Modeling A Multi-Criteria Decision Support System for Prequalification Assessment of Construction Contractors Using CRITIC and EDAS Models

  • M. Gopal Naik Department of Civil Engineering, UCE, Osmania University, Telangana State, India
  • Ravande Kishore Department of Civil Engineering, UCE, Osmania University, Telangana State, India
  • Seyed Ali Mousavi Dehmourdi Department of Civil Engineering, UCE, Osmania University, Telangana State, India
Keywords: Support system, Contractor Prequalification, MCDM, CRITIC, EDAS


Contractor prequalification assessment in the construction industry is an essential part of the project development process because contractors play a pivotal role in the extension of projects and resources. The main objective of the present study is comprised prequalification assessment for classifying contractors by applied the EDAS method for recognizing the contractors' potential before competitive tendering and obtaining bids. First, an inclusive, detailed list of 56 sub-factors under 5 main factors for project prequalification was compiled following a thorough literature review, and review of contractors by experts of Bandar Imam Khomeini municipality who already have done projects with contractors. Second, used the CRITIC method for obtained the weighing and importance of each factor. Third, classified the contractors by applied the EDAS system for recognizing the contractors' potential before competitive tendering and obtaining bids. Finally, the prequalification assessment process was developed to obtaining the rank of each contractor and help the stakeholders to select the right contractors. The effectiveness of the present approach was tested by applying it to a case study of the prequalification assessment of four construction companies' in Bandar Imam Khomeini municipality, Khuzestan, Iran. It is worth mentioning that the prequalification assessment by the proposed approach is approved by the project stakeholders and is consistent with their expectations. It can be concluded that based on relevant ranking and weighing of companies that procedure can be extended to the same studies in this regard, and the contribution of the present study is to propose a support system for prequalification and identification of contractors' ability, before assigning projects to companies for success in projects.


Acheamfour, V. K., Kissi, E., & Adjei-Kumi, T. (2019). Ascertaining the impact of contractors pre-qualification criteria on project success criteria. Engineering, Construction and Architectural Management.

Adalı, E. A., & Işık, A. T. (2017). CRITIC and MAUT methods for the contract manufacturer selection problem. European Journal of Multidisciplinary Studies, 2(5), 93–101.

Adedokun, O. A. (2020). Appraising the criteria for contractors’prequalification on selected public tertiary educational building projects in southwestern nigeria. Journal of Building Performance ISSN, 11(1), 2020.

Afshar, M. R., Alipouri, Y., Sebt, M. H., & Chan, W. T. (2017). A type-2 fuzzy set model for contractor prequalification. Automation in Construction, 84, 356–366.

Attar, A. M., Khanzadi, M., Dabirian, S., & Kalhor, E. (2013). Forecasting contractor’s deviation from the client objectives in prequalification model using support vector regression. International Journal of Project Management, 31(6), 924–936.

Awad, A., & Fayek, A. R. (2012). A decision support system for contractor prequalification for surety bonding. Automation in Construction, 21, 89–98.

Banaitiene, N., & Banaitis, A. (2006). Analysis of criteria for contractors’ qualification evaluation. Technological and Economic Development of Economy, 12(4), 276–282.

Chen, Z.-S., Zhang, X., Rodríguez, R. M., Pedrycz, W., & Martínez, L. (2021). Expertise-based bid evaluation for construction-contractor selection with generalized comparative linguistic ELECTRE III. Automation in Construction, 125, 103578.

Dehmourdi, S. A. M., Naik, M. G., & Kishore, R. (2021). An Investigation on Construction Crisis Framework Based on the CRITIC and WASPAS Methods, a Case Study; Khuzestan province (Iran). International Journal of Engineering and Advanced Technology (IJEAT), 10(4), 89–100.

Doloi, H. (2009). Analysis of pre‐qualification criteria in contractor selection and their impacts on project success. Construction Management and Economics, 27(12), 1245–1263.

Duarte, B. M., & Sousa, S. D. (2020). Supplier pre-qualification method for the Portuguese construction industry. Procedia Manufacturing, 51, 1703–1708.

El-Sawalhi, N., Eaton, D., & Rustom, R. (2007). Contractor pre-qualification model: State-of-the-art. International Journal of Project Management, 25(5), 465–474.

Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2018). A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Archives of Civil and Mechanical Engineering, 18, 32–49.

Jafari, A. (2013). A contractor pre-qualification model based on the quality function deployment method. Construction Management and Economics, 31(7), 746–760.

Jaskowski, P., Biruk, S., & Bucon, R. (2010). Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment. Automation in Construction, 19(2), 120–126.

Kahraman, C., Keshavarz Ghorabaee, M., Zavadskas, E. K., Cevik Onar, S., Yazdani, M., & Oztaysi, B. (2017). Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection. Journal of Environmental Engineering and Landscape Management, 25(1), 1–12.

Kazan, H., & Ozdemir, O. (2014). Financial performance assessment of large scale conglomerates via TOPSIS and CRITIC methods. International Journal of Management and Sustainability, 3(4), 203–224.

Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435–451.

Khoso, A. R., Memon, N. A., Sohu, S., Siddiqui, F., & Khan, J. S. (2020). Decision Criteria For Assessment Of Contractors In Prequalification Phase Of Public Projects. Int. J. Adv. Sci. Technol, 29, 2624–2635.

Khosrowshahi, F. (1999). Neural network model for contractors’ prequalification for local authority projects. Engineering, Construction and Architectural Management.

Kishore, R., Dehmourdi, S. A. M., Naik, M. G., & Hassanpour, M. (2020). Designing a framework for Subcontractor’s selection in construction projects using MCDM model. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 48–64.

Korytárová, J., Hanák, T., Kozik, R., & Radziszewska–Zielina, E. (2015). Exploring the contractors’ qualification process in public works contracts. Procedia Engineering, 123, 276–283.

Kukoyi, P. O., Osuizugbo, I. C., Yohanna, H. S., Edike, U. E., & Ohiseghame, I. E. (2021). Pre-Qualification of Selecting Construction Project Contractors Using Health and Safety Criteria. Journal of Engineering, Project, and Production Management, 11(1), 30–36.

Lam, K C, Ng, S. T., Tiesong, H., Skitmore, M., & Cheung, S. O. (2000). Decision support system for contractor pre‐qualification—artificial neural network model. Engineering Construction and Architectural Management, 7(3), 251–266.

Lam, Ka Chi, & Yu, C. Y. (2011). A multiple kernel learning-based decision support model for contractor pre-qualification. Automation in Construction, 20(5), 531–536.

Landy, M. F. B., Sousa, S., & Romero, F. (2020). Service quality factors in the construction sector: A literature review. IOP Conference Series: Materials Science and Engineering, 800(1), 12035.

Liang, W.-Z., Zhao, G.-Y., & Luo, S.-Z. (2018). An integrated EDAS-ELECTRE method with picture fuzzy information for cleaner production evaluation in gold mines. Ieee Access, 6, 65747–65759.

Maheshwari, N., Choudhary, J., Rath, A., Shinde, D., & Kalita, K. (2021). Finite Element Analysis and Multi-criteria Decision-Making (MCDM)-Based Optimal Design Parameter Selection of Solid Ventilated Brake Disc. Journal of The Institution of Engineers (India): Series C, 1–11.

Marović, I., Perić, M., & Hanak, T. (2021). A Multi-Criteria Decision Support Concept for Selecting the Optimal Contractor. Applied Sciences, 11(4), 1660.

Mat Isa, C. M., Saman, H. M., & Preece, C. (2015). Determining significant factors influencing Malaysian construction business performance in international markets.

Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237–253.

Nassar, K., & Hosny, O. (2013). Fuzzy clustering validity for contractor performance evaluation: Application to UAE contractors. Automation in Construction, 31, 158–168.

Ng, S. T. (2001). EQUAL: a case-based contractor prequalifier. Automation in Construction, 10(4), 443–457.

Nieto-Morote, A., & Ruz-Vila, F. (2012). A fuzzy multi-criteria decision-making model for construction contractor prequalification. Automation in Construction, 25, 8–19.

Okifitriana, M., & Latief, Y. (2021). Development of Quality Management System for Construction Services Procurement to Improve the Quality of Contractor Performance in Universitas Indonesia. Journal of Physics: Conference Series, 1858(1), 12083.

Patil, S., Konnur, B., Devthanekar, P., & Patil, K. (2020). Review of Contractor Prequalification Criteria and their Impact on Project Success Factors. International Journal of Research in Engineering, Science and Management, 3(7), 298–302.

Polat, G., & Bayhan, H. G. (2020). Selection of HVAC-AHU system supplier with environmental considerations using Fuzzy EDAS method. International Journal of Construction Management, 1–9.

Poloie, K., Fazli, S., Alvandi, M., & Hasanlo, S. (2012). A framework for measuring the supply chain’s agility of mass construction industry in Iran. Management Science Letters, 2(7), 2317–2334.

Prasetia, F. T., & Imaroh, T. S. (2020). Contractor selection assessment strategy in the upstream oil and gas industry towards green supply chain management. Dinasti International Journal of Economics, Finance & Accounting, 1(3), 373–383.

Rashvand, P., Abd Majid, M. Z., & Pinto, J. K. (2015). Contractor management performance evaluation model at prequalification stage. Expert Systems with Applications, 42(12), 5087–5101.

Russell, J. S., & Skibniewski, M. J. (1988). Decision criteria in contractor prequalification. Journal of Management in Engineering, 4(2), 148–164.

Sacks, R., & Harel, M. (2006). An economic game theory model of subcontractor resource allocation behaviour. Construction Management and Economics, 24(8), 869–881.

Sönmez, M., Holt, G. D., Yang, J. B., & Graham, G. (2002). Applying evidential reasoning to prequalifying construction contractors. Journal of Management in Engineering, 18(3), 111–119.

Stević, Ž., Vasiljević, M., Zavadskas, E. K., Sremac, S., & Turskis, Z. (2018). Selection of carpenter manufacturer using fuzzy EDAS method. Engineering Economics, 29(3), 281–290.

Topcu, Y. I. (2004). A decision model proposal for construction contractor selection in Turkey. Building and Environment, 39(4), 469–481.

Xu, B., Jiang, Q., & Sun, W. (2020). The Impacts of Standards on the Economic Growth in Construction Industry with the Example of China. 4th International Symposium on Business Corporation and Development in South-East and South Asia under B&R Initiative (ISBCD 2019), 158–162.

Yap, J. B. H., Chow, I. N., & Shavarebi, K. (2019). Criticality of construction industry problems in developing countries: Analyzing Malaysian projects. Journal of Management in Engineering, 35(5), 4019020.

Zavadskas, E. K., Stevic, R., Turskis, Z., & Tomaševic, M. (2019). A novel extended EDAS in Minkowski Space (EDAS-M) method for evaluating autonomous vehicles. Studies in Informatics and Control, 28(3), 255–264.

Žižović, M., Miljković, B., & Marinković, D. (2020). Objective methods for determining criteria weight coefficients: A modification of the CRITIC method. Decision Making: Applications in Management and Engineering, 3(2), 149–161.

How to Cite
Naik, M. G., Kishore, R., & Mousavi Dehmourdi, S. A. (2021). Modeling A Multi-Criteria Decision Support System for Prequalification Assessment of Construction Contractors Using CRITIC and EDAS Models. Operational Research in Engineering Sciences: Theory and Applications, 4(2), 79-101.