Quantification of Uncertainty in Transportation Infrastructure Projects - FY04NATSRL

Principal Investigator(s):

Ryan Rosandich, Former Associate Professor, UMD-Industrial Engineering

Project summary:

Project Risk Management (PRM) is a main area of interest in the project management community. Risk management has traditionally been done using Monte Carlo simulation to determine the effect of uncertainties in the PERT/CPM network. Although effective, this technique is complex and computationally expensive, so it is typically only used in the early part of the project life cycle. There has been significant recent interest in and progress toward developing an alternative to Monte Carlo simulation based on fuzzy set theory. This research developed and tested a fuzzy technique that can be used throughout the project management life cycle to improve project risk management. It is anticipated that improved PRM techniques will be particularly applicable to northern transportation projects that take place outdoors during a short construction season with limited resources and uncertain funding. Results equaled or exceeded the accuracy of Monte Carlo simulation, but computational times exceeded those of Monte Carlo simulation for networks with many dependencies.


Project details:

  • Project number: 2004022
  • Start date: 07/2003
  • Project status: Completed
  • Research area: Transportation Safety and Traffic Flow
  • Topics: Economics, Planning