Abstract
Purpose:
This study introduces a novel fuzzy multi-attribute analysis (FMAA) model to enhance decision-making in engineering-procurement-construction (EPC) contractor selection by effectively addressing the complexities and uncertainties inherent in these processes.
Design/methodology/approach:
The FMAA model was developed through a comprehensive methodology comprising an extensive literature review to identify challenges and criteria in contractor selection, expert consultations to refine the model framework, and validation via in-depth interviews with experienced EPC project managers. Key steps include defining selection criteria, determining appropriate weights, and employing fuzzy set theory to manage uncertainties.
Findings:
Compared to traditional methods, the FMAA model offers a structured and scalable approach to contractor selection, effectively reducing uncertainty and enhancing decision-making accuracy. Validation through expert interviews confirmed its practical relevance and adaptability across diverse EPC project contexts.
Research limitations/implications:
While the model provides valuable insights, further research utilizing quantitative validation methods and case studies is essential to assess its applicability across various construction sectors and geographical regions.
Practical implications:
By adopting the FMAA model, project managers can systematically evaluate contractors, ensuring the selection of candidates best suited to achieve long-term project success and sustainability.
Social implications:
Structured decision-making frameworks like the FMAA model promote improved project outcomes and sustainable practices, contributing to enhanced performance within the construction industry.
Originality/value:
This study advances contractor selection methodologies by integrating contemporary decision-making approaches with empirical validation, addressing critical gaps in managing the complexity and uncertainty inherent in EPC projects.
This study introduces a novel fuzzy multi-attribute analysis (FMAA) model to enhance decision-making in engineering-procurement-construction (EPC) contractor selection by effectively addressing the complexities and uncertainties inherent in these processes.
Design/methodology/approach:
The FMAA model was developed through a comprehensive methodology comprising an extensive literature review to identify challenges and criteria in contractor selection, expert consultations to refine the model framework, and validation via in-depth interviews with experienced EPC project managers. Key steps include defining selection criteria, determining appropriate weights, and employing fuzzy set theory to manage uncertainties.
Findings:
Compared to traditional methods, the FMAA model offers a structured and scalable approach to contractor selection, effectively reducing uncertainty and enhancing decision-making accuracy. Validation through expert interviews confirmed its practical relevance and adaptability across diverse EPC project contexts.
Research limitations/implications:
While the model provides valuable insights, further research utilizing quantitative validation methods and case studies is essential to assess its applicability across various construction sectors and geographical regions.
Practical implications:
By adopting the FMAA model, project managers can systematically evaluate contractors, ensuring the selection of candidates best suited to achieve long-term project success and sustainability.
Social implications:
Structured decision-making frameworks like the FMAA model promote improved project outcomes and sustainable practices, contributing to enhanced performance within the construction industry.
Originality/value:
This study advances contractor selection methodologies by integrating contemporary decision-making approaches with empirical validation, addressing critical gaps in managing the complexity and uncertainty inherent in EPC projects.
| Original language | English |
|---|---|
| Pages (from-to) | 1-19 |
| Number of pages | 19 |
| Journal | Built Environment Project and Asset Management |
| Early online date | 27 May 2025 |
| DOIs | |
| Publication status | E-pub ahead of print - 27 May 2025 |