An association-based clustering approach to order batching considering customer demand patterns

Mu-Chen Chen*, Hsiao Pin Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

119 Scopus citations

Abstract

Research on warehousing systems has gained interest since the 1980s, reflecting the fact that supply chain management has pursued a demand-driven organization with high product variety, small order sizes, and reliable short response times throughout the supply chain. This market trend has affected warehouse management and operations tremendously. Order batching in a warehouse attempts to achieve high-volume order processing operations by consolidating small orders into batches. Order batching is an essential operation of order processing in which several orders are grouped into batches. This paper describes the development of an order batching approach based on data mining and integer programming. It is valuable to discover the important associations between orders such that the occurrence of some orders in a batch will cause the occurrence of other orders in the same batch. An order-clustering model based on 0-1 integer programming can be formulated to maximize the associations between orders within each batch. From the results of several test problems, the proposed approach shows its ability to find quality solutions of order batching problems.

Original languageEnglish
Pages (from-to)333-343
Number of pages11
JournalOmega
Volume33
Issue number4
DOIs
StatePublished - 1 Aug 2005

Keywords

  • 0-1 integer programming
  • Association rule
  • Data mining
  • Order batching
  • Warehousing

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