforce fluctuations, impacting both the scientific understanding of multi-agent systems and the practical efficiency of supply chains.