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Title: An Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Models
Authors: Tsakalidis, George
Georgoulakos, Kostas
Paganias, Dimitris
Vergidis, Kostas
Type: Article
Subjects: FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Natural sciences::Mathematics::Applied Mathematics
Keywords: business process
evolutionary algorithms
Issue Date: 4-Feb-2021
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
Source: Computation
Volume: 9
Issue: 2
First Page: 16
Abstract: 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.
ISSN: 2079-3197
Other Identifiers: 10.3390/computation9020016
Appears in Collections:Department of Applied Informatics

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