Human-robot collaborative picking optimization in robotic mobile fulfillment systems

In a robotic mobile fulfillment system, mobile robots transport pods containing commodities from storage areas to a picking station, where a human picker works to fulfill orders. Such a human-robot cooperative operation process leads to optimization problems of pod selection, multi-robot task allocation, and picking scheduling. This work focuses on their joint optimization to minimize makespan, i.e., the completion time of the last order-picking task. To efficiently solve the concerned problem, this work adapts a Tabu search algorithm by introducing problem-specific encoding and decoding procedures, variable tabu duration, and memory-based penalty moves. Experimental results indicate the significant advantages of the designed algorithm in solving accuracy and stability compared with other intelligent optimization algorithms.