Application of a Fuzzy Decision Support System for Project Risk Management in Rail Transportation Infrastructure

Abstract

Abstract. This article presents a fuzzy decision support system (FDSS) to enhance project risk assessment and decision-making in rail transportation infrastructure. Grounded in fuzzy logic theory, the FDSS evaluates risks by analyzing probability, impact, and mitigation strategies, thereby informing contingency levels for project budgets and schedules. By applying compositional rules of inference and incorporating real-world performance factors, the model reduces uncertainty and subjectivity common in traditional qualitative approaches. Drawing on the primary author's experience moderating over 3000 risk sessions in the rail industry, the study highlights how inconsistent and subjective assessments contribute to scope creep, cost escalation, delays, and cancellations. Although rail infrastructure teams typically possess strong technical expertise, they often lack specialized skills for rigorous risk management. The FDSS addresses this gap by producing reliable and replicable evaluations that increase confidence in decision-making. Enhanced fuzzy models, developed through Monte Carlo triangulation risk assessments, enable less-experienced staff to conduct simulations and achieve results comparable to seasoned experts. The system also automates the selection of critical project parameters, improving financial planning and schedule optimization. Its simplicity has been identified as a key factor in user adoption and effectiveness. A case study demonstrates the FDSS in practice, underscoring its adaptability and impact on reducing uncertainty and improving risk management outcomes in real-world transportation infrastructure projects.