DocumentCode :
239058
Title :
Perturbing event logs to identify cost reduction opportunities: A genetic algorithm-based approach
Author :
Low, W.Z. ; De Weerdt, J. ; Wynn, M.T. ; ter Hofstede, Arthur H. M. ; van der Aalst, Wil M. P. ; vanden Broucke, S.
Author_Institution :
Queensland Univ. of Technol. (QUT), Brisbane, QLD, Australia
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
2428
Lastpage :
2435
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.
Keywords :
cost reduction; genetic algorithms; business operations; comprehensive cost structure; cost environment; cost reduction opportunity; cost utilisation; event logs; genetic algorithm-based approach; objective function; potential efficiency gains; process dimensions; process execution assessment; process-related cost reduction; reduced case durations; resource utilisation; time utilisation; Abstracts; Genetic algorithms; Insurance; Maintenance engineering; Optimization; Resource management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
Type :
conf
DOI :
10.1109/CEC.2014.6900465
Filename :
6900465
Link To Document :
بازگشت