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Towards an Automated Optimization-as-a-Service Concept

Many organizations try to apply analytics in order to improve their business processes. More and more cloud services are offered to support these efforts. However, the support of prescriptive analytics is weak. While concepts for such an optimization-as-a-service exist, these require much expert knowledge in solution methods. In this paper, a workflow for optimization-as-a-service is proposed that utilizes an optimization knowledge base in which machine learning techniques are applied to automatically select and parametrize suitable solution algorithms. This would allow consumers to use the service without expert knowledge while reducing operational costs for providers.