ESTIMATING DISTRIBUTION PARAMETERS OF SCHEDULE ACTIVITY DURATION ON THE BASIS OF RISK RELATED TO EXPECTED PROJECT CONDITIONS

Abstract

The paper focuses on handling schedule risks. For each schedule activity, a statistical distribution of its duration is to be defined. Therefore, a research was undertaken to develop a method to assist planners in determining activity duration distribution parameters according to risk level. A triangular distribution was assumed, and its parameters estimated on the basis of three input values (the most likely, pessimistic and optimistic durations). In contrast to the Program Evaluation and Review Technique, the approach proposed in the paper assumes that these input values should be evaluated independently of the particular project’s conditions and could be derived from the planner’s database of past experience. For the risk evaluation, the AHP was adopted. The proposed risk model – considering the diversity of activity types – was based on evaluating and weighting the particular project’s characteristics and expected conditions. This approach, combined with simulation technique, is argued to improve project planning and evaluation of risk mitigation alternatives

Keywords

• Project scheduling, Project risk assessment, Project management

Kaynakça

1. AbouRizk, S., Knowles, P., Hermann, U.R. (2001), “Estimating labor production rates for industrial construction activities”, Journal of Construction Engineering and Management, Vol. 127, No. 6, pp. 502-511.
2. Chua, D.K.H., Kog, Y.C., Loh, P.K., Jaselskis, E.J. (1997), “Model for construction budget performance-neural network approach”, Journal of
3. Construction Engineering and Management, Vol. 123, No. 33, pp. 214-222. Dawood, N. (1998), “Estimating project and activity duration: a risk management approach using network analysis”, Construction Management and Economics, Vol. 16, pp. 41-48.
4. Dey, P.K. (2001), “Decision support system for risk management: A case study”,
5. Management Decision, Vol. 39, No. 8, pp. 634-649. Hanna, A.S., Gunduz, M. (2005), “Early warning signs for distressed projects”,
6. Canadian Journal of Civil Engineering, Vol. 32, No. 5, pp. 796-802. Hertz, D.B. Thomas, H. (1983), Risk analysis and its applications, John Wiley and Sons. New York, NY, USA.
7. Jaselskis, E.J., Ashley, D.B. (1991), “Optimal allocation of project management resources for achieving success”, Journal of Construction Engineering and Management, Vol. 117, No. 2, pp. 321-340.
8. Jaskowski, P., Biruk, S., Bucoń, R. (2010), “Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment”, Automation in Construction, Vol. 19, No. 2, pp. 120-126.

9. Kog, Y.C., Chua, D.K.H., Loh, P.K., Jaselskis, E.J. (1999), “Key determinants for construction schedule performance”, International Journal of Project
10. Management, Vol. 17, No. 6, pp. 351-359. Lee, S., Halpin, D.W. (2003), „Predictive tool for estimating accident risk”,
11. Journal of Construction Engineering and Management, Vol. 129, No. 4, pp. 431- Mehr, R.I., Cammack, E. (1961), Principles of insurance, 4th edition, Homewood,
12. Illinois: Richard D. Irwin Inc. Nasir, D., McCabe, B., Hartono, L. (2003), “Evaluating risk in construction- schedule model (ERIC-S): construction schedule risk model”, Journal of
13. Construction Engineering and Management, Vol. 129, No. 5, pp. 518-527. Papageorge, TE. (1985), Risk management for building professionals. R S. Means
14. Company, Inc. Kingston, Massachusetts, USA. Saaty, T.L., Vargas, L.G. (2007), “Dispersion of group judgements”,
15. Mathematical and Computer Modelling, Vol. 46, pp. 918-925. Schatteman, D., Herroelen, W., Van de Vonder, S., Boone, A. (2008),
16. „Methodology for integrated risk management and proactive scheduling of construction projects”, Journal of Construction Engineering and Management, Vol. 134, No. 11, pp. 885-893. Shi, J.J. (1999), “A neural network based system for predicting earthmoving production”, Construction Manngement and Economics, Vol. 17, pp. 463-471.
17. Sonmez, R., Rowings, J.E. (1998), “Construction labor productivity modeling with neural networks”, Journal of Construction Engineering and Management, Vol. 124, No. 6, pp. 498-504.
18. Van Den Honert, R.C., Lootsma, F.A. (1996), “Group preference aggregation in the multiplicative AHP. The model of the group decision process and Pareto optimality”, European Journal of Operational Research, Vol. 96, pp. 363-370.
19. Zayed, T.M., Halpin, D.W. (2005), “Pile Construction Productivity Assessement”,
20. Journal of Construction Engineering and Management, Vol. 131, No. 6, pp. 705