Simulation of Stochastic Activity Networks

Bajis M. Dodin, Abdelghani A. Elimam

2015

Abstract

Stochastic Activity Networks (SANs) are used in modeling and managing projects that are characterized by uncertainty. SANs are primarily managed using Monte Carlo Sampling (MCS). The accuracy of the results obtained from MCS depends on the sample size. So far the required sample size has been determined arbitrarily and independent of the characteristics of the SAN such as the number of activities and their underlying distributions, number of paths, and the structure of the SAN. In this paper we show that the accuracy of the SANs simulation results would depend on the sample size. Contrary to existing practices, we show that such sample size must reflect the project size and structure, as well as the number of activities. We propose an optimization-based approach to determine the project variance, which in turn is used to determine the number of replications in SAN simulations.

References

  1. Adlakha, V. G. and Kulkarni, V. G., 1989, A Classified Bibliography of Research on Stochastic PERT Networks: 1966-1987, INFOR. 27(3): 272 296.
  2. Clark, C. E., 1961, The Greatest of a Finite Set of Random Variables. Opns Res. 9:146 162.
  3. David, H. A., 1981, Order Statistics, 2nd ed. Wiley, New York.
  4. Demeulemeester, E, Herroelen, W., 2002, Project Scheduling: A Research Handbook, Kluwer Academic Publishing.
  5. Dodin, B. M.,2006, “Practical & Accurate Alternative to PERT” Perspectives in Modern Project Scheduling, J. Weglarz, Springer International series; pp 3-23.
  6. Dodin, B. M. and Elmaghraby, S. E., 1985, Approximating the Criticality Indices of the Activities in PERT Networks, Magt. Sci. 31: 207 223.
  7. Dodin, B., and Sirvanci, M., 1990, Stochastic Networks and the Extreme Value Distribution, Computers and Opns. Res. 17(4): 397 409.
  8. Feller, W., 1968, An Introduction to Probability Theory and its Applications, Vol. I, 3rd ed., John Wiley & Sons, New York.
  9. Galambos, J., 1978, The Asymptotic Theory of Extreme Order Statistics. Wiley, New York.
  10. Glover, F., Klingman, D., and Phillips, N. V., 1992, Network Models in Optimization and their Applications in Practice, John Wiley & Sons, New York.
  11. Herroelen, W., and Leus, R., 2005, Project scheduling under uncertainty: Survey and research potentials, European J. of Opnl. Res., 165(2): 289 306.
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Paper Citation


in Harvard Style

M. Dodin B. and A. Elimam A. (2015). Simulation of Stochastic Activity Networks . In Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-120-5, pages 205-211. DOI: 10.5220/0005561502050211


in Bibtex Style

@conference{simultech15,
author={Bajis M. Dodin and Abdelghani A. Elimam},
title={Simulation of Stochastic Activity Networks},
booktitle={Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2015},
pages={205-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005561502050211},
isbn={978-989-758-120-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Simulation of Stochastic Activity Networks
SN - 978-989-758-120-5
AU - M. Dodin B.
AU - A. Elimam A.
PY - 2015
SP - 205
EP - 211
DO - 10.5220/0005561502050211