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.
Optimization is known as the process of finding the best possible solution to a problem given a set of constraints. The problem becomes challenging when dealing with conflicting objectives, which leads to a multiplicity of solutions. Evolutionary algorithms, which use a population approach in their search procedures, are advised to suitably solve the problem. In this article, we present an approach for an evolutionary combinatorial multi-objective optimization of business process designs using a variation of NSGAII, baptized MA-NSGAII. The variants of NSGAII are numerous. In fact, the vast majority deals either with the crossover operator or with the crowding distance. We discuss an optimization Framework that uses (i) a proposal of effective Fitness function, (ii) 02 contradictory criteria to optimize and (iii) an original selection technique. We test the proposed Framework with a real life case of multi-objective optimization of business process designs. The obtained results clearly indicate that an effectual Fitness function combined with the appropriate selection operator affects undeniably quality and quantity of solutions.
The addressed issue in the present work revolves around the area of business process management in general and in particular optimization. The problem involves the generation of optimized business process designs from a business process model in a multi-criteria optimization environment by appealing an evolutionary algorithm. Thus, the main contribution is to analyze the characteristics of using a multi-criteria decision-analysis method within a genetic algorithm in an issue of business process optimization. The experimental results clearly demonstrated that using a multi-criteria decision-analysis method helps considerably the production of qualitatively interesting alternative solutions in a reasonable period time regard-ing the problem complexity, which ultimately assists the decision maker to perform improved decision making
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