A Fuzzy Discrete Event Simulation Framework for Construction Applications: Improving the Simulation Time Advancement
Publication: Journal of Construction Engineering and Management
Volume 142, Issue 12
Abstract
Discrete event simulation (DES) has been widely used for scheduling and analysis of construction projects. Integration of DES with fuzzy logic enhances the capabilities of DES by capturing imprecise, subjective, and linguistically expressed knowledge in the simulation inputs using fuzzy numbers. However, available fuzzy discrete event simulation (FDES) methodologies have significant shortcomings in handling fuzzy ranking and updating of the simulation time. These limitations often result in the inaccurate estimation of the start and completion times for individual activities and the whole project. This paper presents a new approach for calculating the event times to increase the accuracy of simulation time estimation in FDES. The major contributions of this paper are in integrating DES with fuzzy logic to increase its applicability in the construction domain and increasing the accuracy of FDES results. The proposed FDES approach was implemented in a construction project scheduling example, which confirms the higher accuracy of the proposed methodology compared to existing FDES methodologies.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research is funded by the NSERC Industrial Research Chair in Strategic Construction Modeling and Delivery held by Dr. Aminah Robinson Fayek.
References
AbouRizk, S. M. and Sawhney, A. (1993). “Subjective and interactive duration estimation.” Can. J. Civ. Eng., 20(3), 457–470.
Ahuja, H. N. and Nandakumar, V. (1985). “Simulation model to forecast project completion time.” J. Constr. Eng. Manage., 325–342.
Al-Hussein, M., Athar Niaz, M., Yu, H., and Kim, H. (2006). “Integrating 3D visualization and simulation for tower crane operations on construction sites.” Autom. Constr., 15(5), 554–562.
Alvanchi, A., Nguyen, A., and Abourizk, S. M. (2012). “Structural steel fabrication special purpose simulation.” Construction Research Congress 2012: Construction Challenges in a Flat World, ASCE, Reston, VA, 1391–1399.
Anglani, A., Grieco, A., Nucci, F., Semeraro, Q., and Tolio, T. (2000). “A new algorithm to rank temporal fuzzy sets in fuzzy discrete event simulation.” Proc., Fuzzy Syst. Conf., IEEE, San Antonio, 923–928.
Ayyub, B. M., and Haldar, A. (1984). “Project scheduling using fuzzy set concepts.” J. Constr. Eng. Manage., 189–204.
Azzaro, C., Floquet, P., Pibouleau, L., and Domenech, S. (1997). “A fuzzy approach for performance modeling in batch plant: Application to semiconductor manufacturing.” IEEE Trans. Fuzzy Syst., 5(3), 338–357.
Chang, W. (1981). “Ranking of fuzzy utilities with triangular membership functions.” Proc., Int. Conf. on Policy Analysis and Systems, Vol. 272, 263–272.
Chen, S. J. and Chen, S. M. (2003). “A new method for handling multicriteria fuzzy decision-making problems using FN-IOWA operators.” Cybern. Syst., 34(2), 109–137.
Chen, S. J. J., Hwang, C. L., Beckmann, M. J., and Krelle, W. (1992). Fuzzy multiple attribute decision making: Methods and applications, Springer, New York.
Dubois, D. J. (1980). Fuzzy sets and systems: Theory and applications, Academic Press, New York.
Guyonnet, D., Bourgine, B., Dubois, D., Fargier, H., Côme, B., and Chilès, J. P. (2003). “Hybrid approach for addressing uncertainty in risk assessments.” J. Environ. Eng., 68–78.
Hajjar, D., and AbouRizk, S. (1999). “Simphony: An environment for building special purpose construction simulation tools.” Proc., 31st Conf., Winter Simulation: Simulation—A Bridge to the Future, Vol. 2, ACM, New York, 998–1006.
Halpin, D. W., and Riggs, L. S. (1992). Planning and analysis of construction operations, Wiley, Hoboken, NJ.
Holický, M. (1999). “Fuzzy probabilistic optimisation of building performance.” Autom. Constr., 8(4), 437–443.
Janssen, J. A. E. B., Krol, M. S., Schielen, R. M. J., Hoekstra, A. Y., and de Kok, J. L. (2010). “Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models.” Ecol. Modell., 221(9), 1245–1251.
Karumanasseri, G., and AbouRizk, S. (2002). “Decision support system for scheduling steel fabrication projects.” J. Constr. Eng. Manage., 392–399.
Kim, K. J. and Gibson, G. E., Jr. (2003). “Interactive simulation modeling for heavy construction operations.” Autom. Constr., 12(1), 97–109.
Klir, G. J., and Yuan, B. (1995). Fuzzy sets and fuzzy logic, Prentice Hall, Upper Saddle River, NJ.
Lee, D., Yi, C., Lim, T., and Arditi, D. (2010). “Integrated simulation system for construction operation and project scheduling.” J. Comput. Civ. Eng., 557–569.
Liou, T., and Wang, M. (1992). “Ranking fuzzy numbers with integral value.” Fuzzy Sets Syst., 50(3), 247–255.
Liu, L. Y. (1991). “COOPS: Construction object-oriented simulation system.” Ph.D. dissertation, Univ. of Michigan, Ann Arbor, MI.
Lorterapong, P., and Moselhi, O. (1996). “Project-network analysis using fuzzy sets theory.” J. Constr. Eng. Manage., 308–318.
Lu, M., Lam, H. C., and Dai, F. (2008). “Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization.” Autom. Constr., 17(6), 670–681.
Martinez, J. C. (1996). “Stroboscope: State and resource based simulation of construction processes.” Ph.D. dissertation, Univ. of Michigan, Ann Arbor, MI.
Martinez, J. C. and Ioannou, P. G. (1999). “General-purpose systems for effective construction simulation.” J. Constr. Eng. Manage., 265–276.
Mauris, G., Lasserre, V., and Foulloy, L. (2001). “A fuzzy approach for the expression of uncertainty in measurement.” Measurement, 29(3), 165–177.
Mehdi, R., Araar, A., and Khali, H. (2005). “New fuzzy ranking algorithm for discrete event simulation.” 12th IEEE Int. Conf., Electronics, Circuits and Systems, IEEE, New York, 1–4.
Nguyen, Q., and Le, T. (1997). “A fuzzy discrete-event simulation model.” Proc., Intelligent Processing and Manufacturing of Materials, Australia-Pacific Forum, Gold Coast, QLD, Australia.
Oloufa, A. A. (1993). “Modeling and simulation of construction operations.” Autom. Constr., 1(4), 351–359.
Pedrycz, W., and Gomide, F. (2007). Fuzzy systems engineering: Toward human-centric computing, Wiley, Hoboken, NJ.
Perrone, G., Zinno, A., and La Diega, N. (2001). “Fuzzy discrete event simulation: A new tool for rapid analysis of production systems under vague information.” J. Intell. Manuf., 12(3), 309–326.
PMI (Project Management Institute). (2013). A guide to the project management body of knowledge (PMBOK guide), Newtown Square, PA.
Portas, J., and AbouRizk, S. (1997). “Neural network model for estimating construction productivity.” J. Constr. Eng. Manage., 399–410.
Prade, H. (1979). “Using fuzzy set theory in a scheduling problem: A case study.” Fuzzy Sets Syst., 2(2), 153–165.
Prodanovic, P., and Simonovic, S. P. (2002). “Comparison of fuzzy set ranking methods for implementation in water resources decision-making.” Can. J. Civ. Eng., 29(5), 692–701.
Sadeghi, N., Fayek, A. R., and Mosayebi, S. (2013). “Developing a fuzzy discrete event simulation framework within a traditional simulation engine.” IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), IEEE, New York, 1102–1106.
Shaheen, A. A., Fayek, A. R., and AbouRizk, S. M. (2009). “Methodology for integrating fuzzy expert systems and discrete event simulation in construction engineering.” Can. J. Civ. Eng., 36(9), 1478–1490.
Song, L., and AbouRizk, S. M. (2006). “Virtual shop model for experimental planning of steel fabrication projects.” J. Comput. Civ. Eng., 308–316.
Tran, L., and Duckstein, L. (2002). “Comparison of fuzzy numbers using a fuzzy distance measure.” Fuzzy Sets Syst., 130(3), 331–341.
Wang, Y. M., Yang, J. B., Xu, D. L., and Chin, K. S. (2006). “On the centroids of fuzzy numbers.” Fuzzy Sets Syst., 157(7), 919–926.
Yager, R. R. (1980). “On a general class of fuzzy connectives.” Fuzzy Sets Syst., 4(3), 235–242.
Zadeh, L. A. (1965). “Fuzzy sets.” Inf. Control, 8(3), 338–353.
Zadeh, L. A. (2002). “Toward a perception-based theory of probabilistic reasoning.” Rough sets and current trends in computing, L. Polkowski and A. Skowron, eds., Springer, Berlin, 46–48.
Zhang, H., Tam, C. M., and Li, H. (2005). “Modeling uncertain activity duration by fuzzy number and discrete-event simulation.” Eur. J. Oper. Res., 164(3), 715–729.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 142 • Issue 12 • December 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 20, 2015
Accepted: Apr 21, 2016
Published online: Jun 20, 2016
Discussion open until: Nov 20, 2016
Published in print: Dec 1, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.