Dredging projects are capital-intensive, complex, and exposed to high uncertainty from weather, soil conditions, technical reliability, and logistics. During early tender bidding, contractors must estimate project duration, costs, and emissions with limited time, data, and engineering capacity. Detailed studies are rarely feasible, yet bidders must anticipate which uncertainties drive outcomes. Existing methods provide detailed analyses of individual aspects but require resources unavailable at this stage. What is missing is a structured, rapid way to identify and prioritize the most critical risks.

This thesis develops a rapid risk-assessment methodology that combines expert judgment with Discrete Event Simulation (DES). The approach enables contractors to focus scarce resources on uncertainties that matter most for project success. The open-source platform OpenCLSim was chosen for its layered structure (activity, sequence, asset, project), flexibility, and suitability for dredging logistics.

Inputs are expert estimates of risk likelihood and impact, grouped into four categories: workability, technical, logistical, and environmental/social. These are mapped to the correct project layer and encoded through a custom Risk & Uncertainty extension using occurrence models and impact distributions. Risks are thus represented at appropriate levels—activity variability, sequence delays, or project-wide interruptions.

Outputs are probabilistic KPIs for project duration, costs, and emissions, obtained via Monte Carlo simulation and reported at the industry-accepted P80 level. This provides risk-aware estimates under uncertainty. The method also includes mitigation evaluation, where updated risk parameters are simulated with countermeasures. Comparing mitigation costs with reductions in time, cost, and emissions highlights which measures are financially viable and where engineering capacity should be allocated.

The method shows how risks at different layers interact: delays off the critical path may be absorbed in project duration, but still cause idle costs and emissions. Thus, time buffering and cascading impacts diverge, clarifying risk importance for different KPIs.

Applied to the Malmporten case, expert elicitation identified three risks: mooring-time uncertainty, a sequence-level backhoe breakdown, and project-wide turbidity exceedance. Simulations revealed that the backhoe breakdown had the greatest impact by delaying dependent assets; turbidity exceedance mainly raised costs and emissions; mooring-time uncertainty had modest operational effects. Mitigation analysis showed sequence-critical risks offered the highest leverage: on-site spare parts sharply reduced repair durations and had the best benefit–cost ratio, while silt screens and experienced captains offered smaller, complementary benefits.

Conclusion: The methodology provides a structured, time-efficient decision-support tool for early tendering. By combining expert judgment with DES, it delivers probabilistic insights into which risks matter most and which countermeasures are justified. Though demonstrated in one case, the method can be generalized to other dredging project types and extended with advanced failure models, calibrated inputs, and broader KPI frameworks. It equips contractors to make more reliable, risk-informed decisions under the constraints of early tendering.