A novel decision-making model for selecting a construction project delivery system
Abstract
It is crucial for the owner of a construction project to select an appropriate project delivery system (PDS) during early decision-making stages of the project. Due to project uncertainty or a lack of project information, the parameters of a PDS are difficult to measure and quantify. Therefore, there are still major challenges to the objective selection of PDSs. This research proposes a novel systematic decision-making model to select the appropriate PDS by using the combination of case-based reasoning (CBR) and robust nonparametric production frontier method. The Bayesian-Structural Equation Modeling (SEM) supported Z-order-m method is interpreted into the case retrieves process of traditional CBR method in order to eliminate the deteriorative internal and external influence for PDS selection. The case study was based on questionnaire survey conducted in China and used to test the validation of the proposed model. The findings reveal that the systematic decision-making model can overcome some problems of the traditional methods and improve the accuracy of PDS selection. As a result, this research has both theoretical and practical implications for the construction industry.
Keyword : data envelopment analysis, multi-criteria decision-making, construction project, project delivery system, nonparametric production frontier theory, case-based reasoning
How to Cite
Zhu, X., Meng, X., & Chen, Y. (2020). A novel decision-making model for selecting a construction project delivery system. Journal of Civil Engineering and Management, 26(7), 635-650. https://doi.org/10.3846/jcem.2020.12915
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis (3rd ed.). CRC Press. https://doi.org/10.1201/b16018
Guerrini, A., Romano, G., Mancuso, F., & Carosi, L. (2016). Identifying the performance drivers of wastewater treatment plants through conditional order-m efficiency analysis. Utilities Policy, 42, 20–31. https://doi.org/10.1016/j.jup.2016.08.001
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Khanzadi, M., Nasirzadeh, F., Hassani, S. M. H., & Mohtashemi, N. N. (2016). An integrated fuzzy multi-criteria group decision making approach for project delivery system selection. Scientia Iranica, 23(3), 802–814. https://doi.org/10.24200/sci.2016.2160
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