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This paper focuses on grid performance optimization in large scale workflow applications with an intelligent workflow scheduling mechanism. Utility Management Systems (UMS) are managing very large numbers of workflows with very high resource requirements. This paper proposes a UMS scheduling architecture which dynamically executes a scheduling algorithm using near real-time feedback about the current status of grid nodes. Workflow scheduling was performed with an artificial neural network (ANN). The network was trained in a system with three workflows. The case study presented in this paper shows results achieved in a three workflow system, as well as results achieved in a five workflow system where an adaptive ANN was used. The results testify that significant improvement of overall execution time can be achieved by adapting weights in the neural network.