Solving a Business Process Optimization Issue With a Genetic Algorithm Coupled With Multi-Criteria Decision Analysis Method

Mahammed, Nadir; Benslimane, Sidi Mohamed

doi:10.4018/ijoci.2021010105

Cited by 2 publications

(1 citation statement)

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“…The experimental results are promising in the sense that the proposed framework is capable of producing an acceptable number of optimized design alternatives regarding the problem complexity and in a reasonable period. The benefits of using a multicriteria decision analysis method within a genetic algorithm for BPO are also evident in [33]. The experimental results clearly demonstrated that using a multicriteria decision-analysis method provably facilitates the production of qualitatively interesting alternative solutions in a short period time, a fact that ultimately assists in the decision-making procedure.…”

Section: Related Workmentioning

confidence: 76%

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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Section: Related Workmentioning

confidence: 76%