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

AbstractProbabilistic 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 c...

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Bibliographic Details
Published in:Journal of construction engineering and management 2021-08, Vol.147 (8)
Main Authors: Kammouh, Omar, Nogal, Maria, Binnekamp, Ruud, Wolfert, A. R. M. (Rogier)
Format: Article
Language:English
Subjects:
Adaptive control
Completion time
Controllers
Cost control
Multiple objective analysis
Optimization
Schedules
Simulation
Statistical analysis
Technical Papers
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Summary:AbstractProbabilistic 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.
ISSN:0733-9364
1943-7862
DOI:10.1061/(ASCE)CO.1943-7862.0002126