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Title: Evolutionary Multi-Objective Optimization of business process designs with pre-processing
Authors: Georgoulakos, Kostas
Vergidis, Kostas
Tsakalidis, George
Samaras, Nikolaos
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Social sciences::Economics and Business::Business and Management
Keywords: business process
evolutionary algorithms
Issue Date: 2017
First Page: 897
Last Page: 904
Volume Title: 2017 IEEE Congress on Evolutionary Computation (CEC)
Abstract: This paper discusses the problem of business process optimization within a multi-objective evolutionary framework. Business process optimization (BPO) is considered as the problem of constructing feasible business process designs with optimum attribute values such as duration and cost. The proposed approach involves a pre-processing stage and the application of a series of Evolutionary Multi-Objective Optimization Algorithms (EMOAs) in an attempt to generate a series of diverse optimized business process designs for the same process requirements. The proposed optimization framework introduces a quantitative representation of business processes involving two matrices one for capturing the process design and one for calculating and evaluating the process attributes. It also introduces an algorithm that checks the feasibility of each candidate solution (i.e. process design). The work presented in this paper is aimed to investigate the benefits that come from the utilization of a pre-processing stage in the execution process of the EMOAs. The experimental results demonstrate that the proposed optimization framework is capable of producing a satisfactory number of optimized design alternatives considering the problem complexity and high rate of infeasibility. The addition of the pre-processing stage appears to have a positive effect on the framework by producing more non-dominated solutions in reduced time frames.
ISBN: 978-1-5090-4601-0
Other Identifiers: 10.1109/CEC.2017.7969404
Appears in Collections:Department of Applied Informatics

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