Combination of SKU in POD Assignment in Robotic Mobile Fulfillment Systems

Dinda Tria Pratiwi*, Shuo Yan Chou, Suparno, Nani Kurniati

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Industrial Engineering and Applications - ICIEA 2024
EditorsLoon Ching Tang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages185-195
Number of pages11
ISBN (Print)9789819764914
DOIs
Publication statusPublished - 2025
Event11th International Conference on Industrial Engineering and Applications, ICIEA 2024 - Hiroshima, Japan
Duration: 17 Apr 202419 Apr 2024

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference11th International Conference on Industrial Engineering and Applications, ICIEA 2024
Country/TerritoryJapan
CityHiroshima
Period17/04/2419/04/24

Keywords

  • ABC classification
  • Cluster
  • Combination of SKU in Pod assignment
  • Pile-on
  • RMFS

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