Toward Multi Criteria Optimization of Business Processes Design

Mahammed, Nadir; Benslimane, Sidi Mohamed

doi:10.1007/978-3-319-45547-1_8

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…They replace the binary tournament selection with uniform selection, roulette wheel selection and ranking selection, combining the whole with a proposed crossover operator. The proposed framework improves the results obtained by [6]. In the present work, the authors present an approach for a BPMOO using NSGAII with an unusual selection operator, Mass selection.…”

Section: Related Workmentioning

confidence: 69%

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Evolutionary Multi-objective Optimization of Business Process Designs with MA-NSGAII

Mahammed¹,

Benslimane²,

Hamdani

2018

Computational Intelligence and Its Applications

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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.

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Section: Related Workmentioning

confidence: 69%

“…Farsani et al [14] modifies the mutation and crossover operators used by NSGAII. Mahammed et al [6] are interested in a BPMOO (up to 03 criteria). They proposed a Framework that combines an original Fitness function with NSGAII with a modified crossover operator.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Multi-objective Optimization of Business Process Designs with MA-NSGAII

Mahammed¹,

Benslimane²,

Hamdani

2018

Computational Intelligence and Its Applications

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“…The bpo F framework is well established and has been utilized both by the authors [5] and [6,35] and other researchers [41,42] with significant findings both for business process optimization and the performance of the EMOAs. The initial addition of preprocessing presented in [6] increased the capability of the framework in generating diverse designs and selecting those with optimal objective values for business processes in reduced time.…”

Section: Resultsmentioning

confidence: 99%

An Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Models

Tsakalidis¹,

Georgoulakos²,

Paganias³

et al. 2021

Computation

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Business process optimization (BPO) has become an increasingly attractive subject in the wider area of business process intelligence and is considered as the problem of composing feasible business process designs with optimal attribute values, such as execution time and cost. Despite the fact that many approaches have produced promising results regarding the enhancement of attribute performance, little has been done to reduce the computational complexity due to the size of the problem. The proposed approach introduces an elaborate preprocessing phase as a component to an established optimization framework (bpoF) that applies evolutionary multi-objective optimization algorithms (EMOAs) to generate a series of diverse optimized business process designs based on specific process requirements. The preprocessing phase follows a systematic rule-based algorithmic procedure for reducing the library size of candidate tasks. The experimental results on synthetic data demonstrate a considerable reduction of the library size and a positive influence on the performance of EMOAs, which is expressed with the generation of an increasing number of nondominated solutions. An important feature of the proposed phase is that the preprocessing effects are explicitly measured before the EMOAs application; thus, the effects on the library reduction size are directly correlated with the improved performance of the EMOAs in terms of average time of execution and nondominated solution generation. The work presented in this paper intends to pave the way for addressing the abiding optimization challenges related to the computational complexity of the search space of the optimization problem by working on the problem specification at an earlier stage.

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