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PERT takes the CPM approach one step further by calculating expected times using the optimistic, pessimistic and most likely activity durations of the unique critical path used in CPM. Although the PERT approach takes into account three various durations for each activity, only one unique critical path is established. Monte Carlo simulations build upon the PERT method by providing multiple critical paths based on the variances of the expected durations of the activities. Monte Carlo simulations take into account that activities not on the critical path in the deterministic approach can become
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The Monte Carlo method is a technique that involves using random numbers and probability and running many iterations to solve problems which is analogous to gambling. When we increase the number of iteration, the output of the simulation will increase until we reached the number that increasing the number of iterations will have very insignificant affect on the overall output. A good number of iterations should be when we increase the number it will not produce a significant difference in results. The software sets 1000 iterations as default but it should change based on each different project. For example, in our Network 2 example, the results begin to converge once we reach 211 …show more content…
Figure 2 shows the criticality index and in addition to the critical path used for CPM and PERT, the simulation also considered activity F to have a 30% probability of becoming critical and activity C as an 11% probability of becoming critical. Figure 3 shows a comparison of the probability of the project completion times calculated by the simulation vs. PERT for scenario 2. It is again obvious that the PERT calculations underestimate the probability of the project completion times assuming the Monte Carlo simulation as a baseline. In this example, according to PERT, there is 50% probability that the project will finish by 5/7/08, however the simulation states there is now an 80% probability that the project will finish by 5/7/08. The simulation states that there is now a 62% probability that the project will complete by 5/5/08 which is the deterministic time identified by
The Monte Carlo method is a technique that involves using random numbers and probability and running many iterations to solve problems which is analogous to gambling. When we increase the number of iteration, the output of the simulation will increase until we reached the number that increasing the number of iterations will have very insignificant affect on the overall output. A good number of iterations should be when we increase the number it will not produce a significant difference in results. The software sets 1000 iterations as default but it should change based on each different project. For example, in our Network 2 example, the results begin to converge once we reach 211 …show more content…
Figure 2 shows the criticality index and in addition to the critical path used for CPM and PERT, the simulation also considered activity F to have a 30% probability of becoming critical and activity C as an 11% probability of becoming critical. Figure 3 shows a comparison of the probability of the project completion times calculated by the simulation vs. PERT for scenario 2. It is again obvious that the PERT calculations underestimate the probability of the project completion times assuming the Monte Carlo simulation as a baseline. In this example, according to PERT, there is 50% probability that the project will finish by 5/7/08, however the simulation states there is now an 80% probability that the project will finish by 5/7/08. The simulation states that there is now a 62% probability that the project will complete by 5/5/08 which is the deterministic time identified by