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An Evolutionary Algorithm Based Approach for Business Process Multi-Criteria Optimization

An Evolutionary Algorithm Based Approach for Business Process Multi-Criteria Optimization

Nadir Mahammed, Sidi Mohamed Benslimane
Copyright: © 2017 |Volume: 7 |Issue: 2 |Pages: 20
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781522513261|DOI: 10.4018/IJOCI.2017040103
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MLA

Mahammed, Nadir, and Sidi Mohamed Benslimane. "An Evolutionary Algorithm Based Approach for Business Process Multi-Criteria Optimization." IJOCI vol.7, no.2 2017: pp.34-53. http://doi.org/10.4018/IJOCI.2017040103

APA

Mahammed, N. & Benslimane, S. M. (2017). An Evolutionary Algorithm Based Approach for Business Process Multi-Criteria Optimization. International Journal of Organizational and Collective Intelligence (IJOCI), 7(2), 34-53. http://doi.org/10.4018/IJOCI.2017040103

Chicago

Mahammed, Nadir, and Sidi Mohamed Benslimane. "An Evolutionary Algorithm Based Approach for Business Process Multi-Criteria Optimization," International Journal of Organizational and Collective Intelligence (IJOCI) 7, no.2: 34-53. http://doi.org/10.4018/IJOCI.2017040103

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Abstract

Business processes design optimization is known as the problem of creating feasible business processes while optimizing their criteria such as resource cost and execution time. In this paper, the authors propose an evolutionary multi-criteria approach based on a modified evolutionary algorithm for generating optimized business processes. The main contribution of this work is a framework capable of (i) generating business processes using an enhanced version of evolutionary algorithm NSGAII, (ii) verifying the feasibility of each business process created employing an effective algorithm, and (iii) selecting Pareto optimal solutions in a multi criteria optimization environment up to three criteria, with use of an effectual fitness function. The experimental results showed that the authors' proposal generates efficient business processes with high quality in terms of qualitative parameters compared with existing solutions.

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