Abstract
In enterprise, optimization is seen as making business decisions by varying some parameters to maximize profit and reduce loss. We focus on business processes design optimization. It is known as the problem of creating feasible business processes while optimizing their criteria such as resource cost and execution time. In this paper, we propose an approach that focuses on tasks composing a business process, their resources and attributes rather than a full representation of a business process for its evaluation according to certain criteria. 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 using an effective algorithm. At last, (iii) selecting Pareto optimal solutions in a multi criteria optimization environment up to three criteria, using an effectual fitness function. The experimental results showed that our proposal generates efficient business processes in terms of qualitative parameters compared with existing solutions.
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Notes
- 1.
Non-dominated Sorting Genetic Algorithm.
- 2.
Hajela's and Link Genetic Algorithm.
- 3.
Non-dominated Sorting Genetic Algorithm II.
- 4.
Strength Pareto Evolutionary Algorithm 2.
- 5.
Pareto Envelope-based Selection Algorithm 2.
- 6.
Pareto Archived Evolution Strategy.
- 7.
Multi-objective Particle Swarm Optimization Algorithm.
- 8.
Ant Colony Optimization.
- 9.
Bee Colony Optimization.
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Mahammed, N., Benslimane, S.M. (2016). Toward Multi Criteria Optimization of Business Processes Design. In: Bellatreche, L., Pastor, Ó., Almendros Jiménez, J., Aït-Ameur, Y. (eds) Model and Data Engineering. MEDI 2016. Lecture Notes in Computer Science(), vol 9893. Springer, Cham. https://doi.org/10.1007/978-3-319-45547-1_8
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