Revising history for cost-informed process improvement

Low, Wei Zhe, vanden Broucke, Seppe K.L.M., Wynn, Moe T., ter Hofstede, Arthur H.M., De Weerdt, Jochen, & van der Aalst, Wil M.P. (2016) Revising history for cost-informed process improvement. Computing, 98(9), pp. 895-921.

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Abstract

Organisations are constantly seeking new ways to improve operational efficiencies. This study investigates a novel way to identify potential efficiency gains in business operations by observing how they were 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 these trade-offs can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A number of optimisation techniques are proposed to explore and assess alternative execution scenarios. The objective function is represented by a cost structure that captures different process dimensions. An experimental evaluation is conducted to analyse the performance and scalability of the optimisation techniques: integer linear programming (ILP), hill climbing, tabu search, and our earlier proposed hybrid genetic algorithm approach. The findings demonstrate that the hybrid genetic algorithm is scalable and performs better compared to other techniques. Moreover, we argue that the use of ILP is unrealistic in this setup and cannot handle complex cost functions such as the ones we propose. Finally, we show how cost-related insights can be gained from improved execution scenarios and how these can be utilised to put forward recommendations for reducing process-related cost and overhead within organisations.

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ID Code: 85431
Item Type: Journal Article
Refereed: Yes
Keywords: Business Process Analysis, Business Process Improvement, Process Mining, Optimisation, Cost-Informed, Genetic Algorithm
DOI: 10.1007/s00607-015-0478-1
ISSN: 1436-5057
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Decision Support and Group Support Systems (080605)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Springer
Deposited On: 14 Jul 2015 22:53
Last Modified: 22 Sep 2016 01:36

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