Task allocation for maximum cooperation in complex structured business processes

Abstract

The execution of a business process usually involves the cooperation of many resources (actors) performing various tasks (activities). Generally speaking, the cooperation among actors could significantly influence the efficiency of process execution. The better cooperation among actors, the higher performance the process instance would achieve. The task allocation is, therefore, expected to ensure that the actors are compatible well with each other when performing tasks. However, the decision of task allocation is always ad-hoc or performed manually in many organizations. As for some automatic methods, they usually serve only for sequentially structured simple processes. In addition, the existing studies only concentrate on the cooperation between two actors and fail to consider the effects from the context of performing different tasks by these two actors especially in the business process with many-to-many relationships among the resources and activities. To address these issues, we handle the process with complex block structures including iteration, choice and parallelism, and propose an effective entity-wise method to measure the cooperation by combining both actors and activities together. Based on them, we present two approaches, one greedy-based and the other A*-based, to assign actors for maximum cooperation in the complex structured business process with many-to-many relationships among the resources and activities. The experimental results on both public released and synthetic datasets demonstrate the effectiveness and efficiency of our approaches.