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An entropy-based clustering ensemble method to support resource allocation in business process management

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Abstract

Resource allocation, as a crucial task of business process management, has been widely acknowledged by its importance for process performance improvement. Although some methods have been proposed to support resource allocation, there is little effort to allocate resources from the task preference perspective. This paper proposes a novel mechanism in which resource allocation is considered as a multi-criteria decision problem and solved by a new entropy-based clustering ensemble approach. By mining resource characteristics and task preference patterns from past process executions, the “right” resources could be recommended to improve resource utility. Further, to support dynamic resource allocation in the context of multiple process instances running concurrently, a heuristic method is devised to deal with resource conflicts caused by the interplay between various instances. The effectiveness of this study is evaluated with a real-life scenario, and the simulation results indicate that resource utility can be improved and resource workload can be balanced with the support of resource recommendation.

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Acknowledgments

This study was funded by Shanghai Pujiang Program (14PJC017) and the National Nature and Science Foundation of China under Grant No. 71071038.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Data Science, School of Software, Fudan University, No. 220 Handan Road, Shanghai, 200433, China

    Weidong Zhao & Haitao Liu

  2. School of Management, Fudan University, No. 220 Handan Road, Shanghai, 200433, China

    Weihui Dai

  3. Department of Information Systems, City University of Hong Kong, Kowloon, Hong Kong

    Jian Ma

Authors
  1. Weidong Zhao
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  2. Haitao Liu
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  3. Weihui Dai
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  4. Jian Ma
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Correspondence to Haitao Liu.

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Zhao, W., Liu, H., Dai, W. et al. An entropy-based clustering ensemble method to support resource allocation in business process management. Knowl Inf Syst 48, 305–330 (2016). https://doi.org/10.1007/s10115-015-0879-7

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  • DOI: https://doi.org/10.1007/s10115-015-0879-7

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