Abstract— Productivity monitoring is a crucial process that considerably contributes in the success of earthmoving projects. Over decades, researchers have been focused on identification and assessment of the factors that lead to loss-in-productivity in earthmoving operations. However, considerably less work was focused on the effects of productivity variation on cost and schedule of earthmoving projects. This paper introduces an automated data collection that acquires data from various technological sources. The collected data facilitates the assessment of productivity ratio that assists in continuous monitoring of productivity variation in earthmoving projects. Also, this paper introduces a new fuzzy set-based monitoring system that investigates the effects of productivity variation on cost, schedule and depletion of resources in earthmoving projects based on set of qualitative and quantitative factors. The proposed monitoring system generates an early warning that allows for proactive decision making to avoid delays, overruns, and unnecessary depletion of resources. A case example is used to demonstrate the applicability of proposed method and its features in monitoring and evaluating the effects of productivity variation on cost, schedule and utilization of resources in earthmoving projects. Finally, results are discussed and conclusions are drawn highlighting the features of proposed method and recommendations for future work.
— Decision making, earthmoving project, fuzzy set theory, monitoring system, productivity variation, resources depletion.
The authors are with the Department of Building Civil and Environmental Engineering at Concordia University Montreal, Canada (Corresponding author: A. Salah; tel.: 514-662-9296; e-mail: firstname.lastname@example.org, email@example.com, Moselhi@encs.concordia.ca).
Cite: A. Salah, A. Salem, and O. Moselhi, " Automated Fuzzy Set-Based System for Monitoring the Effects of Productivity Variation on Earthmoving Projects," International Journal of Innovation, Management and Technology vol. 8, no. 2, pp. 85-89, 2017.