Modeling the influence of multiskilled construction workers in the context of the covid-19 pandemic using an agent-based ap-proach

Authors

  • Felipe Araya Departamento de Obras Civiles, Universidad Técnica Federico Santa Maria, Valparaíso (Chile)

DOI:

https://doi.org/10.7764/RDLC.21.1.105

Keywords:

construction, COVID-19, multiskilled workers, agent-based modeling

Abstract

As the COVID-19 pandemic continues, construction projects have struggled to be completed. As such, it is necessary to find alternatives that optimize the limited human resources that can be working on construction sites. One alternative to do so is using multiskilled workers so workers can be reassigned to construction activities minimizing projects’ disruption due to workers getting contagion with COVID-19. This study simulates the influence of multiskilled workers in the development of a construction project in the context of the COVID-19 pandemic using an agent-based modeling approach. The aim of the study is to quantify the influence of multiskilled workers in the deficit of construction workers due to COVID-19. The proposed model generates six scenarios to include the uncertainty from limited data from the field due to the pandemic context to quantify the deficit of workers to develop a construction project. This study found that using multiskilled workers reduces the deficit of workers required to perform critical activities in construction projects. More specifically, it can reduce the average deficit of workers roughly in half when compared with the alternative of using only single-skilled workers, from 33.4% to 16.7% of deficit. Consequently, multiskilled workers represents an alternative for construction managers to deal with the disruption from COVID-19 in construction projects from a workforce management standpoint. Understanding alternatives to minimize the impacts of COVID-19 in construction projects may assist engineers and managers in applying strategies to develop construction projects accounting the limitations that COVID-19 places on construction sites. 

References

Ahn, S., Lee, S., & Steel, R. P. (2013). Effects of workers’ social learning: Focusing on absence behavior. Journal of Construction Engineer-ing and Management, 139(8), 1015-1025.

Aktar, M. A., Alam, M. M., & Al-Amin, A. Q. (2020). Global Economic Crisis, Energy Use, CO2 Emissions, and Policy Roadmap Amid COVID-19. Sustainable Production and Consumption.

Alsharef, A., Banerjee, S., Uddin, S. M., Albert, A., & Jaselskis, E. (2021). Early impacts of the COVID-19 pandemic on the United States construction industry. International journal of environmental research and public health, 18(4), 1559.

AnyLogic. (2020). AnyLogic Simulation Software <https://www.anylogic.com/> (Accessed October 21, 2020).

Ahmadian Fard Fini, A., Rashidi, T. H., Akbarnezhad, A., & Travis Waller, S. (2016). Incorporating multiskilling and learning in the optimiza-tion of crew composition. Journal of Construction Engineering and Management, 142(5), 04015106.

Araya, F. (2021a). Modeling working shifts in construction projects using an agent-based approach to minimize the spread of COVID-19. Journal of Building Engineering, 102413. https://doi.org/10.1016/j.jobe.2021.102413

Araya. F. (2021b). Modeling the Spread of COVID-19 on construction workers: an agent-based approach. Safety Science, Vol 133, January, 105022, https://doi.org/10.1016/j.ssci.2020.105022

Araya, F. (2020). Agent based modeling: a tool for construction engineering and management. Revista Ingeniería de Construcción, 35(2), 111-118. http://dx.doi.org/10.4067/S0718-50732020000200111

Araya, F., & Sierra, L. (2021). Influence between COVID-19 Impacts and Project Stakeholders in Chilean Construction Projects. Sustainabil-ity, 13(18), 10082. https://doi.org/10.3390/su131810082

Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences, 99(suppl 3), 7280-7287.

Carley, L. A., Haas, C. T., Borcherding, J. D., & Goodrum, P. M. (2003). Experiences with multiskilling among non‐union craft workers in US industrial construction projects. Engineering, Construction and Architectural Management.

Chan, A. P., Yi, W., Wong, D. P., Yam, M. C., & Chan, D. W. (2012). Determining an optimal recovery time for construction rebar workers after working to exhaustion in a hot and humid environment. Building and environment, 58, 163-171.

Chen, X., Guo, C., Song, J., Wang, X., & Cheng, J. (2019). Occupational health risk assessment based on actual dust exposure in a tunnel construction adopting roadheader in Chongqing, China. Building and Environment, 165, 106415.

Chun, C., Sung, K., Kim, E., & Park, J. (2010). Self-reported multiple chemical sensitivity symptoms and personal volatile organic com-pounds exposure concentrations in construction workers. Building and environment, 45(4), 901-906.

Currie CS, Fowler JW, Kotiadis K, Monks T, Onggo BS, Robertson DA, Tako AA. How simulation modelling can help reduce the impact of COVID-19. Journal of Simulation. 2020 Apr 2;14(2):83-97.

ENR. (2020a). COVID-19: AGC Says 75%of Contractors Had Project Canceled or Postponed Due to Coronavirus. <https://www.enr.com/articles/50527-agc-says-75-of-contractors-had-project-canceled-or-postponed-due-to-coronavirus > (Accessed, January 13, 2021).

ENR. (2020b). COVID-19: Confronting the New Normal. < https://www.enr.com/articles/49086-covid-19-confronting-the-new-normal > (Ac-cessed, October 19, 2020).

ENR. (2020c). COVID-19: Managing Human Assets Gets Harder in the COVID-19 Era. < https://www.enr.com/articles/49285-managing-human-assets-gets-harder-in-covid-19-era> (Accessed, January 13, 2021).

ENR. (2020d). McCormick Place to Become a COVID-19 Hospital; Chicago Veterans Home Fast-Tracked to Help. < https://www.enr.com/articles/49058-mccormick-place-to-become-a-covid-19-hospital-chicago-veterans-home-fast-tracked-to-help >. (Accessed October 19, 2020).

Gomar, J. E., Haas, C. T., & Morton, D. P. (2002). Assignment and allocation optimization of partially multiskilled workforce. Journal of construction Engineering and Management, 128(2), 103-109.

Haas, C. T., Rodriguez, A. M., Glover, R., & Goodrum, P. M. (2001). Implementing a multiskilled workforce. Construction Management & Economics, 19(6), 633-641.

Hegazy, T., Shabeeb, A. K., Elbeltagi, E., & Cheema, T. (2000). Algorithm for scheduling with multiskilled constrained resources. Journal of construction engineering and management, 126(6), 414-421.

Hendrickson, C., Hendrickson, C. T., & Au, T. (1989). Project management for construction: Fundamental concepts for owners, engineers, architects, and builders. Chris Hendrickson

Heimerl, C., & Kolisch, R. (2010). Scheduling and staffing multiple projects with a multi-skilled workforce. OR spectrum, 32(2), 343-368.

Huang, X., Lin, Y., Zhou, F., Lim, M. K., & Chen, S. (2021). Agent-based modelling for market acceptance of electric vehicles: evidence from China. Sustainable Production and Consumption.

Lee, S. C., Kim, J. H., & Hong, J. Y. (2019). Characterizing perceived aspects of adverse impact of noise on construction managers on con-struction sites. Building and Environment, 152, 17-27.

Lill, I. (2009). Multiskilling in construction–a strategy for stable employment. Technological and Economic Development of Economy, (4), 540-560.

Liu, S. S., & Wang, C. J. (2012). Optimizing linear project scheduling with multi-skilled crews. Automation in Construction, 24, 16-23.

Lobo, Y. B., & Wilkinson, S. (2008). New approaches to solving the skills shortages in the New Zealand construction industry. Engineering, Construction and Architectural Management. 15 (1): 42–53. https://doi.org/10.1108/09699980810842052

Macal, C. M., & North, M. J. (2005). Tutorial on agent-based modeling and simulation. In Proceedings of the Winter Simulation Conference, 2005. (pp. 14-pp). IEEE.

Mofijur, M., Fattah, I. R., Alam, M. A., Islam, A. S., Ong, H. C., Rahman, S. A., ... & Mahlia, T. M. I. (2020). Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic. Sustainable production and consumption.

Nasirian, A., Arashpour, M., & Abbasi, B. (2019a). Critical literature review of labor multiskilling in construction. Journal of construction engineering and management, 145(1), 04018113.

Nasirian, A., Arashpour, M., Abbasi, B., & Akbarnezhad, A. (2019b). Optimal work assignment to multiskilled resources in prefabricated construction. Journal of Construction Engineering and Management, 145(4), 04019011.

Osman, H. (2012). Agent-based simulation of urban infrastructure asset management activities. Automation in Construction, 28, 45-57.

Pham, H., & Kim, S. Y. (2019). The effects of sustainable practices and managers’ leadership competences on sustainability performance of construction firms. Sustainable Production and Consumption, 20, 1-14.

Ren, Z., & Anumba, C. J. (2004). Multi-agent systems in construction–state of the art and prospects. Automation in Construction, 13(3), 421-434.

Sacks, R., & Goldin, M. (2007). Lean management model for construction of high-rise apartment buildings. Journal of construction engineer-ing and Management, 133(5), 374-384.

Sargent, R. G. (2013). Verification and validation of simulation models. Journal of simulation, 7(1), 12-24.

Sargent, R. G. (2004). Validation and verification of simulation models. In Proceedings of the 2004 Winter Simulation Conference, 2004. (Vol. 1). IEEE.

Sarihi, M., Shahhosseini, V., & Banki, M. T. (2020). Multiskilled Project Management Workforce Assignment across Multiple Projects Re-garding Competency. Journal of Construction Engineering and Management, 146(12), 04020134.

Varotsos, C. A., & Krapivin, V. F. (2020). A new model for the spread of COVID-19 and the improvement of safety. Safety Science, 104962.

Wang, Y., Goodrum, P. M., Haas, C. T., & Glover, R. W. (2009). Analysis of observed skill affinity patterns and motivation for multiskilling among craft workers in the US industrial construction sector. Journal of construction engineering and management, 135(10), 999-1008.

Watkins, M., Mukherjee, A., Onder, N., & Mattila, K. (2009). Using agent-based modeling to study construction labor productivity as an emergent property of individual and crew interactions. Journal of construction engineering and management, 135(7), 657-667.

Weiss, F., Baloh, P., Pfaller, C., Cetintas, E. C., Kasper-Giebl, A., Wonaschütz, A., ... & Grothe, H. (2018). Reducing paving emissions and workers' exposure using novel mastic asphalt mixtures. Building and Environment, 137, 51-57.

Wongwai, N., & Malaikrisanachalee, S. (2011). Augmented heuristic algorithm for multi-skilled resource scheduling. Automation in Construc-tion, 20(4), 429-445.

Yi, W., Zhao, Y., Chan, A. P., & Lam, E. W. (2017). Optimal cooling intervention for construction workers in a hot and humid environment. Building and Environment, 118, 91-100.

Zhang, N., Cheng, P., Jia, W., Dung, C. H., Liu, L., Chen, W., ... & Li, Y. (2020). Impact of intervention methods on COVID-19 transmission in Shenzhen. Building and environment, 180, 107106.

Zhang, P., Li, N., Jiang, Z., Fang, D., & Anumba, C. J. (2019). An agent-based modeling approach for understanding the effect of worker-management interactions on construction workers' safety-related behaviors. Automation in construction, 97, 29-43.

Downloads

Published

2022-04-18

How to Cite

Araya , F. . (2022). Modeling the influence of multiskilled construction workers in the context of the covid-19 pandemic using an agent-based ap-proach. Revista De La Construcción. Journal of Construction, 21(1), 105–117. https://doi.org/10.7764/RDLC.21.1.105