Aggregation of orders in distribution centers using data mining

Mu-Chen Chen*, Cheng Lung Huang, Kai Ying Chen, Hsiao Pin Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

77 Scopus citations


This paper considers the problem of constructing order batches for distribution centers using a data mining technique. With the advent of supply chain management, distribution centers fulfill a strategic role of achieving the logistics objectives of shorter cycle times, lower inventories, lower costs and better customer service. Many companies consider both their cost effectiveness and market proficiency to depend primarily on efficient logistics management. Warehouse management system (WMS) presently is considered a key to strengthening company logistics. Order picking is routine in distribution centers. Before picking a large set of orders, effectively grouping orders into batches can accelerate product movement within the storage zone. The order batching procedure has to be implemented in WMS and may be run online many times daily. The literature has proposed numerous batching heuristics for minimizing travel distance or travel time. This paper presents a clustering procedure for an order batching problem in a distribution center with a parallel-aisle layout. A data mining technique of association rule mining is adopted to develop the order clustering approach. Performance comparisons between the developed approach and existing heuristics are given for various problems.

Original languageEnglish
Pages (from-to)453-460
Number of pages8
JournalExpert Systems with Applications
Issue number3
StatePublished - 1 Jan 2005


  • Association rules
  • Data mining
  • Distribution centers
  • Order batching

Fingerprint Dive into the research topics of 'Aggregation of orders in distribution centers using data mining'. Together they form a unique fingerprint.

Cite this