A semi-automatic approach for workflow staff assignment

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/30171

Title: A semi-automatic approach for workflow staff assignment
Author(s): Liu, Yingbo; Wang, Jianmin; Yang, Yun; Sun, Jiaguang
Abstract: Staff assignment is of great importance for workflow management systems. In many workflow applications, staff assignment is still performed manually. In this paper, we present a semi-automatic approach intended to reduce the number of manual staff assignment. Our approach applies a machine learning algorithm to the workflow event log to learn various kinds of activities that each actor undertakes. When staff assignment is needed, the classifiers generated by the machine learning technique suggest a suitable actor to undertake the specified activities. With experiments on three enterprises, our approach achieved a fairly accurate recommendation.
Publication type: Journal article
Research centre: Swinburne University of Technology. Faculty of Information and Communication Technologies. Centre for Information Technology Research
Source: Computers in Industry, Vol. 59, no. 5 (May 2008), p. 463-476
Publication year: 2008
Keyword(s): Machine learning; Resource management; Staff assignment; Workflow
Publisher: Elsevier
ISSN: 0166-3615
Publisher URL: http://dx.doi.org/10.1016/j.compind.2007.12.002
Peer reviewed