Primary Author: Mr. Bahaa Hussein
Co-Author(s): Dr. Osama El Sayed Moselhi P Eng
Audience Focus: Intermediate
Application Type: Application
Venue: 2015 AACE International Annual Meeting, Las Vegas, NV, USA
Abstract: This paper proposes a Joint Risk Analysis (JRA) method based on a holistic risk analysis model that performs integrated simulation under one platform for projects risks, cost and time using a combination of critical path scheduling and Monte Carlo simulation. The model takes into consideration approximate methods developed for the representation of: (1) internal uncertainty in activity cost and durations caused by uncertainties in estimates of project line items, (2) discrete risk events that may impact cost and/or schedule, (3) probabilistic time-cost trade-off, (4) conditional risks and opportunities, and, (5) correlation of project attributes. The simulation results are represented visually providing response to questions, (1) what is the probability of finishing a project by a target date and a target cost?, (2) what is the optimal amount of time and cost contingency reserves required for a desired confidence level?, and, (3) what are the probabilistic schedule compression, schedule reduction and cost compression solutions?. Numerical examples will be presented to demonstrate the application and capabilities of the proposed method.
Co-Author(s): Dr. Osama El Sayed Moselhi P Eng
Audience Focus: Intermediate
Application Type: Application
Venue: 2015 AACE International Annual Meeting, Las Vegas, NV, USA
Abstract: This paper proposes a Joint Risk Analysis (JRA) method based on a holistic risk analysis model that performs integrated simulation under one platform for projects risks, cost and time using a combination of critical path scheduling and Monte Carlo simulation. The model takes into consideration approximate methods developed for the representation of: (1) internal uncertainty in activity cost and durations caused by uncertainties in estimates of project line items, (2) discrete risk events that may impact cost and/or schedule, (3) probabilistic time-cost trade-off, (4) conditional risks and opportunities, and, (5) correlation of project attributes. The simulation results are represented visually providing response to questions, (1) what is the probability of finishing a project by a target date and a target cost?, (2) what is the optimal amount of time and cost contingency reserves required for a desired confidence level?, and, (3) what are the probabilistic schedule compression, schedule reduction and cost compression solutions?. Numerical examples will be presented to demonstrate the application and capabilities of the proposed method.