LAUSR

Completing construction projects in time requires highly integrated contractor selection processes. Selecting the ‘best’ contractor is a multi-criteria and multi-group hard decision-making problem. The decision makers (DMs) usually do not have a joint interest in achieving agreement on choosing the best contractor. Traditionally, consensus on a decision does not mean a full and unanimous agreement on the selection criteria. Because the criteria expressed by quantitative and/or qualitative data are generally conflicting, an improvement in one often results in declining the others. Therefore, DMs base their judgments upon huge-size, high-variety and conflicting data which refer to Big Data. Hence, massive amount of data are analyzed in an iterative and time-sensitive manner for the crucial success of organizations. This study aims to integrate the contractor selection approaches for the formulation of decision problems using fuzzy and crisp data. Fuzzy AHP approach was employed for determining the criteria weights, and fuzzy TOPSIS method was used to find out the performance of contractors. Fuzzy extension of AHP enables the pair-wise comparison of criteria using synthetic global scores based on the data of a single expert. However, in this study, we used the data of multiple DMs and averaged the aggregated findings in the pair-wise comparison table; hence, seven contractors were evaluated based on the Big Data. The results showed that these methodologies are able to assess contractors’ Big Data in a more scientific and practical way. The suggested approach helped to select the best contractor or share the projects between equally strong contractors.