Proceedings of International Conference on Applied Innovation in IT  ·  2026/04/22  ·  Vol. 14  ·  Issue 2  ·  pp. 235–242
Impact of Structural Uncertainty on the Time Parameters of the Project Schedule in a Coal Mining Application
Danylo Nazymko
Stochastic Monte Carlo simulation has been used to simulate a mining program schedule in uncertain parametric and structural conditions. Task duration was entered as distributions, reflecting parametric uncertainty. To simulate structural uncertainty, the probability of the task occurrence and the correlation between tasks were considered. The combined effect of these factors was a novel aspect of this research. The probabilities and distributions of the critical path were analyzed as a function of structural and parametric uncertainties. Nonlinear dependences were registered as an effect of the uncertainties. A case study was conducted on an example of a mining program schedule having 107 tasks. This experience confirmed that parametric and structural uncertainties, when combined, can dramatically alter the distribution of the critical path, thereby altering decisions in project management and expediting. Enhancing the sustainability of the coal mining program can be essentially improved by eliminating task correlations and minimizing program structural uncertainties.
Project Schedule Structure Uncertainty Stochastic Simulation Causal Analytics Expediting.
References
  1. P. L. Guida and G. Sacco, “A method for project schedule delay analysis,” Computers and Industrial Engineering, vol. 128, pp. 346-357, Feb. 2019, [Online]. Available: https://doi.org/10.1016/j.cie.2018.12.046.
  2. A. Naderpour, J. Majrouhi Sardroud, M. Mofid, Y. Xenidis and T. Pour Rostam, “Uncertainty Management in Time Estimation of Construction Projects: A Systematic Literature Review and New Model Development,” Scientia Iranica, vol. 0, no. 0, pp. 0-0, Dec. 2017, [Online]. Available: https://doi.org/10.24200/sci.2017.4605.
  3. H.-S. Ang, A. A. Chaker and J. Abdelnour, “Analysis of Activity Networks under Uncertainty,” Journal of the Engineering Mechanics Division, vol. 101, no. 4, pp. 373-387, Aug. 1975, [Online]. Available: https://doi.org/10.1061/JMCEA3.0002028.
  4. Mahmoudi and S. A. Javed, “Project scheduling by incorporating potential quality loss cost in time-cost tradeoff problems,” Journal of Modelling in Management, vol. 15, no. 3, pp. 1187-1204, Feb. 2020, [Online]. Available: https://doi.org/10.1108/JM2-12-2018-0208.
  5. V. V. Nazimko and L. M. Zakharova, “Project Schedule Expediting under Structural and Parametric Uncertainty,” Engineering Management Journal, vol. 35, no. 1, pp. 29-49, Jan. 2023, [Online]. Available: https://doi.org/10.1080/10429247.2022.2030179.
  6. A. De Meyer, C. H. Loch and M. T. Pich, “Managing Project Uncertainty: From Variation To Chaos,” IEEE Engineering Management Review, vol. 30, no. 3, pp. 91-91, 2002, [Online]. Available: https://doi.org/10.1109/EMR.2002.1032403.
  7. B. Black, R. Ainslie, T. Dokka and C. Kirkbride, “Distributionally robust resource planning under binomial demand intakes,” European Journal of Operational Research, vol. 306, no. 1, pp. 227-242, Apr. 2023, [Online]. Available: https://doi.org/10.1016/j.ejor.2022.08.019.
  8. A Guide to the Project Management Body of Knowledge (PMBOK® Guide), 6th ed. Project Management Institute, 2017.
  9. S. S. Shapiro and M. B. Wilk, “An analysis of variance test for normality (complete samples),” Biometrika, vol. 52, nos. 3-4, pp. 591-611, 1965, [Online]. Available: https://doi.org/10.1093/biomet/52.3-4.591.
  10. D. C. Montgomery and G. C. Runger, Applied Statistics and Probability for Engineers. Hoboken, NJ, USA: John Wiley & Sons, 2010, [Online]. Available: https://books.google.fr/books?id=_f4KrEcNAfEC.
  11. D. Nazymko, “Results of stochastic simulation of projects’ schedules,” Mendeley Data, vol. 1, 2023, [Online]. Available: https://doi.org/10.17632/77n5v9tgzp.1.
  12. H. Barthwal, F. J. Calixto and M. van der Baan, “Attenuation tomography using recorded microseismicity in a mine,” in SEG Technical Program Expanded Abstracts 2019, Society of Exploration Geophysicists, Aug. 2019, pp. 3011-3015, [Online]. Available: https://doi.org/10.1190/segam2019-3213938.1.
  13. C.-P. Lu, G.-J. Liu, N. Zhang, T.-B. Zhao and Y. Liu, “Inversion of stress field evolution consisting of static and dynamic stresses by microseismic velocity tomography,” International Journal of Rock Mechanics and Mining Sciences, vol. 87, pp. 8-22, Sep. 2016, [Online]. Available: https://doi.org/10.1016/j.ijrmms.2016.05.008.
  14. H. L. Chen, “Early Prediction of Project Duration: A Longitudinal Study,” Engineering Management Journal, vol. 30, no. 3, pp. 191-202, Jul. 2018, [Online]. Available: https://doi.org/10.1080/10429247.2018.1483169.
ICAIIT 2026
International Conference on Applied Innovation in IT
Bringing together researchers, engineers and practitioners to share advances in applied information technology.
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November 30, 2026
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