Abstract
Two important performance measures related to Stochastic Activity Networks (SANs) are the length of the longest path and the probability that this longest path length exceeds a given threshold. We examine the sensitivity of these performance measures to changes in the underlying parameters of the arc length distributions by calculating four different derivative estimators via Monte Carlo simulation. We explore the statistical properties of these estimators and suggest a method of combining these estimators as a tool for variance reduction
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R.A. Bowman. Stochastic gradient-based time-cost tradeoffs in PERT networks using simulation. Annals of Operations Research 53, 533–551, 1994.
R.A. Bowman. Efficient estimation of are criticalities in stochastic activity networks. Management Science 41, 58–67, 1995.
S.E. Elmaghraby. On criticality and sensitivity in activity networks. European Journal of Operational Research 127, 220–238, 2000.
M.C. Fu. Sensitivity Analysis for Simulation of Stochastic Activity Networks, Topics in Modeling. Optimization, and Decision Technologies: Honoring Saul Gass’ Contributions to Operations Research (tentative title), F.B. Alt, M.C. Fu and B.L. Golden, editors, Kluwer Academic Publishers, 2006
M.C. Fu. Stochastic Gradient Estimation, Chapter 19 in Handbooks in Operations Research and Management Science: Simulation, S.G. Henderson and B.L. Nelson, eds., Elsevier, 2006.
Krivulin, Nikolai. Unbiased Estimates for Gradients of Stochastic Network Performance Measures. Acta Applicandae Mathematicae, 1993, Vol. 33, p. 21–43.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Groër, C., Ryals, K. (2007). Sensitivity Analysis in Simulation of Stochastic Activity Networks: A Computational Study. In: Baker, E.K., Joseph, A., Mehrotra, A., Trick, M.A. (eds) Extending the Horizons: Advances in Computing, Optimization, and Decision Technologies. Operations Research/Computer Science Interfaces Series, vol 37. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-48793-9_12
Download citation
DOI: https://doi.org/10.1007/978-0-387-48793-9_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-48790-8
Online ISBN: 978-0-387-48793-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)