An Optimal Approach for Workflow Staff Assignment Based on Hidden Markov Models

摘要

Staff assignment of workflow is often performed manually and empirically. In this paper we propose an optimal approach named SAHMM (Staff Assignment based on Hidden Markov Models) to allocate the most proficient set of employees for a whole business process based on workflow event logs. The Hidden Markov Model (HMM) is used to describe the complicated relationships among employees which are ignored by previous approaches. The validity of the approach is confirmed by experiments on real data.