Skip to main content
Log in

Efficient sensitivity analysis of PERT network performance measures to significant changes in activity time parameters

  • Theoretical Paper
  • Published:
Journal of the Operational Research Society

Abstract

Using a Program Evaluation and Review Technique network model of a project schedule, a method is presented to estimate the effects of changes to the probability distribution for any activity time on several project schedule measures. Computational results show this method to be significantly faster and more accurate than a previously published approach, which estimated the effects of changes to the means of normally distributed activity times on expected project completion time and activity criticality. In addition, the new method allows more flexibility to model changes to activity times, including independent changes to parameters and even changes in the distributional form. Finally, the new method estimates the effects of these changes on several additional performance measures, including the probability of meeting a specified due date and a project (penalty) cost function. All desired estimates are obtained from a single set of simulation runs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1

Similar content being viewed by others

References

  • Bowman RA (1994). Stochastic gradient-based time-cost tradeoffs in PERT networks using simulation. Ann Oper Res 53: 533–551.

    Article  Google Scholar 

  • Bowman RA (2001). Due date-based metrics for activity importance in stochastic activity networks. Ann Oper Res 102: 39–48.

    Article  Google Scholar 

  • Bowman RA (2003). Sensitivity curves for effective project management. Nav Res Log 50: 481–497.

    Article  Google Scholar 

  • Cho JG and Yum BJ (1997). An uncertainty performance measure of activities in PERT networks. Int J Prod Res 35(10): 2737–2758.

    Article  Google Scholar 

  • Cho JG and Yum BJ (2004). Functional estimation of activity criticality indices and sensitivity analysis of expected project completion time. J Opl Res Soc 55: 850–859.

    Article  Google Scholar 

  • Elmaghraby SE (1977). Activity Networks: Project Planning and Control by Network Models. Wiley: New York.

    Google Scholar 

  • Elmaghraby SE (2000). On criticality and sensitivity in activity networks. Eur J Opl Res 127: 220–238.

    Article  Google Scholar 

  • Elmaghraby SE, Fathi Y and Taner MR (1999). On the sensitivity of project variability to activity mean duration. Int J Prod Econ 62(1999): 219–232.

    Article  Google Scholar 

  • Kelley JE and Walker MR (1959). Critical path planning and scheduling. In:Proceedings of the Eastern Joint Computer Conference: Boston. pp. 16–172.

  • Malcolm DG, Roseboom JH, Clark CE and Fazar W (1959). Applications of a technique for research and development program evaluation. Oper Res 7: 646–669.

    Article  Google Scholar 

  • Van Slyke RM (1963). Monte Carlo methods and the PERT problem. Oper Res 11: 839–860.

    Article  Google Scholar 

  • Williams TM (1992). Criticality in stochastic networks. J Opl Res Soc 43: 353–357.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R A Bowman.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bowman, R. Efficient sensitivity analysis of PERT network performance measures to significant changes in activity time parameters. J Oper Res Soc 58, 1354–1360 (2007). https://doi.org/10.1057/palgrave.jors.2602297

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/palgrave.jors.2602297

Keywords

Navigation