A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fuzzy clustering validity for contractor performance evaluation: Application to UAE contractors
Abstract Several statistical algorithms are used to categorize contractors. The number of categories depends on the clustering algorithm used. This paper presents a framework for classifying contractors using five of the most common clustering algorithms and assesses their performance with appropriate validity measures. The framework was implemented on actual data for 14 contractors working in UAE using a database of 294 projects. Quantitative measures were suggested and calculated for the contractors in the database. Qualitative measures were determined using AHP. The quality of contractor's staff and equipment was deemed to be the most important measure. The results show that contractors are grouped into four categories based on the quantitative and qualitative measures identified. The Fuzzy-C means algorithm had the highest validity measures when applied to the studied data set. The results show that the proposed framework can be used to categorize contractors into different performance groups in a rational and unbiased way.
Highlights ► Framework for categorizing contractors using five clustering algorithms is introduced. ► Qualitative measures for assessing contractors' performance are identified. ► Different methods for categorizing contractors based on performance are introduced.
Fuzzy clustering validity for contractor performance evaluation: Application to UAE contractors
Abstract Several statistical algorithms are used to categorize contractors. The number of categories depends on the clustering algorithm used. This paper presents a framework for classifying contractors using five of the most common clustering algorithms and assesses their performance with appropriate validity measures. The framework was implemented on actual data for 14 contractors working in UAE using a database of 294 projects. Quantitative measures were suggested and calculated for the contractors in the database. Qualitative measures were determined using AHP. The quality of contractor's staff and equipment was deemed to be the most important measure. The results show that contractors are grouped into four categories based on the quantitative and qualitative measures identified. The Fuzzy-C means algorithm had the highest validity measures when applied to the studied data set. The results show that the proposed framework can be used to categorize contractors into different performance groups in a rational and unbiased way.
Highlights ► Framework for categorizing contractors using five clustering algorithms is introduced. ► Qualitative measures for assessing contractors' performance are identified. ► Different methods for categorizing contractors based on performance are introduced.
Fuzzy clustering validity for contractor performance evaluation: Application to UAE contractors
Nassar, Khaled (author) / Hosny, Ossama (author)
Automation in Construction ; 31 ; 158-168
2012-11-07
11 pages
Article (Journal)
Electronic Resource
English
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