Tao-Ming Cheng* and Hsien-Tang Wu

Department of Construction Engineering,Department of Construction Engineering, Chaoyang University of Technology, Taiwan, R.O.C.

 

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ABSTRACT

Traditional stochastic discrete event simulation method requires the users to fit the task duration data with an empirical probability function or a theoretical probability density function. This has limited the application of discrete event simulation techniques from research stage to construction practice due to the tedious data fitting tasks required for the construction operation planners lacking of statistical background. On the other hand, usually the activity duration estimated by practitioners contains some kind of vagueness caused by estimator’s subjective judgment. This type of vagueness may be more appropriate to be modeled by fuzzy numbers that can be assessed without data fitting. This paper proposes a new mechanism where fuzzy duration modeled by fuzzy numbers can be used in discrete event simulation. Consequently, the tedious data fitting tasks are eliminated but the uncertainty of task duration is still taken into account.


Keywords: fuzzy duration, discrete event simulation


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REFERENCES

[1] AbouRizk, S. M. and Halpin, D. W. 1990.Probabilistic simulation studies for repetitiveconstruction processes, Journalof Construction Engineering and Management,116, 4: 575-594.

[2] Abou Rizk, S. M. and Halpin, D. W. 1992.Statistical properties of construction durationdata, Journal of Construction Engineeringand Management, 118, 3:525-544.

[3] AbouRizk, S. M., Halpin, D. W., andWilson J. R., 1994. Fitting beta distributionsbased on sample data, Journal ofConstruction Engineering and Management,120, 2: 288-305.

[4] Fente, J., Schexnayder, C., and Knutson,K. 2000. Defining a probability distributionfunction for construction simulation,Journal of Construction Engineering andManagement, 126, 3: 234-241.

[5] Maio, C., Schexnayder, C., Knutson, C.K., and Weber S. 2000. Probability distributionfunction for construction simulation,Journal of Construction Engineeringand Management, 126, 4:285-292.

[6] Back, W. E., Boles, W. W., and Fry, G. T.2000. Defining triangular probability distributionsfrom historical cost data,Journal of Construction Engineering andManagement, 126, 1: 29-37.

[7] Farid, F. and Koning, T. L. 1994. Simulationverifies queuing program for selectingloader-truck fleets, Journal ofConstruction Engineering and Management,120, 2: 386-404.

[8] AbouRizk, S. M., Halpin, D. W., andWilson, J. R. 1991. Visual interactive fittingof beta distributions, Journal ofConstruction Engineering and Management,117, 4: 589-605.

[9] Zadeh, L. 1965. Fuzzy sets, InformationControl, 8: 338-353.

[10] Chanas, S. and Kamburowski, J. 1981.The use of fuzzy variables in PERT,Fuzzy sets and systems, 5, 1: 11-19.

[11] Lorterapong, P. and Moselhi, O. 1996,Project-network analysis using fuzzy setstheory, Journal of Construction Engineeringand Management, 122, 4:308-318.

[12] Leu, S. S., Chen, A. T., and Yang, C. H.1999. Fuzzy optimal model for resource-constrained construction scheduling,Journal of Computing in CivilEngineering, 13, 3: 207-216.

[13] Chanas, S. and Zielinski, P. 2001. Criticalpath analysis in the network withfuzzy activity times, Fuzzy sets and system,122, 2: 195-204.

[14] Pierre, B., Gourc, D., and Lacoste, G.2004. Where do we stand with fuzzyproject scheduling?, Journal of ConstructionEngineering and Management,130, 1: 114-123.

[15] Ross, T. J. 1995. “Fuzzy logic with engineeringapplications”, McGraw Hill,New York.

[16] Halpin, D. W. and Riggs, L. S. 1992.“Planning and analysis of constructionoperations”, Wiley, New York.

[17] Cheng, T. M. and Wu, S. T. 2001. COSTuser manual, Department of ConstructionEngineering, Chaoyang University ofTechnology, Taiwan.


ARTICLE INFORMATION



Accepted: 2006-04-03
Available Online: 2006-08-25


Cite this article:

Cheng, T.-M., Wu, H.-T. 2006. Simulation with fuzzy durations. International Journal of Applied Science and Engineering, 4, 189–203. https://doi.org/10.6703/IJASE.2006.4(2).189

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