Mitigation Controller: Adaptive Simulation Approach for Planning Control Measures in Large Construction Projects

Omar Kammouh*, Maria Nogal, Ruud Binnekamp, A. R.M.Rogier Wolfert

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
27 Downloads (Pure)


Probabilistic Monte Carlo simulations are often used to determine a project's completion time given a required probability level. During project execution, schedule changes negatively affect the probability of meeting the project's completion time. A manual trial and error approach is then conducted to find a set of mitigation measures to again arrive at the required probability level. These are then implemented as scheduled activities. The mitigation controller (MitC) proposed in this paper automates the search for finding the most cost-effective set of mitigation measures using multiobjective linear optimization so that the probability of timely completion remains at the required level. It considers different types of uncertainties and risk events in the probabilistic simulation. Moreover, it removes the fundamental modeling error that exists in the traditional probabilistic approach by incorporating human control and adaptive behavior in the simulation. Its usefulness is demonstrated using an illustrative example derived from a recent Dutch construction project in which delay is not permitted. It is shown that the MitC is capable of identifying the most effective mitigation strategies allowing for substantial cost savings.

Original languageEnglish
Article number04021093
Number of pages15
JournalJournal of Construction Engineering and Management
Issue number8
Publication statusPublished - 2021

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Adaptive
  • Corrective/control measures
  • Mitigation
  • Monte Carlo simulation
  • Project scheduling
  • Risk event
  • Time-cost optimization
  • Uncertainty


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