In recent years, the provisioning of goods from warehouses has been subject to great pressure and high expectations regarding rapid order fulfillment. One major influence is the design of the storage location search for individual goods with regard to their provisioning time. The aim of this thesis is to investigate a possible improvement of the storage location search by using the metaheuristic "Covariance Matrix Adaption Evolution Strategy" (CMAES). It is used to optimize the weights of the objective function criteria for the storage location search by means of minimizing the total throughput time. The results show, that this use of CMAES achieves lower total throughput times for a specific set of orders. However, the extent of the improvement is dependent upon the warehouse data for a fixed scenario. The duration of the simulation-based evaluation of the objective function proved to be the significantly limiting factor. The potential for improvement using an optimization method on weights of the objective function criteria of a storage location search was empirically shown by a selection of different scenarios.
|Translated title of the contribution||Logistics Software Parameter Optimization: Application of an Evolutionary Method using the example of a Storage Location Assignment Problem|
|Award date||1 Jul 2022|
|Publication status||Published - 2022|
Bibliographical noteembargoed until 01-06-2027
- Covariance Matrix Adaption Evolution Strategy
- Storage Location Assignment Problem
- Storage Location Search