Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1451
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dc.contributor.authorTsakalidis, George-
dc.contributor.authorGeorgoulakos, Kostas-
dc.contributor.authorPaganias, Dimitris-
dc.contributor.authorVergidis, Kostas-
dc.date.accessioned2022-09-29T07:30:30Z-
dc.date.available2022-09-29T07:30:30Z-
dc.date.issued2021-02-04-
dc.identifier10.3390/computation9020016en_US
dc.identifier.issn2079-3197en_US
dc.identifier.urihttps://doi.org/10.3390/computation9020016en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/1451-
dc.description.abstractBusiness 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.en_US
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.sourceComputationen_US
dc.subjectFRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineeringen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subjectFRASCATI::Natural sciences::Mathematics::Applied Mathematicsen_US
dc.subject.otherbusiness processen_US
dc.subject.otheroptimizationen_US
dc.subject.otherpreprocessingen_US
dc.subject.otherevolutionary algorithmsen_US
dc.titleAn Elaborate Preprocessing Phase (p3) in Composition and Optimization of Business Process Modelsen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume9en_US
local.identifier.issue2en_US
local.identifier.firstpage16en_US
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