“Construction planning and scheduling is a logical analysis of a construction project together with all of its parts, and a thorough knowledge of construction methods, materials, and practices” (Mohamed, 2006 cites Fisk, 1997).
To overcome the challenges of expected duration, associated with the PERT method, Monte Carlo simulations can be used as an alternative.

Monte Carlo simulation approach is the system used to consider variability of project component durations and the uncertainty of proliferation factor during the project lifetime and calculates for the cost spread and allocation (Mohamed, 2006). Monte Carlo seems to generally used in risk management as well as CPM and PERT.

In typical Critical Path Method (CPM) scheduling, it is acceptable to have a single point estimate of the amount of time required to perform a particular task. PERT (Program Evaluation and Review Technique) allows analysing the possible evolution of the achievement time taking into account all projects activities and not only those belonging to the critical path. In Monte Carlo Simulation (MCS), each activity in the project has its own range and pattern of duration possibilities.
From PERT analysis applied for estimate of expected duration of the project as shown in previous section it can be discussed that PERT method can be used to help predict the probability & range of values that will measures the actual duration of the project with using normal distribution and beta to determine the probability of each particular task and expected lifetime of project duration.
Monte Carlo simulation is an analysis tool that enables project manager attaches many factors that may cause or contribute to uncertainties and risk, which this simulation data can be calculated many time (Mohamed, 2006).
A simple triangular distribution was used to generate stochastic inputs applied in the Monte Carlo simulation. This requires only three values: a lower limit or “mini- mum,” a mode or “most probable,” and an upper limit or “maxi- mum” value for the criteria.

Figure1: Triangular distribution for W1 (weight percentage)

Figure2: table shows example from Monte Carlo Simulation Approach to Support Alliance Team Selection (EI Asmar et al, 2009) each factor generated with triangular distribution
Monte Carlo