A dynamic and human-centric resource allocation for managing business process execution

Generally, resource allocation is essential to the efficient operational execution. More specifically, resource allocation for semi-automatic business processes might be more sophisticated due to human involvement. To this point, human performances are oscillating over time. Hence, upfront and static resource allocation might be suboptimal to deal with human dynamics. For this reason, this research suggests a dynamic and human-centric resource allocation to organize human-type resources in semi-automatic business process. Here, we use Bayesian approaches to predict resource's performances according to historical data set. As a result, we can construct a dynamic priority rule to assign a job to a specific resource with the highest probability to work faster. Finally, we demonstrate that our approach outperforms other priority rules: Random, Lowest Idle, Highest Idle, Order, and previously developed Bayesian Selection Rule from the total completion time and waiting time point of view.