A Novel Rough-Fuzzy DEMATEL-TOPSIS Approach for Contractor Selection with a Sustainable Perspective

Document Type : Original Article

Authors

1 Master's degree, Department of Industrial Management, Faculty of Management, Kharazmi University, Tehran, Iran.

2 Assistant Professor, Department of Operations Management and Information Technology, Faculty of Management, Kharazmi University, Tehran, Iran.

10.48308/jimp.15.3.58

Abstract

Introduction: The selection of sustainable contractors is a critical challenge faced by executive managers and procurement personnel in today’s competitive world. Each year, numerous industrial and manufacturing projects are delegated to contractors, and improper contractor selection can lead to project failures. The importance of this issue is especially evident in supply chain management, where choosing the right contractor based on quality, price, and timing is of great significance. Due to its unique position in telecommunications product manufacturing, the company under study has a large number of contractors, particularly in the mechanical sector. Recognizing the need for a clear framework and method for evaluating and selecting mechanical contractors within the Electro-Optic Industries Company (SaIran), this research aims to address this gap. It is noteworthy that given the sensitivity of projects and the significant outsourcing of modular product construction to contractors, the wrong choice of mechanical contractor can have serious ramifications regarding delivery time management, manufacturing costs, product quality, and the company's reputation.
Methods: This research develops a new framework for evaluating and selecting mechanical contractors, considering sustainability criteria. In the first phase, the internal power of each sustainability criterion is determined using the fuzzy AHP method, followed by identifying the internal relationships between these criteria using the fuzzy DEMATEL approach. In the second phase, the combination of internal power and the external impacts of the criteria results in the formation of a fuzzy power-relation matrix and the calculation of criteria weights. Finally, the fuzzy TOPSIS method is used to rank the contractors.
Results and discussion: The proposed model, which utilizes an integrated rough-fuzzy approach, can simultaneously assess internal strengths and the interrelationships among criteria while providing the necessary flexibility to manage internal and external uncertainties. This research demonstrates significant results by evaluating the effectiveness and performance of the model through a real case study of sustainable mechanical contractor evaluation and comparing it with other methods.
The findings from the rough-fuzzy DEMATEL-TOPSIS method indicate that the five main criteria for selecting contractors include bid price, equipment, environmental management system, financial capability, and green and clean technology. Particularly in the current economic conditions and due to sanctions, the financial capability of contractors and the need for advanced equipment are of great importance. Given the economic conditions and sanctions, bid price should be considered the most crucial factor in contractor selection. The identified criteria reflect the positive impact of economic, environmental, and social sustainability on contractor selection, enabling managers to identify the best options and enhance their processes.
Conclusion: This research represents a significant advancement in sustainable contractor selection and introduces its innovative approach as an effective tool for managers. The provision of a fuzzy framework facilitates more accurate and effective decision-making in contractor selection and emphasizes the importance of focusing on sustainability in management processes. This model can serve as a comprehensive solution to address the challenges faced in contractor evaluation. Considering the combination of fuzzy sets and rough sets for the comprehensive management of internal and external uncertainties, the proposed rough-fuzzy method demonstrates a significantly different evaluation result compared to the other three methods. Therefore, the use of the rough-fuzzy method has a substantial impact on the final outcomes of the DEMATEL and TOPSIS methods.

Keywords

Main Subjects

References

  1. Alnsour, M., Zeidan, A., Al Quwaider, B., Alkubaisi, A., Alreqeb, R., & Bader, M. (2023). Developing sustainability assessment indicators for measuring contractor’s performance during the construction phase of construction projects in Jordan. Asian Journal of Civil Engineering, 24(1), 245-266.‏
  2. Amin-Tahmasbi, H., Tavakkoli-Moghaddam, R., & Najafi, S. E. (2016). IAHP/DEA Model for Contractor Assessment in Construction Industries in the Presence of Imprecise Data. Research in Production and Operations Management, 7(2), 199-216.‏ (In Persian).
  3. Arowosafe, O., Oduyemi, O., Ceranic, B., & Dean, A. (2018). Sustainable infrastructure delivery in Nigeria: implementation of the analytic network process for contractor selection. Sustainable Buildings.‏
  4. Chen, Z., Ming, X., Zhang, X., Yin, D., & Sun, Z. (2019). A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system. Journal of Cleaner Production, 228, 485-508.‏
  5. Çalık, A., Paksoy, T., & Huber, S. (2019). Lean and green supplier selection problem: a novel multi objective linear programming model for an electronics board manufacturing company in Turkey. Multiple Criteria Decision Making and Aiding: Cases on Models and Methods with Computer Implementations, 281-309.‏
  6. Chen, Z., & Ming, X. (2020). A rough–fuzzy approach integrating best–worst method and data envelopment analysis to multi-criteria selection of smart product service module. Applied Soft Computing, 94, 106479.‏
  7. Chen, Z., Ming, X., Zhou, T., & Chang, Y. (2020). Sustainable supplier selection for smart supply chain considering internal and external uncertainty: An integrated rough-fuzzy approach. Applied Soft Computing, 87, 106004.
  8. Dalalah, D., Hayajneh, M., & Batieha, F. (2011). A fuzzy multi-criteria decision making model for supplier selection. Expert systems with applications, 38(7), 8384-8391.‏
  9. El-Kholy, A. M. (2019). A new technique for subcontractor selection by adopting choosing by advantages. International Journal of Construction Management, 1-23.
  10. Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of Cleaner Production, 266, 121981.
  11. Hsu, Y. L., Lee, C. H., & Kreng, V. B. (2010). The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert systems with Applications, 37(1), 419-425.
  12. Hosseini Nasab, H., & Mirghani Ghamsarian, M. (2015). A fuzzy multiple-criteria decision-making model for contractor prequalification. Journal of Decision Systems, 24(4), 433-448.‏
  13. Hasnain, M., Ullah, F., Thaheem, M. J., & Sepasgozar, S. M. (2018). Prioritizing best value contributing factors for contractor selection: An AHP approach. In Proceedings of the 21st International Symposium on Advancement of Construction Management and Real Estate(pp. 1121-1131). Springer Singapore.‏
  14. Gu, J., Guo, F., Peng, X., & Wang, B. (2023). Green and sustainable construction industry: A systematic literature review of the contractor’s green construction capability. Buildings, 13(2), 470.‏
  15. Lai, Y. J., Liu, T. Y., & Hwang, C. L. (1994). Topsis for MODM. European journal of operational research, 76(3), 486-500.‏
  16. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2018). A dynamic fuzzy approach based on the EDAS method for multi-criteria subcontractor evaluation. Information, 9(3), 68.
  17. Koc, K., Ekmekcioglu, Ö., & Işık, Z. (2023). Developing a hybrid fuzzy decision-making model for sustainable circular contractor selection. Journal of Construction Engineering and Management, 149(10), 04023095.‏
  18. Leśniak, A., Kubek, D., Plebankiewicz, E., Zima, K., & Belniak, S. (2018). Fuzzy AHP application for supporting contractors’ bidding decision. Symmetry, 10(11), 642.
  19. Nazari, A., Parchami Jalal, M., Shahidi Nashroud Kola, S., & Hojat Panah, S. (2022). Analytical Review of the Construction Contractors' Challenges in the Contractual Claims. Journal of Industrial Management Perspective, 12(4), 199-221.‏ (In Persian).
  20. N‌a‌j‌i‌a‌z‌a‌r‌p‌o‌u‌r, S., & Teimouri, E. (2018). S‌U‌P‌P‌L‌Y C‌H‌A‌I‌N M‌A‌N‌A‌G‌E‌M‌E‌N‌T A‌N‌D U‌T‌I‌L‌I‌Z‌A‌T‌I‌O‌N O‌F P‌R‌O‌M‌T‌H‌E‌E. Sharif Journal of Industrial Engineering & Management, 34(1.1), 29-37.‏ (In Persian).
  21. Mirmousa, S., & Dehnavi, H. D. (2016). Development of criteria of selecting the supplier by using the fuzzy DEMATEL method. Procedia-Social and Behavioral Sciences, 230, 281-289.
  22. Mohammed, A., Setchi, R., Filip, M., Harris, I., & Li, X. (2018). An integrated methodology for a sustainable two-stage supplier selection and order allocation problem. Journal of Cleaner Production, 192, 99-114.‏
  23. Masmoudi, M., Alshamsi, M., Piya, S., & Gupta, S. (2025). Contractors allocation for public building maintenance: a sustainable approach aligned with SDGs using AHP‐TOPSIS and bi‐objective optimization. International Transactions in Operational Research.‏
  24. Prakash, C., & Barua, M. K. (2016). A robust multi-criteria decision-making framework for evaluation of the airport service quality enablers for ranking the airports. Journal of Quality Assurance in Hospitality & Tourism, 17(3), 351-370.
  25. Polat, G. (2016). Subcontractor selection using the integration of the AHP and PROMETHEE methods. Journal of Civil Engineering and Management, 22(8), 1042-1054.
  26. Pamučar, D., Petrović, I., & Ćirović, G. (2018). Modification of the Best–Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert systems with applications, 91, 89-106.‏
  27. Ranaei Koroshlooei, H., Alimohammadloo, M., Mirghaderi, S. H., & Amini, M. (2018). A framework for evaluating qualification and selecting contractor in the process of outsourcing the creation and maintenance of green space projects case study: Shiraz municipality. Journal of Iranian public administration studies, 1(1), 59-85.‏ (In Persian).
  28. Steger, C. (1996). On the calculation of moments of polygons. Reporte técnico, Technische Universität München.‏
  29. Senthil, S., Srirangacharyulu, B., & Ramesh, A. (2015). A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics. Expert Systems with Applications, 41(1), 50-58.
  30. Sarkis, J., Meade, L. M., & Presley, A. R. (2012). Incorporating sustainability into contractor evaluation and team formation in the built environment. Journal of Cleaner Production, 31, 40-53.
  31. Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert systems with applications, 37(12), 7745-7754.‏
  32. Saaty, T. L. (1989). Decision making, scaling, and number crunching. Decision sciences, 20(2), 404-409.‏
  33. Song, W., & Cao, J. (2017). A rough DEMATEL-based approach for evaluating interaction between requirements of product-service system. Computers & Industrial Engineering, 110, 353-363.‏
  34. Zhang, J. (2017). Evaluating regional low-carbon tourism strategies using the fuzzy Delphi-analytic network process approach. Journal of Cleaner Production, 141, 409-419.‏
Journal of Industrial Management Perspective
Volume 15, Issue 3 - Serial Number 59
September 2025
Pages 58-90

Files

Share

How to cite

Statistics