Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search

Fueled by advances in artificial intelligence, robotic process automation is impacting virtually every sector of the economy (McKinsey Global Institute 2017). The logistics sector lies at the core of this transformation: autonomous mobile robots are being deployed in tens of thousands of manufacturing and distribution facilities with a near-term $10-50 billion market potential (Grand View Research 2021, ABI Research 2021). A predominant operating model, shown in Figure 1, involves part-to-picker warehousing operations, which relies on robotic agents transporting shelves of inventory from a storage location to a workstation for a human operator to fulfill orders and back to a storage location. Robotic operations can improve throughput and working conditions by letting human workers focus on the more productive tasks, while improving system reliability. Yet, to truly take advantage of automation opportunities, modern warehousing systems require dedicated decision support tools to manage large robotic fleets and human-robot interactions in high-density operations. At the core of robotic process automation lies the computer vision, sensing, mapping and robotic technologies to empower autonomous agents--in our case, robots capable to move shelves of inventory. A subsequent problem involves control mechanisms to coordinate multiagent systems--in our case, to avoid conflicts and collisions between robots.