Perturbing event logs to identify cost reduction opportunities : a genetic algorithm-based approach

W.Z. Low, J. De Weerdt, M.T. Wynn, A.H.M. Hofstede, ter, W.M.P. Aalst, van der, S. Broucke, vanden

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)
1 Downloads (Pure)


Organisations are constantly seeking new ways to improve operational efficiencies. This research study investigates a novel way to identify potential efficiency gains in business operations by observing how they are carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how they can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A genetic algorithm-based approach is proposed to explore and assess alternative process execution scenarios, where the objective function is represented by a comprehensive cost structure that captures different process dimensions. Experiments conducted with different variants of the genetic algorithm evaluate the approach's feasibility. The findings demonstrate that a genetic algorithm-based approach is able to make use of cost reduction as a way to identify improved execution scenarios in terms of reduced case durations and increased resource utilisation. The ultimate aim is to utilise cost-related insights gained from such improved scenarios to put forward recommendations for reducing process-related cost within organisations.
Original languageEnglish
Title of host publicationIEEE Congress on Evolutionary Computation (CEC'14, Beijing, China, July 6-11, 2014)
Editorsxx Derong Liu, xx Jennie Si
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4799-6626-4
Publication statusPublished - 2014
Eventconference; IEEE Congress on Evolutionary Computation; 2014-07-06; 2014-07-11 -
Duration: 6 Jul 201411 Jul 2014


Conferenceconference; IEEE Congress on Evolutionary Computation; 2014-07-06; 2014-07-11
OtherIEEE Congress on Evolutionary Computation


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