The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres

David Ming Huang Chiang, Chia Ping Lin, Mu-Chen Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

119 Scopus citations

Abstract

Among distribution centre operations, order picking has been reported to be the most labour-intensive activity. Sophisticated storage assignment policies adopted to reduce the travel distance of order picking have been explored in the literature. Unfortunately, previous research has been devoted to locating entire products from scratch. Instead, this study intends to propose an adaptive approach, a Data Mining-based Storage Assignment approach (DMSA), to find the optimal storage assignment for newly delivered products that need to be put away when there is vacant shelf space in a distribution centre. In the DMSA, a new association index (AIX) is developed to evaluate the fitness between the put away products and the unassigned storage locations by applying association rule mining. With AIX, the storage location assignment problem (SLAP) can be formulated and solved as a binary integer programming. To evaluate the performance of DMSA, a realworld order database of a distribution centre is obtained and used to compare the results from DMSA with a random assignment approach. It turns out that DMSA outperforms random assignment as the number of put away products and the proportion of put away products with high turnover rates increase.

Original languageEnglish
Pages (from-to)219-234
Number of pages16
JournalEnterprise Information Systems
Volume5
Issue number2
DOIs
StatePublished - 1 May 2011

Keywords

  • Association rules
  • Business planning and logistics
  • Data mining
  • Enterprise information systems
  • Order picking
  • Storage assignment
  • Warehousing

Fingerprint Dive into the research topics of 'The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres'. Together they form a unique fingerprint.

Cite this