Prediction-based Resource Allocation using Bayesian Neural Networks and
  Minimum Cost and Maximum Flow Algorithm
  Predictive business process monitoring aims at providing predictions about
running instances by analyzing logs of completed cases in a business process.
Recently, a lot of research focuses on increasing productivity and efficiency
in a business process by forecasting potential problems during its executions.
However, most of the studies lack suggesting concrete actions to improve the
process. They leave it up to the subjective judgment of a user. In this paper,
we propose a novel method to connect the results from predictive business
process monitoring to actual business process improvements. More in detail, we
optimize the resource allocation in a non-clairvoyant online environment, where
we have limited information required for scheduling, by exploiting the
predictions. The proposed method integrates the offline prediction model
construction that predicts the processing time and the next activity of an
ongoing instance using Bayesian Neural Networks (BNNs) with the online resource
allocation that is extended from the minimum cost and maximum flow algorithm.
To validate the proposed method, we performed experiments using an artificial
event log and a real-life event log from a global financial organization.
