Framework to analyze construction labor productivity using fuzzy data clustering and multi-criteria decision-making
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Kazerooni, M., Raoufi, M., & Fayek, A. Robinson. (2020). Framework to analyze construction labor productivity using fuzzy data clustering and multi-criteria decision-making. Proceedings, ASCE Construction Research Congress, Tempe, AZ, March 8–10. 10 pp. https://doi.org/10.1061/9780784482865.006
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Construction labor productivity (CLP) has a significant impact on the performance and profitability of construction projects. A construction project can benefit from improved labor productivity in many ways, such as a shorter project life cycle and lower project cost. However, budget and resource restrictions force construction companies to select and implement only the most effective CLP improvement strategies. Analyzing labor productivity in order to determine the most effective CLP improvement strategies is a difficult task because labor productivity is influenced by numerous subjective and objective factors. This paper presents a framework for ranking the factors affecting CLP according to their importance for CLP improvement; the framework uses an integration of fuzzy data clustering and multi-criteria decision-making methods. The proposed framework entails asking experts to weight determinant criteria for selecting CLP improvement strategies and then clustering CLP factors and ranking the clusters. This paper’s major contribution is providing a systematic approach for analyzing and selecting CLP improvement strategies by identifying the CLP factors with the greatest impact on productivity improvement. The findings of this research will help establish a set of CLP improvement strategies in order to enhance CLP.
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http://purl.org/coar/resource_type/R60J-J5BD
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© ASCE This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/9780784482865.006
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en