Section: New Results
Scheduling and Resource Allocation in Business Processes
Participants : Khalid Benali, Abir Ismaili-Alaoui.
Business Process Management (BPM) is concerned with continuously enhancing business processes by adapting a systematic approach that enables companies to increase the performance of their existing business processes and achieve their business goals. Business processes are generaly considered as blind and stateless, which mean that in each business process execution results from past process instances are not taken into consideration.
The main objective of our current research is to exploit the data generated from previous instances in order to enhance business processes in regards with several aspects, such as improvement of process business logical correctness, optimization of business process modeling issues, or improvemment of resource allocation and scheduling procedure in order to particularly optimize costs and time (among other factors).
We focus currently on this last aspect, i.e. scheduling and resource allocation in business processes. Business Processes may contain automatic tasks and non automatic tasks, so managing resources depends on the type of those resources (human or machine) In this context, our work use machine learning techniques to analyze data generated from previous business process execution to improve business process scheduling. This step ensure the assignment of the most critical business process instance task to a qualified (and may be costly) human resource while minimizing global execution costs through assignement of “dummy” tasks to machine agents.