Journal article Open Access

Dynamic Cooperative Model for Ranking Construction Risks using Monte Carlo Simulation

Amr Mahmoud; Ahmed Elhakeem; Ahmed Elyamany


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{
  "DOI": "10.35940/ijitee.L7953.1091220", 
  "container_title": "International Journal of Innovative Technology and Exploring Engineering (IJITEE)", 
  "language": "eng", 
  "title": "Dynamic Cooperative Model for Ranking  Construction Risks using Monte Carlo  Simulation", 
  "issued": {
    "date-parts": [
      [
        2020, 
        10, 
        30
      ]
    ]
  }, 
  "abstract": "<p>Construction projects suffer from diverse uncertainties that hinder the key objectives&rsquo; 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&#39; 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&#39;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.</p>", 
  "author": [
    {
      "family": "Amr Mahmoud"
    }, 
    {
      "family": "Ahmed Elhakeem"
    }, 
    {
      "family": "Ahmed Elyamany"
    }
  ], 
  "page": "211-216", 
  "volume": "9", 
  "type": "article-journal", 
  "issue": "12", 
  "id": "5844690"
}
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