Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1687
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. |
URI: | https://doi.org/10.1109/CBI.2017.39 https://ruomo.lib.uom.gr/handle/7000/1687 |
ISBN: | 978-1-5386-3035-8 |
Other Identifiers: | 10.1109/CBI.2017.39 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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GTsakalidis - CBI2017_final.pdf | 367,76 kB | Adobe PDF | View/Open |
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