Context: The quality of business process models (i.e., software artifacts that capture the relations
between the organizational units of a business) is essential for enhancing the management of business
processes. However, such modeling is typically carried out manually. This is already challenging and time
consuming when (1) input uncertainty exists, (2) activities are related, and (3) resource allocation has to
be considered. When including optimization requirements regarding flexibility and robustness it
becomes even more complicated potentially resulting into non-optimized models, errors, and lack of
flexibility.
Objective: To facilitate the human work and to improve the resulting models in scenarios subject to
uncertainty, we propose a software-supported approach for automatically creating configurable business
process models from declarative specifications considering all the aforementioned requirements.
Method: First, the scenario is modeled through a declarative language which allows the analysts to specify
its variability and uncertainty. Thereafter, a set of optimized enactment plans (each one representing a
potential execution alternative) are generated from such a model considering the input uncertainty.
Finally, to deal with this uncertainty during run-time, a flexible configurable business process model is
created from these plans.
Results: To validate the proposed approach, we conduct a case study based on a real business which is
subject to uncertainty. Results indicate that our approach improves the actual performance of the business
and that the generated models support most of the uncertainty inherent to the business.
Conclusions: The proposed approach automatically selects the best part of the variability of a declarative
specification. Unlike existing approaches, our approach considers input uncertainty, the optimization of
multiple objective functions, as well as the resource and the control-flow perspectives. However, our
approach also presents a few limitations: (1) it is focused on the control-flow and the data perspective
is only partially addressed and (2) model attributes need to be estimated.Ministerio de Ciencia e Innovación TIN2009-1371
