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

Low, Wei Zhe, De Weerdt, Jochen, Wynn, Moe T., ter Hofstede, Arthur H.M., van der Aalst, Wil M.P., & vanden Broucke, Seppe (2014) Perturbing event logs to identify cost reduction opportunities: a genetic algorithm-based approach. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, IEEE, Beijing International Convention Center, Beijing, pp. 2428-2435.

View at publisher


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.

Impact and interest:

1 citations in Scopus
Search Google Scholar™
0 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

173 since deposited on 31 Jul 2014
60 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 74562
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Process Mining, Business Process Management, Business Process Improvement, Cost Mining, Business Process Analysis
DOI: 10.1109/CEC.2014.6900465
ISBN: 978-1-4799-6626-4
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 [please consult the author]
Deposited On: 31 Jul 2014 00:00
Last Modified: 28 Oct 2014 06:18

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page