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Contractor Selection for Construction Projects Using Consensus Tools and Big Data

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Abstract

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.

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Acknowledgements

This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant No. (12-34-RG). The authors, therefore, acknowledge with thanks DSR technical and financial support. We would also like to acknowledge the FEDER funds under Grants TIN2013-40658-P and TIN2016-75850-R.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia

    Osman Taylan & Muhammed R. Kabli

  2. Department of Computer Science, University of Jaén, Jaén, Spain

    Carlos Porcel

  3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia

    Enrique Herrera-Viedma

  4. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Enrique Herrera-Viedma

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  1. Osman Taylan
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Correspondence to Osman Taylan.

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Taylan, O., Kabli, M.R., Porcel, C. et al. Contractor Selection for Construction Projects Using Consensus Tools and Big Data. Int. J. Fuzzy Syst. 20, 1267–1281 (2018). https://doi.org/10.1007/s40815-017-0312-3

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  • DOI: https://doi.org/10.1007/s40815-017-0312-3

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