Algorithms for Multiple Autonomous Robotic Systems in Warehouse Order Picking Operations: A General Review

Warehouses have always been indispensable components of supply chains for the smooth flow of materials from supplier to customer. Expansion of e-commerce, requiring faster delivery of smaller orders, promoted stock management and consequently warehouse operations. The search for increased efficiency in stock management and warehouse operations yielded the deployment of autonomous robotic systems. One example of such a system is Amazon’s Kiva system. It has been claimed that the Kiva system reduces the unnecessary time and cost of pickers close to zero. These recent developments are invaluable since order picking is the most labor-intensive and capital-intensive operation in all warehouse operations. An enhancement in the order picking process decreases warehouse expenses, increases the throughput of the warehouse and customer service level, and implicitly improves the supply chain performance. Hence, intelligent systems are essential to optimize the order fulfillment process. Increasing the throughput and the speed of the system requires the employment of more pickers. Operating autonomous robotic systems simultaneously is more sophisticated. The problem of batching and routing jointly is complex by itself. When it is required to embed congestion and collision prevention into the batching and routing of multiple pickers, the problem can get prohibitively complex. In this study, we review algorithms for the order picking problem both for single picker and multiple picker cases which form the basis for the development of intelligent batching and routing algorithms for multiple autonomous robotic systems.