Fuzzy Monte Carlo Agent-Based Simulation of Construction Crew Performance
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 5
Abstract
The use of agent-based modeling (ABM) in the analysis of construction processes and practices has increased significantly over the last decade. However, the developed models are not able to address both random and subjective uncertainties that exist in many construction processes and practices. Monte Carlo simulation is able to account for random uncertainty, and fuzzy logic is able to account for the subjective uncertainty that exists in model variables and relationships. In this paper, a methodology for the development of fuzzy Monte Carlo agent-based models in construction is provided, and its application is illustrated through the development of a model of construction crew performance. This paper makes three contributions: first, it expands ABM’s scope of applicability by showing how to model both random and subjective uncertainty in ABM; second, it provides a novel methodology for integrating fuzzy logic and Monte Carlo simulation in ABM, which allows for the development of fuzzy Monte Carlo agent-based models in construction; and third, it illustrates a fuzzy Monte Carlo agent-based simulation of construction crew performance, which improves the assessment of crew performance by considering both random and subjective uncertainties in model variables.
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Data Availability Statement
All data generated or analyzed during the study are included in the published paper. Information about the journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This research is funded by the Natural Sciences and Engineering Research Council of Canada Industrial Research Chair in Strategic Construction Modeling and Delivery (NSERC IRCPJ 428226–15), which is held by Dr. Aminah Robinson Fayek. The authors gratefully acknowledge the support and data provided by industry partners and all personnel who participated in this study.
References
AbouRizk, S. 2010. “Role of simulation in construction engineering and management.” J. Constr. Eng. Manage. 136 (10): 1140–1153. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000220.
Ahn, S., and S. Lee. 2014. “Methodology for creating empirically supported agent-based simulation with survey data for studying group behavior of construction workers.” J. Constr. Eng. Manage. 141 (1): 04014065. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000918.
Anumba, C., O. Ugwu, L. Newnham, and A. Thorpe. 2002. “Collaborative design of structures using intelligent agents.” Autom. Constr. 11 (1): 89–103. https://doi.org/10.1016/S0926-5805(01)00055-3.
Asgari, S., A. Kandil, and I. Odeh. 2016. “Optimal risk attitude for construction contractors in competitive bidding environments.” In Proc., Construction Research Congress 2016, 2474–2480. Puerto Rico, Mayagüez: Construction Engineering and Management Area. https://doi.org/10.1061/9780784479827.246.
Ashforth, B. E., and F. Mael. 1989. “Social identity theory and the organization.” Acad. Manage. Rev. 14 (1): 20–39. https://doi.org/10.5465/amr.1989.4278999.
Awwad, R., C. A. Shdid, and R. Tayeh. 2017. “Agent-based model for simulating construction safety climate in a market environment.” J. Comput. Civ. Eng. 31 (1): 05016003. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000612.
Azar, E., and H. Al Ansari. 2017. “Multilayer agent-based modeling and social network framework to evaluate energy feedback methods for groups of buildings.” J. Comput. Civ. Eng. 31 (4): 04017007. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000651.
Bandura, A. 1977. “Self-efficacy: Toward a unifying theory of behavioral change.” Psychol. Rev. 84 (2): 191. https://doi.org/10.1037/0033-295X.84.2.191.
Beal, D. J., R. R. Cohen, M. J. Burke, and C. L. McLendon. 2003. “Cohesion and performance in groups: A meta-analytic clarification of construct relations.” J. Appl. Psychol. 88 (6): 989. https://doi.org/10.1037/0021-9010.88.6.989.
Ben-Alon, L., and R. Sacks. 2017. “Simulating the behavior of trade crews in construction using agents and building information modeling.” Autom. Constr. 74 (Feb): 12–27. https://doi.org/10.1016/j.autcon.2016.11.002.
Bezdek, J. C. 2013. Pattern recognition with fuzzy objective function algorithms. New York: Springer.
Cesário, F., and M. J. Chambel. 2017. “Linking organizational commitment and work engagement to employee performance.” Knowl. Process Manage. 24 (2): 152–158. https://doi.org/10.1002/kpm.1542.
Chiniara, M., and K. Bentein. 2017. “The servant leadership advantage: When perceiving low differentiation in leader-member relationship quality influences team cohesion, team task performance and service OCB.” Leadership Q. 29 (2): 333–345. https://doi.org/10.1016/j.leaqua.2017.05.002.
Choi, B., and S. Lee. 2017. “An empirically based agent-based model of the sociocognitive process of construction workers’ safety behavior.” J. Constr. Eng. Manage. 144 (2): 04017102. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001421.
Dubois, D., L. Foulloy, G. Mauris, and H. Prade. 2004. “Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities.” Reliab. Comput. 10 (4): 273–297. https://doi.org/10.1023/B:REOM.0000032115.22510.b5.
Eid, M. S., and I. H. El-adaway. 2017. “Integrating the social vulnerability of host communities and the objective functions of associated stakeholders during disaster recovery processes using agent-based modeling.” J. Comput. Civ. Eng. 31 (5): 04017030. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000680.
El-Adaway, I. H., and A. A. Kandil. 2010. “Multiagent system for construction dispute resolution (MAS-COR).” J. Constr. Eng. Manage. 136 (3): 303–315. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000144.
Farshchian, M. M., and G. Heravi. 2018. “Probabilistic assessment of cost, time, and revenue in a portfolio of projects using stochastic agent-based simulation.” J. Constr. Eng. Manage. 144 (5): 04018028. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001476.
Fayek, A. R., and R. Lourenzutti. 2018. “Introduction to fuzzy logic in construction engineering and management.” In Fuzzy hybrid computing in construction engineering and management: Theory and applications, 3–35. Bingley, UK: Emerald Publishing.
Gerami Seresht, N., R. Lourenzutti, A. Salah, and A. R. Fayek. 2018. “Overview of fuzzy hybrid techniques in construction engineering and management.” In Fuzzy hybrid computing in construction engineering and management: Theory and applications, 37–107. Bingley, UK: Emerald Publishing.
Hannah, S. T., J. M. Schaubroeck, and A. C. Peng. 2016. “Transforming followers’ value internalization and role self-efficacy: Dual processes promoting performance and peer norm-enforcement.” J. Appl. Psychol. 101 (2): 252. https://doi.org/10.1037/apl0000038.
Jabri, A., and T. Zayed. 2017. “Agent-based modeling and simulation of earthmoving operations.” Autom. Constr. 81 (Sep): 210–223. https://doi.org/10.1016/j.autcon.2017.06.017.
Jung, M., M. Park, H. Lee, and S. Chi. 2017. “Agent-based lift system simulation model for high-rise building construction projects.” J. Comput. Civ. Eng. 31 (6): 04017064. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000707.
Jung, M., M. Park, H. Lee, and S. Chi. 2018. “Multimethod supply chain simulation model for high-rise building construction projects.” J. Comput. Civ. Eng. 32 (3): 04018007. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000751.
Lin, C., H. He, Y. Baruch, and B. E. Ashforth. 2016. “The effect of team affective tone on team performance: The roles of team identification and team cooperation.” Hum. Resour. Manage. 56 (6): 931–952.
Meyer, J. P., and N. J. Allen. 1991. “A three-component conceptualization of organizational commitment.” Hum. Resour. Manage. Rev. 1 (1): 61–89. https://doi.org/10.1016/1053-4822(91)90011-Z.
North, M. J., and C. M. Macal. 2007. Managing business complexity: Discovering strategic solutions with agent-based modeling and simulation. Oxford, UK: Oxford University Press.
Osman, H. 2012. “Agent-based simulation of urban infrastructure asset management activities.” Autom. Constr. 28 (Dec): 45–57. https://doi.org/10.1016/j.autcon.2012.06.004.
Raoufi, M., and A. R. Fayek. 2018a. “Framework for identification of factors affecting construction crew motivation and performance.” J. Constr. Eng. Manage. 144 (9): 04018080. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001543.
Raoufi, M., and A. R. Fayek. 2018b. “Fuzzy agent-based modeling of construction crew motivation and performance.” J. Comput. Civ. Eng. 32 (5): 04018035. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000777.
Raoufi, M., and A. R. Fayek. 2018c. “Key moderators of the relationship between construction crew motivation and performance.” J. Constr. Eng. Manage. 144 (6): 04018047. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001509.
Raoufi, M., N. Gerami Seresht, and A. R. Fayek. 2016. “Overview of fuzzy simulation techniques in construction engineering and management.” In Proc., 2016 Annual Conf. of the North American, Fuzzy Information Processing Society (NAFIPS), 1–6. New York: IEEE.
Raoufi, M., N. Gerami Seresht, N. B. Siraj, and A. R. Fayek. 2018. “Fuzzy simulation techniques in construction engineering and management.” In Fuzzy hybrid computing in construction engineering and management: Theory and applications, 149–178. Bingley, UK: Emerald Publishing.
Ren, Z., C. J. Anumba, and O. O. Ugwu. 2003. “Multiagent system for construction claims negotiation.” J. Comput. Civ. Eng. 17 (3): 180–188. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:3(180).
Sadeghi, N., A. R. Fayek, and W. Pedrycz. 2010. “Fuzzy Monte Carlo simulation and risk assessment in construction.” Comput-Aided Civ. Inf. 25 (4): 238–252. https://doi.org/10.1111/j.1467-8667.2009.00632.x.
Sargent, R. G. 2013. “Verification and validation of simulation models.” J. Simul. 7 (1): 12–24. https://doi.org/10.1057/jos.2012.20.
Seo, J., S. Lee, and J. Seo. 2016. “Simulation-based assessment of workers’ muscle fatigue and its impact on construction operations.” J. Constr. Eng. Manage. 142 (11): 04016063. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001182.
Thomopoulos, N. T. 2013. Essentials of Monte Carlo simulation: Statistical methods for building simulation models, 1–90. New York: Springer.
Watkins, M., A. Mukherjee, N. Onder, and K. Mattila. 2009. “Using agent-based modeling to study construction labor productivity as an emergent property of individual and crew interactions.” J. Constr. Eng. Manage. 135 (7): 657–667. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000022.
Wonneberger, S., S. Kistinger, and A. Deckert. 1995. Unbiased guess, a concept to cope with fuzzy and random parameters?. Luxembourg: Office for Official Publications of the European Communities.
Zadeh, L. A. 2015. “Fuzzy logic—A personal perspective.” Fuzzy Sets Syst. 281 (Dec): 4–20. https://doi.org/10.1016/j.fss.2015.05.009.
Information & Authors
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Published In
Journal of Construction Engineering and Management
Volume 146 • Issue 5 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Jul 26, 2019
Accepted: Oct 30, 2019
Published online: Mar 2, 2020
Published in print: May 1, 2020
Discussion open until: Aug 2, 2020
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