Context: The quality of business process models (i.e., software artifacts that capture the relationsbetween the organizational units of a business) is essential for enhancing the management of businessprocesses. However, such modeling is typically carried out manually. This is already challenging and timeconsuming when (1) input uncertainty exists, (2) activities are related, and (3) resource allocation has tobe considered. When including optimization requirements regarding flexibility and robustness itbecomes even more complicated potentially resulting into non-optimized models, errors, and lack offlexibility.Objective: To facilitate the human work and to improve the resulting models in scenarios subject touncertainty, we propose a software-supported approach for automatically creating configurable businessprocess models from declarative specifications considering all the aforementioned requirements.Method: First, the scenario is modeled through a declarative language which allows the analysts to specifyits variability and uncertainty. Thereafter, a set of optimized enactment plans (each one representing apotential 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 iscreated from these plans.Results: To validate the proposed approach, we conduct a case study based on a real business which issubject to uncertainty. Results indicate that our approach improves the actual performance of the businessand 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 declarativespecification. Unlike existing approaches, our approach considers input uncertainty, the optimization ofmultiple objective functions, as well as the resource and the control-flow perspectives. However, ourapproach also presents a few limitations: (1) it is focused on the control-flow and the data perspectiveis only partially addressed and (2) model attributes need to be estimated.
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