Keywords: computer simulation system, discrete-event simulation, construction operations simulation, fuzzy duration, fuzzy set, fuzzy number.

Discrete event simulation has gained its reputation in analyzing construction operations for several decades. Simulation experiments involve two tasks: building models representing the operations which are going to be analyzed and deciding the activity duration that will be used in the experiments [1,2,3,4,5]. Therefore, the keys for conducting a successful simulation experiment should include both building a correct model and measuring accurately the activity durations.

Construction operations are full of uncertainties due to their nature that operations will be affected by many factors such as weather conditions. Hence, it is usually that the activity duration used in simulation is stochastic for the sake of modeling the uncertainty of the duration [6]. As a result, in the construction area, extensive studies have focused on developing the patterns of probability distribution functions (PDFs) that best represent the uncertainty of the activity duration [7,8].

In order to correctly select the probability distribution for the activity duration, normally data-fitting processes have to be performed. The formal statistical tests such as goodness-of-fit test, then usually are required for successfully selecting an appropriate PDF for an activity duration. However, construction practitioners usually do not have a statistical testing skill. Hence, it limits the application of the simulation methodology to practical construction applications. Moreover, most of the cases, historical data for fitting PDFs to activity durations can not be accessed. Thus, the deterministic duration of activities is adopted occasionally for running a simulation and thus the uncertainty of the activity duration is scarified.

On the other hand, the subjective judgment of the activity duration from experts is usually used for the estimation of the length of the duration in the simulation experiments. The subjective statements used by experts in estimating the task duration normally contains some sort of imprecision that is not easy to be modeled in a stochastic way but is more easily to be represented in the form of fuzzy data. In addition, there are two merits of adopting fuzzy data in the task of duration estimation: (1) a huge amount of historical data is not required to be collected; (2) no tedious data-fitting tasks need to be performed. How to cope with fuzzy activity duration in running discrete event simulation is not well explored. Thus, this paper proposes the mechanism that a fuzzy number can be used to represent the activity duration when running discrete-event simulations. In addition, the proposed mechanism is implemented via reprogramming construction operation simulation tool (COST) [9], which is the discrete-event simulation computer program, for facilitating the simulation with a fuzzy duration.

1

S.M. AbouRizk, D.W. Halpin, "Statistical properties of construction duration data", Journal of Construction Engineering and Management, ASCE, 118 (3), 525-544, 1992. doi:10.1061/(ASCE)0733-9364(1992)118:3(525)

2

S.M. AbouRizk, D.W. Halpin, J.R. Wilson, "Fitting beta distributions based on sample data", Journal of Construction Engineering and Management, ASCE, 120 (2), 288-305, 1994. doi:10.1061/(ASCE)0733-9364(1994)120:2(288)

3

F. Farid, T.L. Koning, "Simulation verifies queuing program for selecting loader-truck fleets", Journal of Construction Engineering and Management, ASCE, 120 (2), 386-404, 1994.doi:10.1061/(ASCE)0733-9364(1994)120:2(386)

4

J. Fente, C. Schexnayder, K. Knutson, "Defining a probability distribution function for construction simulation", Journal of Construction Engineering and Management, ASCE, 126 (3), 234-241, 2000. doi:10.1061/(ASCE)0733-9364(2000)126:3(234)

5

C. Maio, C. Schexnayder, K. Knutson, S. Weber, "Probability distribution function for construction simulation", J. of Construction Engineering and Management, ASCE, 126 (4), 285-292, 2000. doi:10.1061/(ASCE)0733-9364(2000)126:4(285)

6

S.M. AbouRizk, D.W. Halpin, "Probabilistic simulation studies for repetitive construction processes", Journal of Construction Engineering and Management, ASCE, 116 (4), 575-594, 1990. doi:10.1061/(ASCE)0733-9364(1990)116:4(575)

7

J.C. Martinez, P.G. Ioannou, "General-purpose system for effective construction simulation", Journal of Construction Engineering and Management, ASCE, 125 (4), 265-276, 1999. doi:10.1061/(ASCE)0733-9364(1999)125:4(265)

8

W.E. Back, W.W. Boles, G.T. Fry, "Defining triangular probability distributions from historical cost data", Journal of Construction Engineering and Management, ASCE, 126 (1), 29-37, 2000. doi:10.1061/(ASCE)0733-9364(2000)126:1(29)

9

T.-M. Cheng, S.-T. Wu, "COST user manual", Department of Construction Engineering, Chaoyang University, Taiwan, 2001.

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