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Optimatch: applying constraint programming to workforce management of highly skilled employees

Optimatch: applying constraint programming to workforce management of highly skilled employees Today many companies face the challenge of matching highly skilled professionals to high-end positions in large organisations and human deployment agencies. Unlike traditional workforce management problems, such as shift scheduling, highly skilled employees are professionally distinguishable from each other and hence non-interchangeable. Our work specifically focuses on the services industry, where much of the revenue comes from the assignment of highly professional workers. Here, non-accurate matches may result in significant monetary losses and other negative effects. We deal with very large pools of both positions and employees, where optimal decisions should be made rapidly in a dynamic environment. Since traditional operations research methods fail to answer this problem, we employ Constraint Programming (CP), a subfield of Artificial Intelligence with strong algorithmic foundations. Our CP model builds on new constraint propagators designed for this problem (but applicable elsewhere), as well as on information retrieval methods used for analysing the complex text describing high-end professionals and positions. Optimatch, which is based on this technology and is being used by IBM services organisations, provides strong experimental results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Services Operations and Informatics Inderscience Publishers

Optimatch: applying constraint programming to workforce management of highly skilled employees

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1741-539X
eISSN
1741-5403
DOI
10.1504/IJSOI.2008.021338
Publisher site
See Article on Publisher Site

Abstract

Today many companies face the challenge of matching highly skilled professionals to high-end positions in large organisations and human deployment agencies. Unlike traditional workforce management problems, such as shift scheduling, highly skilled employees are professionally distinguishable from each other and hence non-interchangeable. Our work specifically focuses on the services industry, where much of the revenue comes from the assignment of highly professional workers. Here, non-accurate matches may result in significant monetary losses and other negative effects. We deal with very large pools of both positions and employees, where optimal decisions should be made rapidly in a dynamic environment. Since traditional operations research methods fail to answer this problem, we employ Constraint Programming (CP), a subfield of Artificial Intelligence with strong algorithmic foundations. Our CP model builds on new constraint propagators designed for this problem (but applicable elsewhere), as well as on information retrieval methods used for analysing the complex text describing high-end professionals and positions. Optimatch, which is based on this technology and is being used by IBM services organisations, provides strong experimental results.

Journal

International Journal of Services Operations and InformaticsInderscience Publishers

Published: Jan 1, 2008

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