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

Abstract

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 costeffective 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.

MitC: Open-source software for construction project control and delay mitigation

“…This paper focuses on the application of the MitC software rather than the theoretical background behind it. Extensive information on the theory and algorithm details behind the MitC can be found in the repository [7] or in the previously published paper on the Mitigation Controller [8].…”

“…That is, the mitigation cost depends on the outcome of the mitigation capacity in a given iteration. More information on the mitigation cost definition can be found in [8].…”

“…As shown, the optimization problem is a minimization of the net cost. More information on the rationale behind the mathematical formulation of the optimization problem can be found in [8]. The results of the MC simulation are used to create the cumulative probability distribution of project completion (S-curve) as well as other outputs.…”

Dynamic control for construction project scheduling on-the-run

“…To resolve the MC limitations and/or drawbacks mentioned above, the Mitigation Controller concept (MitC) was recently introduced [19]. On this basis, a new software tool has also been released [17,18].…”

“…This paper inherits and builds upon the original MitC concept; hence, the basic starting points will be briefly summarized in the following subsections 2.1-2.4 to make this paper independently readable. For a complete description of the multi-objective optimization in the original MitC, the reader is referred to [19].…”