Purpose
The purpose of this paper is to present a hybrid simulation approach for predicting the value of labor productivity taking account of various continuous influencing factors and the interactions between different agents involved in the project.
Design/methodology/approach
The various continuous factors affecting labor productivity are simulated using system dynamics (SD). The heterogeneity of different agents involved in the project and their interactions is accounted using agent-based modelling (ABM). The developed ABM and SD models are finally integrated to simulate the value of labor productivity taking account of all the influencing factors.
Findings
The proposed hybrid simulation tool is implemented in a real project to evaluate its perfomance. The value of labor productivity is simulated by taking account of all the influencing factors. The most appropriate execution strategy is then selected using the developed hybrid SD-ABM approach to improve productivity. It is shown that the number of working groups and their movement patterns affect the severity of the groups’ interferences which will in turn affect the value of labor productivity.
Practical implications
This research helps project managers to predict and improve the value of labor productivity taking account of all the influencing factors.
Originality/value
It is believed that the proposed hybrid SD-ABM simulation approach offers a novel and robust tool for modeling labor productivity because the effects of various continuous influencing factors and the interactions between different agents are taken into account through the combination of SD and ABM. Many complex problems faced in construction projects involve interacting elements of a different nature, and the integration of SD with ideas from ABM offers potential to combine the strengths of the two methodologies to solve the problem.
Physical fatigue is one of the most important and highly prevalent occupational hazards in different industries. This research adopts a new analytical framework to detect workers’ physical fatigue using heart rate measurements. First, desired features are extracted from the heart signals using different entropies and statistical measures. Then, a feature selection method is used to rank features according to their role in classification. Finally, using some of the frequently used classification algorithms, physical fatigue is detected. The experimental results show that the proposed method has excellent performance in recognizing the physical fatigue. The achieved accuracy, sensitivity, and specificity rates for fatigue detection are 90.36%, 82.26%, and 96.2%, respectively. The proposed method provides an efficient tool for accurate and real-time monitoring of physical fatigue and aids to enhance workers’ safety and prevent accidents. It can be useful to develop warning systems against high levels of physical fatigue and design better resting times to improve workers’ safety. This research ultimately aids to improve social sustainability through minimizing work accidents and injuries arising from fatigue.
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