Study shows flexible human-robot collaboration can significantly improve warehouse productivity

One of my clients has conducted peer-reviewed research on how to best combine humans and robots for ideal results. If the future of warehouse work belongs to humans and robots working side by side, a key question remains: What is the most effective way for them to collaborate?

New research published in Transportation Science, a journal of INFORMS, suggests the answer may be more flexible than many warehouse operators expect. The study, "Picking the Best Bot: Collaboration Strategies for Humans and Bots in Order Pick Systems with Traveling Salesman Problem Routing," found that under many real-world conditions, warehouse workers achieve higher productivity when they dynamically switch among multiple autonomous mobile robots rather than work exclusively with a single robot.

The findings challenge a common assumption that fixed human-robot pairings are the most efficient approach.

Instead, researchers found that a flexible "swarm" policy, in which workers collaborate with different robots throughout a shift, often outperforms more rigid one-to-one assignment strategies. As robots become faster and more plentiful, the advantages of the swarm approach grow even stronger.

The study arrives as warehouses around the world continue investing heavily in automation to meet growing demand while addressing labor shortages and operational pressures. While autonomous mobile robots have already improved warehouse efficiency, relatively little research has examined how the structure of human-robot collaboration itself affects performance.

"This is not simply a question of adding more robots," said Mahdi Ghorashi Khalilabadi of Rotterdam School of Management, Erasmus University and lead author of the study. "Our findings show that the way humans and robots are organized can have a major impact on throughput. When robots are faster or more plentiful than human pickers, allowing flexible collaboration can significantly improve performance."

The researchers analyzed how workers and robots performed across a wide range of operating conditions commonly found in warehouses, including differences in travel times, order sizes and facility layouts.

The study focused on two of the most common human-robot collaboration approaches. Under a swarm policy, workers interact with multiple robots while completing orders. Under a system-directed policy, workers complete an entire order with a single robot before moving to the next task.

Using analytical modeling and simulation, the research team evaluated more than 12,000 warehouse scenarios.

The results were clear.

The swarm policy generally delivered higher order throughput than the system-directed approach. Performance gains increased as the ratio of robots to workers grew and as robots gained speed advantages over human pickers.