Association rule based approach for improving operation efficiency in a randomized warehouse

Data mining has long been used in relationship extraction from large amount of data for a wide range of applications such as consumer behavior analysis in marketing. Some research studies have also extended the usage of this concept in warehousing operations management to determine the order picking policy by batching the orders to minimize the picking distance. Yet, not many research studies have considered the application of the data mining approach on storage location assignment decision to minimize the manual effort on put-away execution which is also a significant factor to the constituent of warehousing operation cost. We present a data mining approach for the storage location assignment problem in a randomized warehouse using association rules extraction algorithm. Result of the preliminary experimental study shows that our proposed storage location assignment algorithm is efficient in determining the correlated products storage location that minimizes the total travel distances of both order picking and put-away operations for a randomized less-than-unit-load warehouse.