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Generating optimized configurable business process models in scenarios subject to uncertainty


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dc.creator Jiménez Ramírez, Andrés es
dc.creator Weber, Barbara es
dc.creator Barba Rodríguez, Irene es
dc.creator Valle Sevillano, Carmelo del es 2017-09-08T10:55:32Z 2017-09-08T10:55:32Z 2015
dc.identifier.citation Jiménez Ramírez, A., Weber, B., Barba Rodríguez, I. y Valle Sevillano, C.d. (2015). Generating optimized configurable business process models in scenarios subject to uncertainty. Information and Software Technology, 57 (january 2015), 571-594.
dc.identifier.issn 0950-5849 es
dc.description.abstract 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. es
dc.description.sponsorship Ministerio de Ciencia e Innovación TIN2009-13714 es
dc.format application/pdf es
dc.language.iso eng es
dc.publisher Elsevier es
dc.relation.ispartof Information and Software Technology, 57 (january 2015), 571-594.
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri *
dc.subject Configurable Business Process Models es
dc.subject Constraint-based business process models es
dc.subject Flexibility es
dc.subject Robustness es
dc.subject Planning and Scheduling es
dc.subject Constraint programming es
dc.title Generating optimized configurable business process models in scenarios subject to uncertainty es
dc.type info:eu-repo/semantics/article es
dc.type.version info:eu-repo/semantics/submittedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.relation.projectID TIN2009-13714 es
dc.relation.publisherversion es
dc.identifier.doi 10.1016/j.infsof.2014.06.006 es
idus.format.extent 24 es
dc.journaltitle Information and Software Technology es
dc.publication.volumen 57 es
dc.publication.issue january 2015 es
dc.publication.initialPage 571 es
dc.publication.endPage 594 es
dc.identifier.sisius 20766029 es
dc.contributor.funder Ministerio de Ciencia e Innovación (MICIN). España
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