Adaptive storage location assignment for warehouses using intelligent products

Nikolaos Tsamis, Vaggelis Giannikas, Duncan McFarlane, Wenrong Lu, James Strachan

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Due to rapidly changing customer preferences, order-picking has become a bottleneck for the efficiency of the order fulfilment process and in turn a burden to the customer satisfaction of warehouse companies. Improved storage location assignment of newly delivered products is one effective method for improving the picking performance. However, most of the available storage policies provide static solutions that do not deal with frequent changes in order demand characteristics. This study aims to identify a potential solution by developing a distributed, adaptive strategy for the storage location assignment problem and follows the product intelligence paradigm for its implementation. The efficiency of such a strategy in real industrial systems is explored via a simulation study using data from a local e-commerce fulfilment warehouse.

Original languageEnglish
Pages (from-to)271-279
Number of pages9
JournalStudies in Computational Intelligence
Volume594
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Adaptive storing
  • Product intelligence
  • Warehouse management systems

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Adaptive storage location assignment for warehouses using intelligent products'. Together they form a unique fingerprint.

Cite this