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Generating business process recommendations with a population-based meta-heuristic

Steven Mertens (UGent), Frederik Gailly (UGent) and Geert Poels (UGent)
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Abstract
In order to provide both guidance and flexibility to users during process execution, recommendation systems have been proposed. Existing recommendation systems mainly focus on offering recommendation according to the process optimization goals (time, cost…). In this paper we offer a new approach that primarily focuses on maximizing the flexibility during execution. This means that by following the recommendations, the user retains maximal flexibility to divert from them later on. This makes it possible to handle (possibly unknown) emerging constraints during execution. The main contribution of this paper is an algorithm that uses a declarative process model to generate a set of imperative process models that can be used to generate recommendations.
Keywords
Run-time flexibility, Declarative process model, Recommender systems, Business processes

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Chicago
Mertens, Steven, Frederik Gailly, and Geert Poels. 2015. “Generating Business Process Recommendations with a Population-based Meta-heuristic.” In Lecture Notes in Business Information Processing, ed. Fabiana Fournier and Jan Mendling, 202:516–528. Springer.
APA
Mertens, Steven, Gailly, F., & Poels, G. (2015). Generating business process recommendations with a population-based meta-heuristic. In F. Fournier & J. Mendling (Eds.), Lecture Notes in Business Information Processing (Vol. 202, pp. 516–528). Presented at the 12th International Conference on Business Process Management (BPM), Springer.
Vancouver
1.
Mertens S, Gailly F, Poels G. Generating business process recommendations with a population-based meta-heuristic. In: Fournier F, Mendling J, editors. Lecture Notes in Business Information Processing. Springer; 2015. p. 516–28.
MLA
Mertens, Steven, Frederik Gailly, and Geert Poels. “Generating Business Process Recommendations with a Population-based Meta-heuristic.” Lecture Notes in Business Information Processing. Ed. Fabiana Fournier & Jan Mendling. Vol. 202. Springer, 2015. 516–528. Print.
@inproceedings{5930927,
  abstract     = {In order to provide both guidance and flexibility to users during process execution, recommendation systems have been proposed. Existing recommendation systems mainly focus on offering recommendation according to the process optimization goals (time, cost{\textellipsis}). In this paper we offer a new approach that primarily focuses on maximizing the flexibility during execution. This means that by following the recommendations, the user retains maximal flexibility to divert from them later on. This makes it possible to handle (possibly unknown) emerging constraints during execution. The main contribution of this paper is an algorithm that uses a declarative process model to generate a set of imperative process models that can be used to generate recommendations.},
  author       = {Mertens, Steven and Gailly, Frederik and Poels, Geert},
  booktitle    = {Lecture Notes in Business Information Processing},
  editor       = {Fournier, Fabiana and Mendling, Jan},
  isbn         = {9783319158952},
  issn         = {1865-1348},
  keyword      = {Run-time flexibility,Declarative process model,Recommender systems,Business processes},
  language     = {eng},
  location     = {Haifa, ISRAEL},
  pages        = {516--528},
  publisher    = {Springer},
  title        = {Generating business process recommendations with a population-based meta-heuristic},
  url          = {http://dx.doi.org/10.1007/978-3-319-15895-2\_44},
  volume       = {202},
  year         = {2015},
}

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