Hybrid fuzzy Monte Carlo agent-based modeling of workforce motivation and performance in construction

This paper aims to cover the development of a methodology for hybrid fuzzy Monte Carlo agent-based simulation (FMCABS) and its implementation on a parametric study of construction crew performance. The developed methodology uses fuzzy logic, Monte Carlo simulation and agent-based modeling to simulate the behavior of construction crews and predict their performance. Both random and subjective uncertainties are considered in model variables. The developed methodology was implemented on a real case involving the parametric study of construction crew performance to assess its applicability and suitability for this context. This parametric study demonstrates a practical application for the hybrid FMCABS methodology. Though findings from this study are limited to the context of construction crew motivation and performance, the applicability of the developed methodology extends beyond the construction domain. Practical implications: This paper will help construction practitioners to predict and improve crew performance by taking into account both random and subjective uncertainties. Social implications: This paper will advance construction modeling by allowing for the assessment of social interactions among crews and their effects on crew performance. The developed hybrid FMCABS methodology represents an original contribution, as it allows agent-based models to simultaneously process all types of variables (i.e. deterministic, random and subjective) in the same simulation experiment while accounting for interactions among different agents. In addition, the developed methodology is implemented in a novel and extensive parametric study of construction crew performance.