TY - GEN
T1 - Combination of SKU in POD Assignment in Robotic Mobile Fulfillment Systems
AU - Pratiwi, Dinda Tria
AU - Chou, Shuo Yan
AU - Suparno,
AU - Kurniati, Nani
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - The Robotic Mobile Fulfillment System (RMFS) is widely used in e-commerce warehouses and includes pods, storage locations, workstations for order picking or replenishment, and mobile robots. Decision-making in warehouse systems can be strategic, tactical, or operational. Product assignment, a tactical decision, significantly impacts picking efficiency. This research concentrates on SKU-to-pod allocation, the preliminary step before simulation, involving two phases: product grouping and product combination. Effective product grouping can enhance picking efficiency, while product combination in pod allocation aims to optimize the units picked per pod, termed pile-on, thereby reducing the reliance on Automated Mobile Vehicles (AMVs). Three scenarios were examined: Random Baseline, Class Combination, and Cluster Combination. Class Combination employs ABC classification to sort SKUs into classes using Pareto’s principle, correlating SKU percentages with order frequency percentages. In contrast, Cluster Combination considers product dimensions for pod placement. Simulations determine the optimal pile-on by evaluating the units picked per pod visit to the pick station, with a higher unit count per visit indicating reduced mobile robot transport and increased efficiency. The simulations revealed that Cluster Combination, the final scenario, achieved the best pile-on, with improvements of 30.82% and 8.19% over the first two scenarios, respectively. These results were validated using one-way ANOVA.
AB - The Robotic Mobile Fulfillment System (RMFS) is widely used in e-commerce warehouses and includes pods, storage locations, workstations for order picking or replenishment, and mobile robots. Decision-making in warehouse systems can be strategic, tactical, or operational. Product assignment, a tactical decision, significantly impacts picking efficiency. This research concentrates on SKU-to-pod allocation, the preliminary step before simulation, involving two phases: product grouping and product combination. Effective product grouping can enhance picking efficiency, while product combination in pod allocation aims to optimize the units picked per pod, termed pile-on, thereby reducing the reliance on Automated Mobile Vehicles (AMVs). Three scenarios were examined: Random Baseline, Class Combination, and Cluster Combination. Class Combination employs ABC classification to sort SKUs into classes using Pareto’s principle, correlating SKU percentages with order frequency percentages. In contrast, Cluster Combination considers product dimensions for pod placement. Simulations determine the optimal pile-on by evaluating the units picked per pod visit to the pick station, with a higher unit count per visit indicating reduced mobile robot transport and increased efficiency. The simulations revealed that Cluster Combination, the final scenario, achieved the best pile-on, with improvements of 30.82% and 8.19% over the first two scenarios, respectively. These results were validated using one-way ANOVA.
KW - ABC classification
KW - Cluster
KW - Combination of SKU in Pod assignment
KW - Pile-on
KW - RMFS
UR - http://www.scopus.com/inward/record.url?scp=85211338398&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-6492-1_15
DO - 10.1007/978-981-97-6492-1_15
M3 - Conference contribution
AN - SCOPUS:85211338398
SN - 9789819764914
T3 - Lecture Notes in Mechanical Engineering
SP - 185
EP - 195
BT - Proceedings of the 11th International Conference on Industrial Engineering and Applications - ICIEA 2024
A2 - Tang, Loon Ching
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Industrial Engineering and Applications, ICIEA 2024
Y2 - 17 April 2024 through 19 April 2024
ER -