Automated Resource Optimization in Business Processes
Business process optimization is a time-consuming activity. Mainly because the amount of possible modifications is very large and a business analyst usually approaches the task with a trial-and-error method. As a result the analyst is not guaranteed to find the best possible solution. Currently some automated process optimizers do exist but their functionality is limited and the processing time is long. In this thesis I implemented an automated resource allocation optimizer based on hill-climbing. The optimizer can optimize on execution time, cost or combination of thereof when given certain resource constraints, expressed in terms of minimum and maximum size for each resource pool. The overall results were positive and on a basis of a case study it was proven that a hill-climbing algorithm can be applied to a resource allocation problem. In a reasonable time frame the optimizer was capable of finding the best cycle time and cost trade-off curve in two-dimensional space.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science
Prof. Marlon Dumas