Monika Kofler,
"Optimising the Storage Location Assignment Problem Under Dynamic Conditions"
, Johannes Kepler Universität, Linz, 5-2015
Original Titel:
Optimising the Storage Location Assignment Problem Under Dynamic Conditions
Sprache des Titels:
Englisch
Original Kurzfassung:
The assignment of products to storage locations has a major impact
on the performance of a warehouse, especially if the warehouse is
not automated, but serviced by human pickers. Although the static
storage location assignment problem has been studied for more than
fifty years, the interrelations with up- and downstream processes and
the effects of dynamic fluctuations in demand are still not well under-
stood. In this thesis, we model and optimise dynamic and integrated
storage assignment problems based on real-world data from the auto-
motive and steel industry. Order picking is the main bottleneck in
both scenarios, therefore the quality of a warehouse assignment is
evaluated via picker travel distance required to supply products to
downstream processes.
Affinity based slotting strategies place products that are frequently
ordered together closer to each other. Part i of this thesis focuses
on the formalisation of the novel Pick Frequency / Part Affinity score,
which combines popularity and affinity measures. The new score
is coupled with various metaheuristics and compared to standard
assignment strategies in two empirical case studies.
The downside of the algorithms studied in the first part of this
work is that implementing the generated assignments in a fully oper-
ational warehouse requires extensive movements of products. Part ii
therefore focuses on the development of a generic multi-period model
of the storage location assignment problem. By considering storage,
re-location, and picking efforts, the costs and benefits of extensive re-
locations versus iteratively moving a small number of products per
period are analysed. Greedily selecting re-locations has a couple of
disadvantages, which were mitigated by switching to a "robust" selec-
tion strategy.
Forschungs-