Application Of COI Based Policy To Solve: The Storage Location Problem

Abstract

Improved storage location assignment of newly delivered products is one effective method for improving the order picking operation performance, especially the warehouse is mostly serviced by human pickers. This study aims to identify a potential solution by developing a distributed, adaptive strategy Cube-per-Oder index based method (COI) for the storage location assignment problem and follows the demand correlation pattern paradigm for its implementation. The proposed model is specially formulated to distribute SKUs that are prevalently ordered together in positions close to one another, add in the criteria that frequently ordered ones near the I/O (input/output) point. Consequently, the experiment results reveal that the items re-assignment is totally helpful to reduce the travel distance of order picker in distribution center when comparing to the existing random storage practice. The efficiency of such a strategy in real industrial systems is explored via a computational study using the empirical data from a real-case warehouse, particularly YCH Protrade.