, Volume 98, Issue 9, pp 895–921 | Cite as

Revising history for cost-informed process improvement

  • W. Z. LowEmail author
  • S. K. L. M. vanden Broucke
  • M. T. Wynn
  • A. H. M. ter Hofstede
  • J. De Weerdt
  • W. M. P. van der Aalst


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.


Business process analysis Business process improvement Process mining Optimisation Cost-informed  Genetic algorithm 

Mathematics Subject Classification

68U01 68U35 



This work is supported by Australian Research Council (ARC) Discovery Grant 120101624. We would also like to thank Professor Erhan Kozan for his input to the ILP formalisation.


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Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • W. Z. Low
    • 1
    Email author
  • S. K. L. M. vanden Broucke
    • 2
  • M. T. Wynn
    • 1
  • A. H. M. ter Hofstede
    • 1
    • 3
  • J. De Weerdt
    • 2
  • W. M. P. van der Aalst
    • 1
    • 3
  1. 1.Queensland University of Technology (QUT)BrisbaneAustralia
  2. 2.Department of Decision Sciences and Information ManagementKU LeuvenLeuvenBelgium
  3. 3.Department of Mathematics and Computer ScienceTechnische Universiteit Eindhoven (TU/e)EindhovenThe Netherlands

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