Optimization of Robotic Mobile Fulfillment System Considering Robot Efficiency and Picker Energy Consumption - Li, Yi; Wang, Nan; Fan, Fan (International Journal of Industrial Engineering: Theory, Applications and Practice, 2025)
Robotic mobile fulfillment system (RMFS) is a human-robot collaborative order-picking system. The substantial workload at the picking stations not only diminishes picker efficiency but also poses a significant risk of injury to their physical health. Therefore, it is essential to incorporate human factors into studying system efficiency optimization. This research analyzes item storage assignment, picker energy consumption, and the working environment to enhance robot efficiency while reducing picker energy expenditure. A two-stage optimization framework is proposed: first, correlation analysis and clustering of items are performed based on different shelf levels, effectively minimizing robot movement, with more pronounced effects observed on multi-level shelves; second, item storage assignments are optimized based on picker energy consumption, revealing that the arrangement of shelves and picking platform (working environment) significantly influences energy expenditure, and several optimal storage configurations that minimizes picker energy consumption. Finally, the results offer tailored decision support for managers with varying operational priorities.