In this thesis, we model and optimise dynamic and integrated storage assignment problems based on real-world data from the automotive 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. Part ii 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" selection strategy. In Part iii, we model an integrated warehouse assignment, order scheduling, and in-house transport problem and solve it sequentially via metaheuristics and simulation. Finally, we model a slab yard assignment problem, which is unexpectedly difficult to solve to optimality. By incorporating strategies that explore neutral plateaus into the metaheuristics, we were able to find the global optimum for the benchmark. The developed algorithms have been successfully deployed in a real-world production warehouse.