Enhancing Association Rule Mining for Solving the Storage Location Assignment Problem
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2022
Författare
Bohlin, Jonas
Gabrielii, Tobias
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In the era of online retailing, reducing the picking time of orders is of great importance.
One way of driving the picking time down is to optimise the locations of
articles within the warehouse, a problem which is referred to as the Storage Location
Assignment Problem (SLAP). The SLAP is an NP-hard problem, and it is therefore
desirable to find a relaxation of the problem. In this thesis a rule based approach to
the problem is proposed, focusing on association rule mining and rules created from
a neural network utilising distance metric learning. These rules are then used by a
greedy and a genetic algorithm, to optimise the article placements. The data used
to find rules and evaluate the algorithms come from an online retailer of electronic
spare parts. When evaluating the genetic algorithm on this dataset, it performs
worse than the baseline of storing the most frequently purchased articles closest to
the picking depot. However, the greedy algorithm outperforms this baseline by up
to 11%, showing that there is a lot of promise for this rule based approach.
Beskrivning
Ämne/nyckelord
Storage Location Assignment Problem, Association Rule Mining, Distance Metric Learning, Genetic Algorithm, Warehouse Management.