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Title: Towards a Comprehensive Business Process Optimization Framework
Authors: Tsakalidis, George
Vergidis, Kostas
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Social sciences::Economics and Business::Business and Management
Keywords: business process
combinatorial optimization;
business process intelligence
web services
evolutionary algorithms
Issue Date: 2017
First Page: 129
Last Page: 134
Volume Title: 2017 IEEE 19th Conference on Business Informatics (CBI)
Abstract: Business processes are a collection of related, structured activities performed to achieve a defined business outcome. Adopting a business process perspective is an essential advantage for organizations to orchestrate and achieve continuous improvements on time and within specified resource constraints. The increased popularity of this domain, however, has resulted in a variety of interdisciplinary approaches with limited tangible, quantifiable -and thus measurable benefits. Operational Research (OR) has critically evolved during the last decades, providing businesses and organizations with problem-solving techniques and methods aiming to enhanced performance and improved efficiency. The proposed project focuses on the development, evaluation and verification of a business process optimisation framework as the central objective of the PhD Thesis. The performed optimisation is intended to use Evolutionary Computing (EC) techniques, as they have been used effectively in a variety of similar problems. The author seeks advice and feedback on the optimal theoretical foundation of the framework, the utilization methods adopted (i.e. in the area of continuous and discrete computational optimization) and the method selection for performance analysis and validation. Furthermore, guidance from experts on the field will decisively influence the PhD Thesis, through directing its orientation to current research trends and future opportunities.
ISBN: 978-1-5386-3035-8
Other Identifiers: 10.1109/CBI.2017.39
Appears in Collections:Department of Applied Informatics

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