A Knowledge-based Decision Support Framework for Wave Put-away Operations of E-commerce and O2O Shipments
- DOI
- 10.2991/iwama-16.2016.16How to use a DOI?
- Keywords
- Logistics and supply chains; E-commerce; Wave management; Case-based reasoning
- Abstract
Warehouse put-away and storage operation is one of the typical internal operations in a warehouse or a distribution center, alongside receiving, picking, packing and shipping. Under today's e-commerce business environment, logistics practitioners are required to efficiently handle e-commerce shipments in warehouses. However, owing to the fundamental differences in the order nature and handling requirements between traditional orders and e-commerce orders, logistics service providers are struggled to handle e-commerce shipments competently. In this paper, a knowledge-based wave put-away decision support system is proposed, which incorporates cloud database for real-time data update and retrieval, and case-based reasoning technique for generating put-away solutions for e-commerce orders based on historical put-away knowledge. The proposed system is validated through a pilot study in a case company. The efficiency of order put-away operation is enhanced by grouping fragmented e-commerce orders at the inbound for performing put-away operations in a wave pattern, which enables logistics practitioners to yield cost-saving and operating efficiency in e-commerce order handling.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - K.H. Leung AU - K.L. Choy AU - Migar M.C. Tam AU - Y.Y. Hui AU - H.Y. Lam AU - Y.P. Tsang PY - 2016/11 DA - 2016/11 TI - A Knowledge-based Decision Support Framework for Wave Put-away Operations of E-commerce and O2O Shipments BT - Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation PB - Atlantis Press SP - 86 EP - 91 SN - 2352-5428 UR - https://doi.org/10.2991/iwama-16.2016.16 DO - 10.2991/iwama-16.2016.16 ID - Leung2016/11 ER -