Dynamic Cooperative Model for Ranking Construction Risks using Monte Carlo Simulation
Amr Mahmoud1, Ahmed Elhakeem2, Ahmed Elyamany3

1Amr Mahmoud, Graduate Student, Construction and Building Engineering Department, Arab Academy for Science, Technology & Maritime Transport, Cairo, Egypt.
2Ahmed Elhakeem, Associate Professor, Construction and Building Engineering Department, Arab Academy for Science, Technology & Maritime Transport, Cairo, Egypt.
3Ahmed Elyamany, Associate Professor, Construction Engineering Department, Zagazig University, Currently at The British University in Egypt.
Manuscript received on September 10, 2020. | Revised Manuscript received on September 22, 2020. | Manuscript published on October 10, 2020. | PP: 211-216 | Volume-9 Issue-12, October 2020 | Retrieval Number: 100.1/ijitee.L79531091220 | DOI: 10.35940/ijitee.L7953.1091220
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Abstract: Construction projects suffer from diverse uncertainties that hinder the key objectives’ achievement. These uncertainties represent risks that may appear through the project life cycle. This paper introduces a quantitative model to estimate and rank risks dynamically during the risk planning phase. Such ranking would help decision-makers appropriately respond to and/or control construction risks. The model provides proper risk contingency reserves for both project time and cost that meet decision-makers’ selected confidence levels using Monte Carlo Simulation (MCS). In order to quantify the project uncertainty, severities of residual risks are determined and allocated at the project’s activities-level using a planning/scheduling spreadsheet model and a MCS tool suitable for spreadsheets. The model is able to calculate the contribution of each risk from the determined contingency at both the project level for both the time and cost at the decision-maker confidence level. The model represents a direct implementation for a Risk Planning Contingency Model (RPCM); which involves four modules as follows: (1) Risk Register (RR), (2) Risk Allocator (RA), (3) Risk Simulator (RS), and (4) Contingency Calculator (CC). These modules are hosted in a critical path model scheduling spreadsheet to facilitate risk management. In addition, a simulation engine add-in is used for analyzing the probability distribution for the project time and cost outcomes. In order to verify the proposed model, the process and analysis have been applied to a case study project. The results show that the RPCM is capable to rank and estimate the residual risks in an easy, fast, and effective way. 
Keywords: Contingency Reserve, Monte Carlo Simulation, Risk Ranking, and Quantitative risk analysis.
Scope of the Article: Simulation Optimization and Risk Management