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A Hybrid Fuzzy Discrete Event Simulation Framework for Analysis of Stochastic and Subjective Uncertainties in Construction Projects Open Access


Other title
Subjective Uncertainty
Fuzzy logic
Construction Management
Discrete Event Simulation
Type of item
Degree grantor
University of Alberta
Author or creator
Sadeghi, Naimeh
Supervisor and department
Aminah Robinson Fayek (Civil and Environmental Engineering)
Examining committee member and department
Armann Ingolfsson (Business)
Yasser Mohamed (Civil and Environmental Engineering)
Mustafa Gul (Civil and Environmental Engineering)
Dulcy Abraham (Civil Engineering)
Department of Civil and Environmental Engineering
Construction Engineering and Managemen
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Discrete event simulation (DES) has proven to be an indispensable tool for planning and analyzing construction projects. Appropriate consideration of uncertainties of the inputs of DES results in more realistic outputs. Uncertainty in general can be categorized as stochastic and subjective. Stochastic uncertainty is a system property and represents the uncertainty associated with variation of a variable. Stochastic uncertainty can be represented by a probability distribution. On the other hand, subjective uncertainty represents the lack of knowledge of the system modeller regarding the actual value of a variable. Subjective uncertainty, for example, can be a result of lack of data or linguistic expression. Subjective uncertainty is often encountered in construction simulation due to the linguistic expression, and use of expert judgment in estimating activity durations. However, traditional DES is only able to consider stochastic uncertainty using probability distributions; and cannot handle subjective uncertainty. Fuzzy set theory provides a methodology for mathematical modelling of subjective uncertainty. Recently, fuzzy discrete event simulation (FDES) has been proposed for considering subjective uncertainty in construction simulation models. However, the fundamental differences between fuzzy numbers and probability distributions introduce new challenges to FDES frameworks. Furthermore, subjective and stochastic uncertainties may simultaneously exist in a simulation model. However, no framework is available that is able to consider both types of uncertainties in a discrete event simulation model. Firstly, this research, proposes a methodology for considering subjective uncertainty in estimating the activity durations or productivity of construction projects. Secondly, a FDES framework is proposed for dealing with subjective uncertainty of activity durations. The proposed framework advances the previous FDES frameworks by: (1) solving the problem of time paradox (overestimation or underestimation of the simulation time) (2) proposing a methodology for analyzing queues in FDES. Furthermore, this research proposes a novel hybrid discrete event simulation (HDES) framework that can simultaneously deal with both stochastic and subjective uncertainties. FDES framework is integrated within the proposed HDES framework for processing fuzzy uncertainty. Sampling from probability distributions are used to process stochastic uncertainty. The proposed framework is validated against analytically solved queuing examples containing both fuzzy and probabilistic uncertainty. The practicality of this framework is demonstrated using a case study of a module assembly yard. The results of this case study are compared with the results of FDES and traditional DES to demonstrate the advantages of the proposed HDES framework.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Sadeghi, N., and Fayek, A. Robinson. (2011). A fuzzy-based approach for proactive scheduling of construction projects. Proceedings, 3rd International/9th Construction Specialty Conference, CSCE, Ottawa, Ont., June 14-17: CN-007-1-CN-007-11.Sadeghi, N., Fayek, A. Robinson, and Pedrycz, W. (2010). Fuzzy Monte Carlo simulation and risk assessment in construction. Computer-Aided Civil and Infrastructure Engineering, 25(4): 238-252.Sadeghi, N., Fayek, A. R., & Mosayebi, S. P. (2013, June). Developing a fuzzy discrete event simulation framework within a traditional simulation engine. In IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint (pp. 1102-1106). IEEE.

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