Improve subcontractor selection decision using fuzzy AHP and fuzzy TOPSIS methods

Nowadays, it is very easy to reach information and search engines, social media and data sources can be in front of us in seconds because of the development of technology. However, it is not as easy as it seems to reach correct and accurate data. Evaluation and analysis of data has great importance. The main key to success, especially in the construction industry, is to reach the right data on time during the selection of the contractor and subcontractor to work with. In general, companies with strong references, that have been worked with in the past, are preferred and the risk of selecting contractors is high because the data presented over the internet is unreliable and difficult to verify even if trusted. It is also important to choose the right data-access method needed for the evaluation of the contractor and subcontractor. Today, service providers have started to fill this gap in order to ensure that this data analysis is provided to the companies in the Construction industry accurately and on time. In this thesis, the decision-making process for the contractor selection problem in the construction sector has been improved. A decision-making committee consisting of 10 experts has been established. The selected experts are senior executives in the construction sector. To determine the importance of the criteria used in the evaluation of the potential contractors, Fuzzy AHP and Fuzzy TOPSIS methods are developed. The results of this thesis work can be incorporated in the search engine of popular websites such as the Insuppliers site that provide qualified data for the contractor selection. This thesis work facilitates choosing the contractor in the construction sector while using the opportunities provided by the internet.