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

Content Provider	IEEE Xplore Digital Library
Author	Low, W.Z. De Weerdt, J. Wynn, M.T. ter Hofstede, A.H.M. van der Aalst, W.M.P. vanden Broucke, S.
Copyright Year	2014
Description	Author affiliation: KU Leuven, Leuven, Belgium (De Weerdt, J.; vanden Broucke, S.) \|\| Queensland Univ. of Technol. (QUT), Brisbane, QLD, Australia (Low, W.Z.; Wynn, M.T.; ter Hofstede, A.H.M.; van der Aalst, W.M.P.)
Abstract	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.
Starting Page	2428
Ending Page	2435
File Size	577772
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900465
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Resource management Genetic algorithms Optimization Insurance Abstracts Maintenance engineering
Content Type	Text
Resource Type	Article

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