Building Maintenance Strategy Selection Using Z-numbers and the Delphi Method Enhanced Choosing by Advantages
Year: 2025
Type: conference-paper
Abstract: Effective maintenance strategies are essential for prolonging the service life of buildings and ensuring seamless business operations. However, selecting the best maintenance strategy can be challenging due to the uncertain nature of building maintenance activities, which often depend on subjective human judgment. Traditional decision-making methods may not fully address these challenges, especially when dealing with conflicting criteria. Subsequently, decision-making methods such as Choosing by Advantages (CBA) received attention in the literature and were utilized in building maintenance decision-making due to their practical and user-friendly structure. This paper proposes a novel approach that integrates Z-numbers and the Delphi method to address a previous limitation of the CBA method: subjectivity in the Importance of Advantage (IoA) scale. The presented integration aids in reducing subjectivity and supports the establishment of a consensus-driven multi-criteria decision-making framework. The proposed approach is demonstrated in a hypothetical scenario where maintenance options are evaluated based on several factors. The results show that Z-Delphi has the potential to improve CBA, marking an initial step toward addressing subjectivity in the IoA. While promising, this study relies on a hypothetical scenario to demonstrate the proposed framework, and real-world validation is required to fully evaluate its applicability. Furthermore, it sets the stage for future research by investigating alternative approaches for developing IoA scales in CBA and incorporating web-based platforms and AI. This study enhances the existing body of knowledge by introducing a novel framework that addresses a challenge within a well-established decision-making method.
Source: CIB Conferences
Authors: Mahdi Anbari Moghadam, Deniz Besiktepe
Institution: Purdue University West Lafayette
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4411472987