A Risk-Based Decision Framework for Selecting Owner Estimate Percentiles in EPC Projects Using Monte Carlo Simulation and PROMETHEE II

Abstract

Owner Estimate (OE) determination in Engineering, Procurement, and Construction (EPC) projects remains a challenging process due to high levels of uncertainty arising from technical complexity, market fluctuations, incomplete information, and project-specific risks. Although Monte Carlo Simulation (MCS) can generate probabilistic cost estimates through percentile distributions, it does not provide a systematic mechanism for selecting the most appropriate percentile as the final OE. This study aims to develop a risk-based decision framework that integrates probabilistic estimation and multi-criteria decision-making approaches to support a transparent and objective OE percentile selection process. The research applied a combination of Monte Carlo Simulation to generate percentile-based alternatives, the Delphi method to identify and weight relevant decision criteria based on expert consensus, and PROMETHEE II to rank alternative OE percentiles. The results demonstrate that the proposed framework effectively transforms probabilistic outputs into structured decision alternatives by considering technical, economic, and organizational factors. Among the evaluated alternatives, P80 achieved the highest PROMETHEE II net flow value (? = 0.508), indicating its superior balance between risk mitigation and cost efficiency. The findings confirm that OE selection should not rely solely on statistical confidence levels or managerial judgment but requires an integrated analytical approach. This study concludes that the proposed framework provides a transparent, reproducible, and risk-based decision model to improve consistency and reliability in EPC project cost estimation.