A decision making support tool for selecting green building certification credits based on project delivery attributes
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CitationSeyis, S., & Ergen, E. (2017). A decision making support tool for selecting green building certification credits based on project delivery attributes. Building and Environment, 126, 107-118. doi:10.1016/j.buildenv.2017.09.028
The Green Building (GB) certification process embodies detailed requirements and specifications that lead to additional tasks for project teams, which increases complexity levels of the entire project delivery process. Previous studies show that if the GB certification credits to be fulfilled are selected without considering project team attributes, then elevated levels of time, money, and labor could get wasted while attempting to meet the additional requirements of GB certification. The aim of this study is to develop a multi-attribute decision making (MADM) support tool to be used by GB experts to select the appropriate GB certification credits based on the project team attributes. The developed framework with relative weights assigned via the Delphi method was used to perform the MADM analysis, which employs the hybrid use of the Multi Attribute Utility Technique (MAUT) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This paper presents the developed MADM tool (i.e., GB-CS tool) and the relative weights of the attributes that were determined following expert opinions. To validate the tool, a case study was conducted at a LEED-registered residential project. The results show that the GB-CS Tool was successful in ranking the GB certification credits to be selected. This hybrid MADM tool can be used for preventing disruptions and bottlenecks in GB project delivery processes by assisting the owners/GB consultants in effectively selecting suitable GB certification credits based on the project team attributes. Thus, with the assistance of the GB-CS tool, root causes of waste can be mitigated in the GB project delivery process, decreasing associated hidden costs.