Bayesian Selection Rule for Human-Resource Selection in Business Process Management Systems

Amna Shifia Nisafani, Arif Wibisono, Seung Kim, Hyerim Bae


This study developed a method for selection of available human resources for incomingjob
allocation that considers factors affecting resource performance in the business process
management (BPM) environment. For many years, resource selection has been treated as
a very important issue in scheduling due to its direct influence on the speed and quality
of task accomplishment. Even though traditional resource selection can work well in many
situations, it might not be the best choice when dealing with human resources. Humanresource
performance is easily affected by several factors such as workload, queue,
working hours, inter-arrival time, and others. The resource-selection rule developed in the
present study considers factors that affect human resource performance. We used a
Bayesian Network (BN) to incorporate those factors into a single model, which we have
called the Bayesian Selection Rule (BSR). Our simulation results show that the BSR can
reduce waiting time, completion time and cycle time.

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Peter, A. Hancock and Paula A. Desmond, Stress, workload, and fatigue : Lawrance Elbaum Associates, 2001.

Adnan Darwiche, Modeling and Reasoning with Bayesian Networks. Los Angeles, New York : Cambridge University Press, 2009.

John, S. Uebersax, Genetic Risk Modeling : An Application of Bayes Nets. [Online] bayes_net_breast_cancer.doc, 2004.

Luis, M. de Campos, Juan, M. Fernandez-Luna, and Juan F Huete, “Bayesian networks and information retrieval : an introduction to the special issue,” Journal of Information Processing and Management, Vol. 40, No. 5, 2004.

Francisco, Diez, Jose Mira, E. Iturralde, and S. Zubillaga, “DIAVAL, a bayesian expert system for echocardiography,” Artificial Intelligence in Medicine 10, Vol. 1, pp. 59-73, 1997.

Han-ying Kao, Chia-hui Huang, and Han-lin Li, “Supply Chain Diagnostics with Dynamic Bayesian Networks,” Computer and Industrial Engineering, Vol. 49, No. 2, pp. 339-347, 2005.

Gregory, F. Cooper and Cooper Edward Herskovits, “A bayesian method for the induction of probabilistics network from data,” Machine Learning, Vol. 9, No. 4, pp. 309-347, 1992.

Alan, B. Pritsker and Jean J. O’Reilly, Simulation with Visual SLAM and AweSim. West Lafayette, Indiana USA : System Publishing Corporation, 1999.

Zhengxing, Huang, W. M. P. van der Aalst, and Xudong Lu, “Reinforcement learning based resource allocation in business process management,” Data and Knowledge Engineering, Vol. 70, No. 1, pp. 127-145, January 2011.

Caroline, B., Hines, “Time-of-Day Effects on Human Performance,” Catholic Education, Vol. 7, 2004.

Joanne White MSc and Johanna Beswick, “Working Long Hours,” The Health and Safety Laboratory (HSL), UK, HSL/2003 /02, 2003.

Kevin, R., Murphy, Human Performance, Vol. 2, No. 3, 1989.

Jong-Min Woo and Teodor T Postolache, “The impact of work environment on mood disorders and suicide : Evidence and implications,” Int J Disabil Hum Dev, Vol. 7, No. 2, pp. 185-200, 2008.

Bierwirth, C. and Mattfeld, D. C., “Minimizing job. Tardiness : Priority rules vs. adaptative scheduling,” in Adaptative Computing in Design and Manufacture. London : Springer-Verlag, 1998.

Hyerim Bae, Wonchang Hu, Woo Sik Yoo, and Byeong Kwon Kwak, “Document configuration control process captured in a workflow,” Computer in Industry, Vol. 53, No. 2, pp. 117-131, 2004.

Wikipedia, [Online], http://id.wikipedia. org/wiki/Surat_Izin_Mengemudi, 2011.

Nick Russell, W. M. P van der Aalst, H. M. ter Hofstede Arthur, and Edmond David, “Workflow Resource Patterns,” Eindhoven University of Technology, Eindhoven, BETA Working Paper Series 2004.


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