Towards energy-efficient Robotic Mobile Fulfillment System: Hybrid agent-based simulation with DEA-based surrogate machine learning

The rapid growth of retail e-commerce has increased demand for warehouses to handle large volumes and diverse SKUs. To meet these demands, Robotic Mobile Fulfillment System (RMFS) is widely adopted. However, the automation in RMFS significantly raises energy consumption. The challenge is that the dynamic complexity of RMFS operations poses a major challenge in improving energy efficiency. This research proposes a hybrid optimization model to optimize traffic policy, routing strategy, number of robots, and robot’s max speed for reducing energy consumption while maintaining throughput rate. We first formulated a realistic RMFS energy consumption. A new priority rule for traffic policy was then proposed to reduce unnecessary stoppages. Two routing strategies namely Aisles Only and Underneath Pod were evaluated. Agent-based model was finally developed. Simulation experiment shows that the proposed priority rule reduces energy consumption by 3.41 % and increases the throughput by 26.07 % compared to FCFS. Further, global optimization was performed by first unifying conflicting objectives into a single-efficiency objective using Data Envelopment Analysis. Surrogate-based machine learning was then fitted and optimized via metaheuristic algorithm. The near-optimal configuration for RMFS was achieved by implementing the Priority Rule as traffic policy, Underneath Pod as routing strategy, 26 as number of robots, and 1.372 m/s as max speed. ANOVA reveals that the number of robots is the most influential factors to overall RMFS performance.