Project managers need to understand the impact of uncertainties on project plans. Two techniques that can meet this need are Monte Carlo simulation (MCS) and discrete event simulation (DES). MCS uses random samples of input parameters to determine the system outputs. DES also uses random samples but models the sequence of events in a system with time modeled explicitly. Both use the results of repeated executions to determine the distribution of outputs. There has been an increasing use of MCS for evaluating the impact of uncertainties in activity durations and costs on the project’s duration and total cost. There are few reports of use of DES for such purpose. This paper presents analyses of a hypothetical project using both the simulation approaches. The results show that discrete event simulation has an advantage for the scope of this study and based on the features and limitations of the software used.
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A Tale of Two Simulations for Project Managers
Semantic Scholar · Engineering · 2020
Abstract
Project managers need to understand the impact of uncertainties on project plans. Two techniques that can meet this need are Monte Carlo simulation (MCS) and discrete event simulation (DES). MCS uses random samples of input parameters to determine the system outputs. DES also uses random samples but models the sequence of events in a system with time modeled explicitly. Both use the results of repeated executions to determine the distribution of outputs. There has been an increasing use of MCS for evaluating the impact of uncertainties in activity durations and costs on the project’s duration and total cost. There are few reports of use of DES for such purpose. This paper presents analyses of a hypothetical project using both the simulation approaches. The results show that discrete event simulation has an advantage for the scope of this study and based on the features and limitations of the software used.