Navigating Uncertainty: Optimizing Contractor Selection for Megaprojects in Group Decision-Making with Multiple Criteria

Abstract

The process of selecting contractors for megaprojects involves inherent uncertainty in judgment and preferences, especially under multiple criteria group decision-making (MCGDM) scenarios. This uncertainty often stems from the ambiguity and hesitation when decision makers (DM) evaluate and prioritize different criteria, causing challenges for the evaluation of contractors’ technical capabilities. This study aims to enhance a generalized comparison table (GCT) by applying hesitant fuzzy soft sets to capture this uncertainty. Through the operation of a criteria adjustment algorithm for group consensus, experts-trust network, and the rule of maximum deviation, the GCT is improved by the ability to incorporate objective weights of subjective criteria. The improved GCT provides a transparent evaluation tool and has been tested on contractors’ assessments for a mega tunneling project in China. Its practicality is demonstrated by matching the method-assisted decision with the final choice of the project manager. The key research findings highlight the importance of incorporating project-specific criteria, the criteria adjustment algorithm, and the objective weights of criteria to refine MCGDM. The main contributions of this study include (1) a novel framework of the improved GCT for MCGDM, (2) a new criteria system that includes common categories and a project-specific criteria category, (3) a criteria adjustment algorithm to attain group consensus decisions, and (4) objective weights of criteria and DM based on the experts-trust network and maximum deviation model. Selecting the right contractor for megaprojects can be a daunting task filled with uncertainty. This uncertainty primarily arises from the ambiguity and hesitation when decision-makers evaluate various technical capabilities of different contractors. This study, therefore, aims to improve a method to make the decision-making process more efficient and dependable, particularly in scenarios involving multiple decision-makers and criteria. The improved method incorporates various factors, from technical ability to project-specific criteria, ensuring a comprehensive evaluation of potential contractors. For practicality, it utilizes a fuzzy evaluation language to capture the decision-makers’ uncertainty during the evaluation process. Additionally, the study uses a specific algorithm method to generate a group consensus result. The improved method also applies objective weights of both criteria and decision-makers to help reduce subjective evaluation. Through a case study, it is further found that the improved method is beneficial for inviting disciplinary decision-makers to join the megaproject evaluation process. The improved method is recommended for evaluating tenderer megaprojects, especially when decision-makers’ backgrounds are diverse and the accurate assessment of technical capabilities is critical. For practitioners interested in applying this method to their own projects, the programming code of this study is available for reference and use.