P. Steinmann
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Megaprojects frequently face cost overruns and schedule delays, a pattern known as the iron law of megaprojects, which persists partly because traditional determinis tic or probabilistic planning methods are inadequate for managing deep uncertainty. This form of uncertainty arises when the probability, timing, or impact of key events cannot be reliably estimated, often leading to unrealistic schedules and ineffective risk responses. This study investigates the question: How can Exploratory Modelling and Analysis and Dynamic Adaptive Policy Pathways be applied to improve schedule robustness in infrastructure construction projects? The research focuses on the Schiphol bridge reconstruction, which is part of the Veenix A9 BaHo project and is conducted in collaboration with Count & Cooper. A Discrete Event Simulation (DES) model is built in SimPy and structured using a task dependency graph de rived from the project’s original schedule via NetworkX. The model is sampled 10,000 times under baseline conditions using Latin Hypercube Sampling. In scenario discovery, Patient Rule Induction Method (PRIM) is used in combination with a scaling function identifying six high-impact scenarios, which serve both as inputs for robust policy search and as Adaptation Tipping Points (ATP) for the Dynamic Adaptive Policy Pathways (DAPP) schedule. Robust mitigation strategies are derived using a Multi-Objective Evolutionary Algorithm under the Multi-Objective Robust Decision Making framework, with a second PRIM experiment selecting four final robust policies. These policies correspond to at least one of the high-impact scenarios and form the backbone of a conditional DAPP schedule. The DAPP schedule is evaluated against a static baseline using 5,000 DES simulations with identical uncertainty sampling. In 20 comparative runs, it reduced project duration by an average of 67 days and cost by approximately e97.5 million on the entire project schedule. All three robust policies include the measures new design, overtime labour, and electric machinery, suggesting that a focused subset of actions can improve resilience even when the future is highly uncertain. Unlike prior Decision Making under Deep Uncertainty (DMDU) applications, which often focus on long-term or high-level strategic planning, this study embeds adaptive logic within a highly granular, task-level construction schedule based on real project data. This approach raises methodological challenges in how adaptation tipping points are defined, triggered, and monitored within network-based simulation. The findings demonstrate not only the feasibility of combining Exploratory Modelling and Analysis (EMA) and DAPP in operational construction settings but also the need for further research into real-time scenario recognition and policy switching mechanisms under uncertainty. ...
Megaprojects frequently face cost overruns and schedule delays, a pattern known as the iron law of megaprojects, which persists partly because traditional determinis tic or probabilistic planning methods are inadequate for managing deep uncertainty. This form of uncertainty arises when the probability, timing, or impact of key events cannot be reliably estimated, often leading to unrealistic schedules and ineffective risk responses. This study investigates the question: How can Exploratory Modelling and Analysis and Dynamic Adaptive Policy Pathways be applied to improve schedule robustness in infrastructure construction projects? The research focuses on the Schiphol bridge reconstruction, which is part of the Veenix A9 BaHo project and is conducted in collaboration with Count & Cooper. A Discrete Event Simulation (DES) model is built in SimPy and structured using a task dependency graph de rived from the project’s original schedule via NetworkX. The model is sampled 10,000 times under baseline conditions using Latin Hypercube Sampling. In scenario discovery, Patient Rule Induction Method (PRIM) is used in combination with a scaling function identifying six high-impact scenarios, which serve both as inputs for robust policy search and as Adaptation Tipping Points (ATP) for the Dynamic Adaptive Policy Pathways (DAPP) schedule. Robust mitigation strategies are derived using a Multi-Objective Evolutionary Algorithm under the Multi-Objective Robust Decision Making framework, with a second PRIM experiment selecting four final robust policies. These policies correspond to at least one of the high-impact scenarios and form the backbone of a conditional DAPP schedule. The DAPP schedule is evaluated against a static baseline using 5,000 DES simulations with identical uncertainty sampling. In 20 comparative runs, it reduced project duration by an average of 67 days and cost by approximately e97.5 million on the entire project schedule. All three robust policies include the measures new design, overtime labour, and electric machinery, suggesting that a focused subset of actions can improve resilience even when the future is highly uncertain. Unlike prior Decision Making under Deep Uncertainty (DMDU) applications, which often focus on long-term or high-level strategic planning, this study embeds adaptive logic within a highly granular, task-level construction schedule based on real project data. This approach raises methodological challenges in how adaptation tipping points are defined, triggered, and monitored within network-based simulation. The findings demonstrate not only the feasibility of combining Exploratory Modelling and Analysis (EMA) and DAPP in operational construction settings but also the need for further research into real-time scenario recognition and policy switching mechanisms under uncertainty.