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Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation

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  • Trietsch, Dan
  • Mazmanyan, Lilit
  • Gevorgyan, Lilit
  • Baker, Kenneth R.

Abstract

Based on theoretical arguments and empirical evidence we advocate the use of the lognormal distribution as a model for activity times. However, raw data on activity times are often subject to rounding and to the Parkinson effect. We address those factors in our statistical tests by using a generalized version of the Parkinson distribution with random censoring of earliness, ultimately validating our model with field data from several sources. We also confirm that project activities exhibit stochastic dependence that can be modeled by linear association.

Suggested Citation

  • Trietsch, Dan & Mazmanyan, Lilit & Gevorgyan, Lilit & Baker, Kenneth R., 2012. "Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation," European Journal of Operational Research, Elsevier, vol. 216(2), pages 386-396.
    • Handle: RePEc:eee:ejores:v:216:y:2012:i:2:p:386-396
    DOI: 10.1016/j.ejor.2011.07.054
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    3. Vanhoucke, Mario & Colin, Jeroen, 2016. "On the use of multivariate regression methods for longest path calculations from earned value management observations," Omega, Elsevier, vol. 61(C), pages 127-140.
    4. Hajdu M. & Isaac S., 2016. "Sixty years of project planning: history and future," Organization, Technology and Management in Construction, Sciendo, vol. 8(1), pages 1499-1510, December.
    5. Lemos, R.F. & Ronconi, D.P., 2015. "Heuristics for the stochastic single-machine problem with E/T costs," International Journal of Production Economics, Elsevier, vol. 168(C), pages 131-142.
    6. Curto, D. & Acebes, F. & González-Varona, J.M. & Poza, D., 2022. "Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves," International Journal of Production Economics, Elsevier, vol. 253(C).
    7. Vaseghi, Forough & Martens, Annelies & Vanhoucke, Mario, 2024. "Analysis of the impact of corrective actions for stochastic project networks," European Journal of Operational Research, Elsevier, vol. 316(2), pages 503-518.
    8. Junguang Zhang & Xiwei Song & Hongyu Chen & Ruixia (Sandy) Shi, 2016. "Determination of critical chain project buffer based on information flow interactions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1146-1157, September.
    9. Colin, Jeroen & Vanhoucke, Mario, 2014. "Setting tolerance limits for statistical project control using earned value management," Omega, Elsevier, vol. 49(C), pages 107-122.
    10. Martens, Annelies & Vanhoucke, Mario, 2017. "A buffer control method for top-down project control," European Journal of Operational Research, Elsevier, vol. 262(1), pages 274-286.
    11. Fernando Acebes & David Poza & Jose M Gonzalez-Varona & Javier Pajares & Adolfo Lopez-Paredes, 2024. "On the project risk baseline: integrating aleatory uncertainty into project scheduling," Papers 2406.00077, arXiv.org.
    12. Buchem, Moritz & Golak, Julian Arthur Pawel & Grigoriev, Alexander, 2022. "Vessel velocity decisions in inland waterway transportation under uncertainty," European Journal of Operational Research, Elsevier, vol. 296(2), pages 669-678.
    13. David Curto & Fernando Acebes & Jose M Gonzalez-Varona & David Poza, 2024. "Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves," Papers 2406.03500, arXiv.org.
    14. Kim, Byung-Cheol, 2022. "Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment," European Journal of Operational Research, Elsevier, vol. 296(2), pages 679-695.
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