Analytical and simulation comparison of robotic warehouse picking cycles
Reconstruction and Discrete-Event Simulation Assessment of an Analytical
RoboticsArtificial Intelligence
Summary
This paper looks at how robots pick items in warehouses by comparing two ways to estimate the time it takes. One way uses math formulas and the other uses computer simulations that mimic real processes. The researchers tested many different setups and found the two methods give very similar results, though they aren’t exactly the same. This helps understand how well the math models work for planning robot movements in warehouses.
What this means in practice
- •For warehouse automation engineers: Improve planning and design of robotic picking systems by using benchmarked cycle time estimates from both simulations and analytical models.
- •For logistics system analysts: Use the close agreement between simulation and analytical results to validate warehouse picking performance predictions under varied conditions.
Authors
Mohsen Reda Mahmoud Moursi, Ahmed Stohy
Abstract
This article presents an analytical-DES benchmark for intra-aisle robotic picking in automated warehousing. The investigated cycle combines horizontal shuttle travel, tote handling and item picking at storage locations. Simulation results are compared with the analytical formulation of Lerher et al. under the same rack geometry, shuttle-motion, handling and picking conditions, and the same definitions of cycle time and derived item throughput. Reproducing the published analytical tables requires the distance interpretation dES = 1.5 m and dTB = 0 m. The analytical-DES benchmark comprises 180 scenarios formed from three rack lengths, two paired motion profiles and n = 1 to 30 visited picking locations, with ten simulation replications per scenario. The six n = 1 cases are reported separately because no travel-between component is present. Across the remaining 174 scenarios, the mean absolute relative cycle-time discrepancy is 0.8655%, the median is 0.6758%, and the maximum is 7.0370%. The mean absolute error is 2.411 s and the RMSE is 2.744 s. The simulated scenario-mean cycle time is below the analytical value in all 174 principal scenarios. The results therefore show close numerical correspondence under the tested conditions, but not exact agreement, formal statistical equivalence, or validation against an operating warehouse.