Task Assignment in Business Processes Based on Completion Rate Evaluation

In order to ensure the completion of cases and tasks on time in business processes, an approach for task assignment based on completion rate evaluation of resources is proposed. The approach is based on the data analysis of the event logs, in which the related historical temporal information is extracted and analyzed to evaluate the completion rate of the resources for tasks. The capabilities of resources are classifying into different tasks, and the completion rate is evaluated according to the historical completion, proportions of working time and the status of the resources. The results of the evaluation are applied to task assignment including two ways of offering and allocating in business processes to ensure the completion of cases on time. Experiments show that the on time completion rate of cases is increased by the applied of the approach on task assignment.

[1]  P. Bühlmann,et al.  The group lasso for logistic regression , 2008 .

[2]  Alessandro Sperduti,et al.  Time and activity sequence prediction of business process instances , 2016, Computing.

[3]  Manfred Reichert,et al.  Mining business process variants: Challenges, scenarios, algorithms , 2011, Data Knowl. Eng..

[4]  Chris Hans Bayesian lasso regression , 2009 .

[5]  Wil M. P. van der Aalst,et al.  Time prediction based on process mining , 2011, Inf. Syst..

[6]  Jin-Soo Kim,et al.  BTS: Resource capacity estimate for time-targeted science workflows , 2011, J. Parallel Distributed Comput..

[7]  Wil M. P. van der Aalst,et al.  A recommendation system for predicting risks across multiple business process instances , 2015, Decis. Support Syst..

[8]  Michelangelo Ceci,et al.  Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining , 2014, Discovery Science.

[9]  Wil M. P. van der Aalst,et al.  A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs , 2016, Inf. Syst..

[10]  Yu Zhou,et al.  Dynamically Predicting the Deadlines in Time-Constrained Workflows , 2013, WISE Workshops.

[11]  Moe Thandar Wynn,et al.  Profiling Event Logs to Configure Risk Indicators for Process Delays , 2013, CAiSE.

[12]  Adam Belloum,et al.  Execution Time Estimation for Workflow Scheduling , 2014, 2014 9th Workshop on Workflows in Support of Large-Scale Science.

[13]  Moe Thandar Wynn,et al.  Predicting Deadline Transgressions Using Event Logs , 2012, Business Process Management Workshops.

[14]  Marlon Dumas,et al.  Predictive Business Process Monitoring with LSTM Neural Networks , 2016, CAiSE.

[15]  Henry C. W. Lau,et al.  A business process activity model and performance measurement using a time series ARIMA intervention analysis , 2009, Expert Syst. Appl..

[16]  Jana-Rebecca Rehse,et al.  A Deep Learning Approach for Predicting Process Behaviour at Runtime , 2016, Business Process Management Workshops.

[17]  Miguel Toro,et al.  Run-time prediction of business process indicators using evolutionary decision rules , 2017, Expert Syst. Appl..

[18]  Mathias Weske,et al.  Prediction of business process durations using non-Markovian stochastic Petri nets , 2015, Inf. Syst..

[19]  Bokyoung Kang,et al.  Real-time business process monitoring method for prediction of abnormal termination using KNNI-based LOF prediction , 2012, Expert Syst. Appl..