Synthesis of Multi-Criteria Decision-Making Applications in Facilities Management and Building Maintenance: Trends, Methods, and Future Research Directions

Abstract

Building maintenance decisions are complex and often influenced by various factors. Multi-criteria decision-making (MCDM) methods have been widely applied to address this complexity, yet guidance on selecting the most appropriate method for specific problems remains limited. Considering these, the purpose of this study is to provide a guidance for the nexus of MCDM methods and facilities management (FM) and building maintenance with the aim of supporting the selection of the most appropriate MCDM method for a specific problem. To achieve this, the study first offers a comprehensive overview of MCDM applications in FM and building maintenance through a systematic literature review guided by the PRISMA framework combined with scientometric analysis. This approach identifies key trends, reviews the methods most frequently employed, and outlines future research directions. From an initial pool of 4291 records retrieved from Scopus and Web of Science between 2000 and 2024, 107 studies were further analyzed. Using VOSviewer and Bibliometrix, the review maps the application of MCDM methods in FM and building maintenance over this period. As a major outcome of the study, a contextual MCDM Method Selection Matrix is developed, linking specific FM and maintenance problems to the most suitable MCDM methods. The findings reveal growing adoption of hybrid MCDM methods and highlight persistent challenges, including subjectivity, uncertainty, expert qualifications, methodological gaps, and technology integration in the decision-making process. By providing structured guidance on method selection, the contextual MCDM Method Selection Matrix supports researchers and practitioners in achieving consistent, data-driven, and context-sensitive decision-making, ultimately enhancing the longevity, efficiency, and sustainability of the built environment.

1. Introduction and Purpose

Facilities management (FM) and building maintenance decisions are complex, often influenced by multiple stakeholders, resource constraints, environmental considerations, technological advancements, regulatory requirements, and operational priorities. Despite their critical impact on the performance, sustainability, and longevity of buildings, these decisions often rely on subjective judgment, leading to inconsistencies, inefficiencies, and missed opportunities [1,2]. To address these issues, systematic approaches such as Multi-Criteria Decision-Making (MCDM) have been increasingly adopted to ensure more rational and data-driven decision-making process [3,4].

Although MCDM methods have been increasingly applied in the built environment, research on their use specifically in FM and building maintenance remains limited. Existing studies mainly focus on individual case studies or specific techniques, providing limited insights into broader patterns, challenges, and best practices [1,5,6,7]. This lack of a cohesive perspective, coupled with the complexity of evaluating multiple, often conflicting criteria, complicates the selection of appropriate MCDM methods. This gap, in turn, contributes to inconsistent and suboptimal decision-making by facility managers and building owners, leading to inefficient resource allocation, maintenance backlog and increased maintenance costs [8]. Emerging technologies such as the Internet of Things (IoT), digital twins, and Building Information Modeling (BIM) further amplify decision-making complexity. Simultaneously, building owners and operators are under increasing pressure to meet sustainability, energy efficiency, and cost-reduction objectives, making structured, evidence-based decision frameworks more essential than ever [9,10]. Despite these, a lack of practical guidance for selecting the most appropriate MCDM methods for specific FM and maintenance problems remains which is the main motivation of this study.

With these, the purpose of this study is to provide a guidance for the nexus of MCDM methods and FM and building maintenance with the aim of supporting the selection of the most appropriate MCDM method for a specific problem. To achieve this, the study first offers a comprehensive overview of MCDM applications in facilities management (FM) and building maintenance through a systematic literature review guided by the PRISMA framework [11] combined with scientometric analysis. Bibliometric tools such as VOSviewer (Version 1.6.20) and Bibliometrix R package (R version 4.4.2) were employed to visualize knowledge networks, analyze annual scientific output, examine keyword co-occurrence, and identify dominant research themes. Building on these findings, the study develops a contextual MCDM Method Selection Matrix that links specific FM and maintenance challenges to the most suitable MCDM methods. By integrating systematic review, scientometric mapping, and structured method selection, this research offers an evidence-based framework to strengthen decision-making, improve resource allocation, and foster sustainable, cost-effective outcomes in the built environment.

2. Background

2.1. Facilities Management and Building Maintenance

Facilities Management (FM) is essential to ensuring the functionality, safety, and efficiency of built environment, influencing organizational performance and occupant well-being. By coordinating space, infrastructure, people, and processes, FM supports core business goals and contributes to user satisfaction. Effective FM practices are linked to increased productivity and reduced absenteeism [1,12,13,14]. In higher education, well-managed facilities are associated with higher student satisfaction and academic success [15,16]. Building maintenance, a key aspect of FM, ensures the longevity and safety of buildings by addressing wear and tears, preventing system failures, and supporting regulatory compliance. Activities such as inspections and timely repairs reduce risks related to structural degradation and equipment breakdowns [17]. Abisuga et al. [18] emphasize that proactive maintenance strategies are crucial for avoiding costly repairs and ensuring safety. Additionally, studies show that well-maintained buildings promote better indoor air quality, positively impacting occupant health, comfort, and performance [19,20,21].

The importance of FM and building maintenance is increasingly recognized in the context of sustainability and resilience. Maintenance strategies now influence energy efficiency, environmental quality, and even occupant behavior. As a result, FM and building maintenance are no longer viewed solely as technical or operational tasks, but as strategic functions that support long-term organizational goals, environmental responsibility, and social well-being [13,22,23,24,25].

Moreover, the integration of Artificial Intelligence (AI) and Machine Learning (ML) in recent years is revolutionizing FM and building maintenance by shifting practices from reactive to proactive and predictive approaches [26]. Initially, the focus of maintenance practices are shifting to digitizing data and using Building Information Modeling (BIM) for better data organization and planning. However, early applications were limited by the quality and generalizability of available datasets, requiring customized models for different building components. Today, AI and ML are enhancing operational efficiency, enabling predictive maintenance, optimizing energy consumption, and improving occupant experiences, all while contributing to more sustainable building operations [27,28,29].

The field is also embracing lifelong learning models, which aim to develop self-learning, adaptive, and real-time predictive maintenance solutions. Advanced ML techniques, such as Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Support Vector Machines (SVM), have shown substantial improvements over traditional methods, particularly in fault detection, system optimization, and maintenance forecasting. These advancements are accelerating the potential of AI-driven, data-informed FM practices, enabling smarter, more sustainable building management [27,30,31,32,33,34].

2.2. Multi-Criteria Decision-Making (MCDM) Methods

MCDM is a structured approach used to evaluate and select the most suitable option among several alternatives when multiple and often conflicting criteria are involved. It helps decision makers analyze complex problems by considering both measurable and subjective attributes. MCDM methods are widely applied in areas such as engineering, management, and operations research where decisions require balancing various objectives. These techniques use decision matrices or similar tools to compare alternatives based on clearly defined criteria. This process enhances transparency, consistency, and logic in decision making. As organizational and technical challenges become more complex, MCDM continues to play a critical role in supporting informed and reliable choices [3,35,36].

A summary of widely used MCDM techniques is presented in

Table 1. Common MCDM Methods in FM and Building Maintenance.

Method	Key Features	Assumptions	Strengths
AHP [37]	Hierarchical structure; pairwise comparisons; expert judgment	Criteria are independent; judgments are consistent	Translates qualitative inputs into quantitative outputs; widely used and understood
ANP [38,39]	Captures interdependencies and feedback	Interrelated criteria and alternatives	Suitable for complex, non-hierarchical problems
BWM [40]	Compares best and worst criteria with others	Clear identification of extremes	High consistency: fewer comparisons required
CBA [41]	Focuses on advantages, not trade-offs or weights	Necessitates clear identification of advantages	Transparent and easy to communicate decisions
DEMATEL [42,43]	Influence mapping via direct relation matrix	Expert opinions; causal relationships	Visualizes interdependencies; often used with other methods
ELECTRE [44,45]	Outranking method; handles conflicts	No need for attribute independence	Good for complex problems; handles both qualitative and quantitative data
KEMIRA [46]	Aggregates rankings via Kemeny median; supports group-weight structures.	No predefined weights needed	Effective for uncertain or conflicting priorities
MAUT [47]	Utility-based evaluation; trade-offs allowed	Assumes independence among attributes	Highly flexible; allows for realistic modeling
PROMETHEE [48]	Utilizes preference functions; provides partial/full rankings	No need for attribute independence	Handles both qualitative and quantitative data; adaptable
SWARA [49]	Expert-driven stepwise weight assignment	Criteria are compensatory and independent	Simple to apply; effective in weighting subjective inputs
TOPSIS [50]	Distance-based ranking using ideal/negative-ideal solutions	Criteria should be measurable on a consistent scale reflecting performance	No requirement for attribute independence; straightforward ranking
VIKOR [51,52]	Compromise ranking based on ideal solutions	Attributes should be independent	Balances group consensus and individual preferences
WASPAS [53]	Combines WSM and WPM; additive and multiplicative analysis	Attributes must be independent	Enhances accuracy; flexible and comprehensive approach

MCDMs: AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), BWM (Best-Worst Method), CBA (Choosing by Advantages), DEMATEL (Decision-Making Trial and Evaluation Laboratory), ELECTRE (ELimination and Choice Expressing REality), KEMIRA (Kemeny Median Indicator Ranks Accordance), MAUT (Multi-Attribute Utility Theory), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), SWARA (Stepwise Weight Assessment Ratio Analysis), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), WASPAS (Weighted Aggregated Sum Product Assessment).

, outlining their key features, methodological foundations, and relevance to decision-making in FM and building maintenance. These methods were chosen due to their frequent application in recent literature and case studies that address complex decision challenges in these fields.

2.3. Built Environment and MCDM

Decision-making in the built environment requires the evaluation of multiple criteria such as cost, time, quality, sustainability, performance, and regulatory compliance. MCDM methods provide a structured approach to handling these complexities, enabling informed and balanced decisions across various construction-related domains [54,55,56]. MCDM is particularly valuable in material selection, where economic, environmental, and functional factors must be balanced. When integrated with Life Cycle Assessment (LCA), it enables systematic comparison of materials based on sustainability criteria, supporting more informed and viable decisions [57,58]. Managing risks in construction has been effectively supported by MCDM methods, especially under economic, safety related, and regulatory uncertainty. Hybrid models, such as ANP combined with MARCOS, have been applied to systematically evaluate and prioritize risks, aiding in the development of targeted mitigation strategies [59,60]. Additionally, MCDM plays a crucial role in evaluating building performance, particularly in energy efficiency, occupant comfort, and environmental impact. By integrating multiple performance indicators, MCDM assists architects and engineers in design optimization aligning sustainability goals and functionality [61,62,63]. Beyond these applications, MCDM is increasingly utilized for contractor selection, project prioritization, and sustainability assessments [64,65,66].

There are several review papers on MCDM related to the built environment, highlighting its expanding role in complex decision-making. For instance, in 2014, Kabir et al. [67] conducted a comprehensive review of around 300 studies on MCDM in infrastructure management, categorizing them by infrastructure type, decision context, and method. They concluded that the use of MCDM has grown significantly and effectively supports infrastructure decision-making, with many decision support tools combining multiple MCDM methods being successfully applied. Nik-Bakht and El-Diraby in 2015 [68] synthesized five decades of decision-making research in construction, tracing the evolution of decision models, tools, and criteria, and emphasizing the growing shift from purely technical, deterministic models toward more probabilistic, stakeholder-driven, and sustainability-oriented approaches. In 2019, Santoso and Darsono [69] studied the use of MCDM methods in dam construction, focusing on important factors like technical, economic, social, and environmental aspects to help choose the best dam location. They highlighted that using clear and organized decision-making tools such as AHP, ANP, and TOPSIS is essential to handle the complexity and multiple factors involved in these decisions. More recently, Zhu et al. in 2021 [70] provided a broad review of MCDM in construction research, emphasizing its wide applicability while also noting ongoing challenges such as data reliability and method selection for practitioners. Another relevant study by Tan et al. [71] reviewed 45 papers on the integration of MCDM with BIM, identifying key application areas, such as sustainability, retrofit, and safety, and outlining five strategies to enhance their synergy, while also highlighting gaps and future directions for improving integrated decision-making in the AEC industry. Most recently, Bajwa et al. in 2025 [57] conducted a systematic literature review on MCDM in sustainable material selection. Their study applied PRISMA and Bibliometrix frameworks to analyze publications from 2010 to 2023, identifying critical decision steps such as criteria establishment, hierarchy ranking, and consistency evaluation. They found AHP particularly useful for waste, recycled, and composite material selection, while also highlighting the growing role of hybrid MCDM techniques in managing complex sustainability-driven material choices.

Despite extensive research on the use of MCDM in the built environment [57,67,68,69,70,71], including applications in infrastructure management, dam construction, BIM integration, and broader construction-related decision-making, there remains a lack of systematic review that specifically addresses its application to FM and building maintenance. Existing reviews have primarily focused on general construction contexts or specialized areas, but none have provided structured guidance on selecting the most appropriate MCDM method for the unique challenges of FM and building maintenance. This study addresses this gap by introducing the Contextual MCDM Method Selection Matrix, which offers a practical framework for aligning decision-making techniques with FM and building maintenance problem types, thereby promoting more consistent, transparent, and informed decisions in practice.

3. Methodology

To achieve the purpose of the study, a structured research methodology was adopted, comprising three stages: (i) a systematic literature review using the PRISMA framework, (ii) Scientometric analysis of the selected literature, and (iii) synthesis of insights related to MCDM applications within FM and building maintenance. These stages are illustrated in Figure 1 and elaborated in the following sections.

In the first stage, a systematic review was conducted in accordance with PRISMA guidelines [11] to ensure a transparent and replicable process. Relevant literature was retrieved from the Web of Science and Scopus databases using a set of carefully developed search queries. The identification of records was followed by a structured screening process involving predefined inclusion and exclusion criteria. This ensured that only high-quality and relevant studies were included for further analysis. As presented in stage 1 of Figure 1, this phase encompassed three main steps: (i) identification, (ii) screening, and (iii) inclusion of studies.

The second stage involved a Scientometric analysis of the selected literature to quantitatively examine the structural characteristics of the research field. Bibliometric tools were employed to support this analysis, including the Bibliometrix package in RStudio 2024.12.0+467 [72] for data processing and VOSviewer [73] for constructing and visualizing bibliometric networks. The analysis focused on annual scientific output, keyword co-occurrence, and key publication sources, enabling the identification of dominant themes and knowledge clusters within the domain. This phase integrates VOSviewer-based visualizations with core analytical tasks such as keyword mapping.

In the final stage, the insights obtained from the previous phases were synthesized to understand the current state of research and methodological practices related to MCDM in FM and building maintenance. This synthesis involved the identification of prevalent trends, the categorization of applied MCDM methods, the formulation of future research directions, and the development of a contextual MCDM Method Selection Matrix tailored to various problem types in FM. Stage 3 of Figure 1 represents this integrative synthesis, highlighting the transition from analytical findings to practical and theoretical contributions. This structured approach not only ensured the rigor and transparency of the research process but also facilitated the recognition of dominant research streams, critical gaps, and underexplored areas that require further investigation.

3.1. Systematic Review Process with PRISMA

This study adopted the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to guide the systematic review process [11]. PRISMA is a widely adopted methodology that ensures clarity in identifying, screening, and including relevant literature. As presented in Figure 2, the process was carried out in three sequential stages: identification, data screening, and inclusion. The following sections present the details of each stage accordingly.

3.1.1. Identification

A comprehensive search was conducted using Scopus and the Web of Science, covering the period from 2000 to 2024 to encompass both foundational and contemporary contributions to the field. The search strategy combined terms representing three dimensions: (i) the built environment, (ii) facilities management and building maintenance, and (iii) multi-criteria decision-making techniques. The final search string was:

(“Building” OR “Construction”) AND (“Building Maintenance” OR “Building Performance” OR “Energy Efficiency” OR “Facility Management” OR “HVAC Systems” OR “Occupant Satisfaction” OR “Sustainability” OR “User Satisfaction”) AND (“AHP” OR “ANP” OR “BWM” OR “CBA” OR “COPRAS” OR “Decision-Making Techniques” OR “DEMATEL” OR “ELECTRE” OR “KEMIRA-M” OR “MADM” OR “MCDM” OR “PROMETHEE” OR “SAW” OR “TOPSIS” OR “VIKOR” OR “WPM” OR “WSM”).

This query yielded 4291 records. Following the removal of 1487 duplicates and 55 non-English publications, 2749 records remained for further screening. Duplicate documents retrieved from Web of Science and Scopus were identified and removed using reference management software Mendeley (version 2.137.0)and the functions in tools such as CiteSpace, which can merge records from both databases and facilitate duplicate removal. To enhance the comprehensiveness of the search, supplementary sources were explored beyond the main bibliographic databases. Manual searches were performed on key academic publisher websites such as the ASCE Library, SpringerLink, Taylor & Francis Online, Elsevier’s ScienceDirect, MDPI, and Emerald Insight. ResearchGate was also reviewed for relevant literature and conference materials. These efforts yielded five additional records. Furthermore, citation tracking using a backward snowballing approach [74], by examining the reference lists of relevant articles, led to the identification of four more studies. In total, nine supplementary records were added for evaluation.

3.1.2. Screening

The initial search yielded 4291 records. Prior to formal screening, duplicate entries and non-English records were excluded, which resulted in the removal of 1487 duplicate records and 55 non-English records. This left 2749 records for the screening phase. The screening process involved evaluating titles, abstracts, and keywords to assess the relevance of each publication to the study’s focus. Records were excluded at this stage if they did not clearly relate to the construction or building sector, failed to address facility operations or maintenance in existing buildings, or did not incorporate or discuss MCDM approaches. Following these steps, 280 records were shortlisted for full-text review. Each of these articles was evaluated in depth based on explicit inclusion and exclusion criteria. Records were excluded if they were unrelated to the construction industry, did not utilize any form of MCDM methodology, or did not focus on issues pertaining to existing buildings. Specifically, 46 records were removed for lacking relevance to the built environment, 54 for not applying MCDM methods, and 77 for targeting new construction rather than operational aspects of existing structures. The label “irrelevant” in this context refers to records that may have included search terms but failed to align conceptually or contextually with the study’s objectives.

To reduce the risk of bias in study selection, the first author screened all retrieved records for eligibility, and the second author validated the inclusion decisions. Any disagreements were resolved through discussion to reach consensus.

3.1.3. Included

Ultimately, 107 records were included in the final review, with 103 obtained through database searches and four identified via supplementary sources. As shown in Figure 2, this structured selection process ensured the inclusion of records closely aligned with the study’s objectives. These finalized records formed the basis for the next phase of the research, which applies Scientometric analysis to uncover the structural patterns, research trends, and intellectual foundations within the field. The following section details the methods and outcomes of this analytical process.

3.2. Scientometric Analysis of Identified Studies

Scientometric analysis is a quantitative method used to examine the structure and evolution of scientific research. As a branch of bibliometrics, it analyzes publication data—such as citation patterns, and keyword co-occurrences—to uncover intellectual trends and thematic developments. This method was selected for its ability to systematically and objectively explore a large body of literature. In this study, it served to investigate how MCDM methods have been applied in FM and building maintenance. The analysis revealed publication trends, influential sources and authors, and emerging research themes. Two established tools were used: VOSviewer [73], which constructs and visualizes bibliometric networks, and the Bibliometrix R package (R version 4.4.2) [72], which provides statistical and network analysis of bibliographic data. The investigation focused on three key areas: annual scientific production to identify research growth, source impact to highlight influential journals, and keyword analysis to trace thematic evolution. This approach provided a structured, data-driven overview of the intellectual landscape of MCDM research in FM and building maintenance.

3.3. Synthesis of Insights Related to MCDM Applications Within FM and Building Maintenance

The third stage of the methodology involved synthesizing outputs from the systematic review and Scientometric analysis to guide the development of Section 4.2. As shown in Stage 3 of Figure 1, this synthesis focused on four components: research trends, method classification, future research directions, and a method selection matrix. To identify trends, publication years and keyword co-occurrence data were analyzed to track thematic shifts and emerging areas of focus. For the classification of MCDM methods (Section 4.2.2), each study was coded according to the technique used, its structure (e.g., single or hybrid), and its application within FM decision domains. Research gaps (Section 4.2.3) were identified through the interpretation of Scientometric results and a qualitative review of study limitations. These were grouped into thematic priorities, including methodological transparency, subjectivity reduction, and the integration of data-driven and AI-based approaches. Finally, a decision matrix (Section 4.2.4) was developed by linking FM problem types to MCDM methods based on reviewed documents and the characteristics that make each method suitable for specific FM problems.

4. Results and Discussion

This section presents the results of the PRISMA-based systematic review and Scientometric analysis, followed by a structured synthesis of thematic and methodological insights into the use of MCDM in FM and building maintenance. The first part examines trends in annual scientific production, keyword usage, and leading publication sources to provide a bibliometric overview of the field. The second part synthesizes the literature, identifying key application trends, commonly used decision-making methods, and methodological developments. Building on this, emerging research directions are discussed, including the need to address methodological gaps, reduce subjectivity, promote data-driven decision-making, and explore the integration of artificial intelligence in FM contexts. The section concludes with a decision matrix that links FM problem types to suitable MCDM methods, offering practical guidance for researchers and practitioners.

4.1. Scientometric Analysis Results

4.1.1. Annual Scientific Production

These records were analyzed using the “biblioshiny” library in RStudio software [72] and VOSviewer software [73]. These tools are widely recognized in bibliometric research for their robust visualization capabilities and user-friendly environments, making them suitable for mapping scientific production trends and co-occurrence patterns. Figure 3 presents the annual scientific production on MCDM applications in FM and building maintenance from 2003 to the end of 2024. Although a general upward trend is observable, the growth has not followed a linear trajectory. Several periods of low publication activity are evident between 2003 and 2013, followed by notable fluctuations thereafter. A significant peak occurred in 2022, when 15 publications were recorded, the highest number in the dataset. This rise in publication volume may reflect a growing interest in data-driven decision-making approaches in the built environment. However, the underlying causes of this increase cannot be determined within the scope of this study.

4.1.2. Most Relevant Sources of the Identified Studies

The most relevant sources were identified as those journals that published the highest number of studies included in the final dataset. This analysis was conducted using the biblioshiny interface within the Bibliometrix package in R, which enables structured and replicable bibliometric assessments. A total of 107 articles were retrieved from 68 distinct journals. From these, the top 10 journals were selected based on publication frequency. This threshold was established to balance depth and breadth in analysis: including more journals would have introduced many low-frequency sources, while a smaller set would have excluded multiple high-contributing outlets. These 10 journals collectively account for approximately 41 percent of all identified publications, as shown in

Table 2. Top 10 journals with the highest number of publications.

Journal	Publisher	# of Publications
Journal of Building Engineering	Elsevier	7
Building and Environment	Elsevier	6
Sustainability	MDPI	6
Buildings	MDPI	4
Energy and Buildings	Elsevier	4
Facilities	Emerald	4
Journal of Performance of Constructed Facilities	ASCE *	4
Journal of Civil Engineering and Management	Taylor & Francis	3
Procedia Engineering	Elsevier	3
Sustainable Cities and Society	Elsevier	3

* American Society of Civil Engineers (ASCE).

, which illustrates the distribution of articles across the top sources.

The selected journals include both generalist and specialist outlets. Generalist journals such as Sustainability and Buildings (MDPI) publish across a wide range of topics related to sustainability, urban development, and the built environment. In contrast, specialist journals such as Building and Environment and Journal of Building Engineering (Elsevier) focus on narrower domains, including building physics, energy efficiency, and environmental performance. This distribution highlights the interdisciplinary nature of MCDM applications in FM and building maintenance, which draw on knowledge from civil engineering, environmental science, architecture, and management. A variety of publication models is also evident, with contributions appearing in both open-access and traditional subscription-based journals. Major publishers represented include Elsevier, MDPI, Taylor & Francis, Emerald, and the American Society of Civil Engineers (ASCE). This diversity of access models supports broad dissemination of research findings across academic, professional, and policy-making communities. It was also observed that 53 journals published only a single article related to the study scope. These one-time contributions may reflect exploratory or topic-specific investigations; however, their limited recurrence suggests a lower level of sustained engagement with MCDM in the context of facilities and building maintenance. Consequently, these sources were not included in the core analysis of the most relevant journals. The prominence of journals focused on sustainability, energy performance, and the built environment indicates a growing alignment between MCDM techniques and strategic goals such as environmental efficiency, lifecycle optimization, and performance-based decision-making. This alignment signals an emerging research orientation that will be further examined in the discussion of trends, methods, and future research directions in the following sections.

4.1.3. Keyword Analysis

The keyword analysis using Biblioshiny library in RStudio, reveals the conceptual structure of the literature on MCDM applications in FM and building maintenance. The Analytic Hierarchy Process (AHP) emerges as the most prominent method, referenced in multiple forms such as “Analytical Hierarchy Process” and “AHP,” totaling 25 mentions across the reviewed studies. To keep the table concise and prevent excessive length of it, only keywords with a minimum frequency of four occurrences are reported in

Table 3. Keyword occurrences.

Keywords	Occurrences
Analytic Hierarchy Process	9
Multi-Criteria Decision-Making	9
Facility Management	8
MCDM	8
Sustainability	8
Analytical Hierarchy Process	7
Multi-Criteria Decision Making	7
AHP	6
Facilities Management	6
BIM	5
Building Maintenance	5
Maintenance	5
Analytic Network Process	4
Energy Efficiency	4
TOPSIS	4

. As a result, the keyword “Analytic Hierarchy Process (AHP)” is excluded, as it appears only three times in the dataset despite its overall prominence in the literature. This consistent presence reflects AHP’s continued dominance, likely due to its structured comparison framework and its ability to handle both qualitative and quantitative data in decision-making. The co-occurrence of terms like sustainability (n = 8) and building information modeling (BIM) (n = 5) signals a growing integration of digitalization, environmental concerns, and strategic decision-making. Other methods such as TOPSIS (n = 4), Analytic Network Process (ANP) (n = 4), DEMATEL (n = 3), and VIKOR (n = 2) appear less frequently, suggesting that newer or more complex approaches are still underutilized. This pattern indicates a prevailing reliance on well-established techniques, with AHP remaining the preferred choice.

Keywords are visualized as nodes in Figure 4 using VOSviewer, where node size represents frequency and link thickness reflects co-occurrence strength. The visualization reveals that multi-criteria decision-making, BIM, and facility management occupy central positions, reflecting both their importance and their strong interconnections. Associated terms such as sustainability, energy efficiency, AHP, and ANP further demonstrate the integration of performance-driven goals with analytical decision-making tools.

Additionally, thematic clusters emerge in the mapping. One cluster emphasizes decision-making techniques, another focuses on sustainability and energy, and a third encompasses operational themes such as maintenance and healthcare facilities. While core topics are well connected, more peripheral terms—such as healthcare facilities—indicate underexplored areas. These findings suggest opportunities for deeper investigation into how decision-making frameworks and digital tools like BIM can be applied across sector-specific and operational domains.

4.2. Synthesis of MCDM Applications in FM and Building Maintenance

4.2.1. Trends

This section synthesizes current trends in the application of MCDM methods to FM and building maintenance, drawing from the 107 reviewed studies. The trends reflect both methodological evolution and thematic shifts in response to increasing complexity, performance pressures, and the digitization of the built environment. Analysis is based on Scientometric findings and a thematic review of published literature.

The annual scientific production depicted in Figure 3 demonstrates a gradual but non-linear increase in research activity from 2003 to 2024, with notable growth after 2015. The surge in publications during the last decade aligns with broader disciplinary trends emphasizing performance-based decision frameworks [75,76,77], lifecycle optimization, and sustainable infrastructure planning [78,79,80,81,82].

AHP remains the dominant method, used in 50 identified studies. It is favored for its transparency, ease of implementation, and compatibility with hierarchical problem structures. However, its assumptions of criteria independence and susceptibility to subjectivity have led to increasing interest in hybrid approaches, which appear in 29 studies. These often combine AHP, TOPSIS, ANP, or DEMATEL to improve analytical robustness, handle interdependencies, and integrate qualitative and quantitative inputs [67,83,84,85]. The rise in hybrid applications reflects a shift from rigid, linear decision models to more dynamic frameworks capable of supporting the multifaceted nature of FM decisions.

A review of the methodological characteristics of the selected studies reveals the continued dominance of crisp MCDM methods, which are applied in 78 studies. This prevalence reflects FM’s traditional reliance on deterministic evaluation criteria. In contrast, 27 studies apply fuzzy-enhanced MCDM techniques, indicating a growing awareness of the need to incorporate subjective judgments and linguistic assessments, particularly in contexts characterized by limited data or qualitative trade-offs. Fuzzy set theory, introduced by Zadeh [86], has proven especially useful in areas such as energy retrofit prioritization, FM outsourcing decisions, and building system evaluations [83,87].

In contrast, only 2 studies utilize grey theory [88], which is designed for conditions of incomplete or partially known information. Despite its suitability for early-stage planning, risk assessment, and infrastructure maintenance forecasting, grey theory remains underexplored in FM [89,90]. This represents a clear gap, suggesting that the field may benefit from methodological diversification beyond the dominant crisp-fuzzy binary. Keyword co-occurrence patterns, illustrated in Figure 4 and quantified in Table 3, reveal central themes such as “facility management,” “multi-criteria decision-making,” “AHP,” and “sustainability.” The frequent pairing of FM-related terms with “BIM” and “energy efficiency” indicates a growing convergence between MCDM and digital performance management systems. This thematic convergence is consistent with findings in adjacent reviews, where digitalization and sustainability are increasingly prominent in decision-support research [89,90,91,92,93,94].

Another important trend is the shift toward participatory and stakeholder-sensitive decision frameworks. Many recent studies incorporate structured interviews, the Delphi method, or collaborative weighting procedures to account for the diverse interests of building owners, users, and facility operators. Stakeholder-based and Delphi-based MCDM approaches have been applied in various FM contexts, including healthcare, education, and municipal facilities, to improve transparency, align priorities, and support consensus-driven decision-making [93,95,96,97,98,99,100]. These models recognize that FM decisions are not solely technical but also shaped by organizational cultures, legal frameworks, and user expectations.

Lastly, a growing subset of studies integrates MCDM tools with data-driven technologies. These include not only previously noted applications of BIM-enhanced MCDM frameworks but also FAHP models supported by cloud computing and data analytics [101], as well as integrated BIM and augmented reality (AR) systems for interactive decision environments [102]. While still emergent, such studies suggest a future trajectory centered on real-time, adaptive decision-making systems [103]. However, challenges remain regarding interoperability, data quality, and the operationalization of complex hybrid algorithms in practice.

The trajectory of MCDM research in FM and building maintenance reflects a maturing field. Traditional, deterministic models continue to serve as foundational tools, but are increasingly supplemented by hybrid, fuzzy, and stakeholder-centered approaches. The field is expanding in both volume and conceptual sophistication, with growing attention to sustainability, digital integration, and participatory governance.

4.2.2. MCDM Methods

This section reviews key MCDM methods applied in FM and building maintenance, emphasizing their conceptual foundations, structural characteristics (e.g., single or hybrid), and practical applications. For each method, representative studies are analyzed to highlight use cases, strengths, and limitations within FM contexts. The aim is to clarify how these tools support complex decision-making and inform method selection based on problem type and contextual requirements.

Analytic Hierarchy Process (AHP)

AHP, developed by Saaty in 1980 [37,104], is a structured decision-making method that uses pairwise comparisons to assign weights across hierarchical levels of criteria and alternatives. It translates qualitative judgments into quantitative values and is particularly effective when expert input is essential. By organizing complex problems into a hierarchy, typically a goal, criteria, and alternatives, AHP enables systematic comparisons and prioritization. In FM and building maintenance, AHP has been widely applied in areas such as maintenance prioritization, procurement strategy selection, performance evaluation, and sustainability assessment. It is especially useful when decisions require stakeholders or expert input, as seen in FM service evaluation [6,105], sourcing strategy selection [106,107], HVAC selection [108]. The range of applications summarized in Table 4 illustrates AHP’s versatility in FM decision-making.

Despite its versatility, AHP has notable limitations. A major drawback is its reliance on expert judgment, which introduces subjectivity and inconsistency [105,109]. Some studies face limitations in generalizability due to small sample sizes and highly context-specific conditions [106,110]. Additionally, AHP struggles with capturing interdependencies between criteria, as it assumes independence in decision factors [111,112]. Implementation challenges arise in resource-constrained environments due to the method’s complexity and the need for structured data [113,114]. Addressing these limitations requires refining AHP models through expanded datasets, integration with dynamic decision tools, and rigorous validation across diverse FM contexts. AHP in FM provides a structured approach to decision-making, integrating qualitative and quantitative data to foster stakeholder agreement and enable sensitivity analysis. It aids in critical areas like maintenance prioritization and procurement. However, AHP can be time-consuming, sensitive to inconsistencies, reliant on expert judgment, and may constrain creativity by enforcing rigid criteria.

Table 4. AHP applications in FM and Building Maintenance.

Reference	Problem Solved Using the AHP Method	Crisp	Fuzzy	Limitations
[115]	Satisfying stakeholders, minimizing deterioration, and evaluating the impact of owners and users on longevity.	✔		The lack of experienced facility management professionals and the insufficient legal framework in Lithuania.
[113]	Developing a decision analysis model that enhances CMMS functionality, supporting informed maintenance policy decisions and dynamic schedule adaptations.		✔	Data availability, accuracy, and the need for structured fault codes, which could hinder widespread adoption.
[107]	Developing a model for selecting sourcing strategies in facilities management services procurement, aiding decision-making for local authorities.	✔		Limited sample size and focus on the Italian market restrict the generalizability of the findings, requiring further research with a larger and more diverse sample.
[106]	Creating a decision support tool for selecting the optimal sourcing strategy in facilities management for hospital enterprises.	✔		reliance on a small sample of expert judgments limits the generalizability of the results to the broader healthcare sector.
[116]	FAHP and GIS were used to optimize solid waste disposal site selection in urban areas, factoring in socio-economic and environmental aspects.		✔	Resource constraints and complex factors hinder model implementation, especially in cities with limited infrastructure.
[111]	Identifying and prioritizing key criteria for intelligent building system selection, including efficiency, comfort, safety, and cost-effectiveness.	✔		It examines building systems and selection criteria but ignores interrelationships among the criteria.
[114]	By integrating AHP, fuzzy logic, and SWOT analysis, this model offers a systematic approach for better FM outsourcing decisions.		✔	The study’s integration of multiple theories may make the model difficult to implement, especially for organizations lacking the required expertise or resources.
[117]	lack of a systematic decision-making framework for building maintainability, which leads to intuitive judgments, recurrent mistakes, and poor maintainability in building projects.	✔		The model’s effectiveness may be limited by its specificity to certain building types, climates, or regions, requiring case-by-case adaptation and potential refinement over time.
[105]	Improving FM service evaluation by addressing limitations in traditional satisfaction surveys and incorporating both user satisfaction and importance rankings.	✔		The presence of inconsistent judgments in one-third of the responses, making it difficult to achieve fully reliable importance ranking across all FM aspects.
[6]	Creating a comprehensive method for evaluating FM services in high-rise structures by resolving inconsistent user feedback and factoring in both costs and performance.	✔		A challenge encountered was the frequent inconsistency in respondents’ judgments, despite attempts to reduce discrepancies during the survey process.
[5]	Integrating uncertainty in key performance indicators like energy efficiency and thermal comfort to enhance building performance assessment and support informed design decisions.	✔		AHP aids decision-making but often complicates cost-effective design due to performance assessment complexity and uncertainty.
[110,118]	Selecting an appropriate procurement method for building maintenance projects in Malaysian public universities.	✔		It focuses on just one university, which restricts the ability to generalize the results and introduces potential biases based on the personal experiences and views of the interviewees.
[119]	Enhancing reliability in maintenance prioritization for community buildings, reducing subjectivity and inconsistency in traditional models.		✔	The model is limited to initial decision-making and requires further research on budget constraints, lifecycle considerations, and real-world validation through case studies.
[120]	Optimizing decision-making in sustainable building maintenance by integrating AHP with Lean Six Sigma to improve benchmarking and prioritization.	✔		Potential resistance to change and lack of management commitment in implementing Lean Six Sigma, as well as the constraints of the McKinsey 7S framework in assessing organizational readiness.
[76]	Selecting the most suitable procurement method for building maintenance in Malaysian public universities, improving decision-making efficiency and strategic planning.	✔		Potential biases in decision-making based on past experiences, misalignment with actual procurement practices, and a small sample size of nine universities, affecting the framework’s generalizability.
[121]	Lack of a credible system to assess FM performance in hotels highlights the need for a comprehensive Performance Measurement System for services like maintenance, cleaning, security, and catering.	✔		Reliance on subjective judgments in KPI importance during interviews calls for future work to identify quantitative measures for more objective assessments.
[91]	Creating a decision support tool that helps optimize retrofit and maintenance choices for improving energy efficiency in building management.	✔		The need for further validation of the decision support tool across various building types beyond the initial case study to ensure broader applicability.
[109]	Developing a Building Performance Risk Rating Tool that evaluates the performance-risk indicators of higher education buildings, integrating health and safety risks with performance assessment.	✔		Relatively small sample size with an approximate of 55% response rate, and the reliance on expert judgment in the AHP method, which introduces subjectivity into the results.
[122]	Complexity in selecting retrofit measures for existing buildings to balance environmental, economic, and social factors.	✔		AHP results depend on criteria, weighting and structure, requiring consensus for reliable retrofit rankings.
[123]	Selecting an optimal ventilation system for buildings in Saudi Arabia which systematically evaluates multiple criteria to determine whether natural or mechanical ventilation is more suitable for different climatic regions.	✔		Focus on only three regions, potential variations in building types, and the exclusion of other influential factors.
[124]	Prioritizing and weighting criteria to develop an energy efficiency rating system for existing buildings in Egypt, aiming to enhance energy performance in the sector.	✔		Focus on existing buildings in Egypt, requiring methodology modifications for application in other regions and regulatory frameworks.
[7]	Selecting the most effective and cost-efficient maintenance strategy for building facilities by handling uncertainties in maintenance knowledge and expert feedback.		✔	Lack of a systematic approach for recording maintenance strategies, work efficiency, and costs, as well as the reliance on subjective expert opinions, which can lead to inconsistent strategic planning.
[77]	Lack of a systematic and environmentally conscious approach to lighting maintenance by incorporating cost, labor, and CO2 emission considerations to optimize maintenance strategies.	✔		Formulating a clustered network to efficiently allocate labor, address non-emergency maintenance, and manage uncertainties in maintenance modeling.
[101]	Improving procurement in FM by using a cloud-based FAHP and OLAP system for better decision-making, transparency, and efficiency.		✔	The system’s effectiveness depends on accurate data input and handling vague or imprecise information.
[125]	The study tackles undefined maintenance standards, budget constraints, and limited assessment criteria in Polish residential buildings, proposing a budgeting support method.	✔		The model’s implementation may be limited by financial constraints and decision-making complexities in building management.
[78]	Prioritizing critical assets in healthcare facilities for capital renewal by integrating factors like physical condition, infection prevention, life safety, and revenue loss.	✔		The model’s effectiveness relies on data accuracy, subjective weight assignments, and periodic updates to stay relevant.
[126]	Prioritizing facility maintenance work orders in public institutions, particularly in K-12 schools, by providing a structured, evidence-based approach.	✔		The effectiveness of model depends on subjective assessments, and its transferability is limited, requiring customization for different institutional contexts.
[93]	The AHP and Delphi-based Performance Information Model enhances BIM-FM integration by prioritizing critical performance metrics, improving decision-making for healthcare facility maintenance and planning.	✔		The implementation is currently limited to operating room environmental systems, requiring further expansion to other engineering systems and broader BIM-FM data integration.
[127]	A data-driven approach for estimating repair schedules in apartment buildings integrates FAHP and Case-Based Reasoning, enabling proactive maintenance with limited historical data.		✔	The model’s applicability is limited by its general approach, lacking consideration of building materials, environmental conditions, and a standardized system for maintenance data.
[102]	The AHP method, combined with BIM and AR, solves inefficiencies in FM by prioritizing equipment maintenance, integrating data, and enhancing decision-making to reduce costs.		✔	The system’s emphasis on a single equipment component restricts its use to the early phases of operation and maintenance, demanding considerable expertise and experience.
[128]	Creating a tool to quantify and assess sustainable building performance based on key criteria and indicators.	✔		The main challenge was integrating multiple sustainability factors into a simple, user-friendly tool for designers and architects.
[129]	Lack of effective FM tools and strategies for post-independence real estate in Lithuania, leading to inefficiencies and high maintenance costs in the public sector.	✔		The model’s focus on Kaunas social housing limits its broader applicability, requiring further validation for other contexts.
[130]	Evaluating the environmental sustainability of FM in Sri Lanka’s apparel industry by identifying and prioritizing key sustainability indicators.	✔		The model’s clarity and responsiveness were rated moderate by users, requiring refinements such as sub-criteria introduction and IT-based implementation for improved usability.
[112]	Addresses slow adoption of energy-efficient retrofits in the U.S. by prioritizing decision factors like payback period and funding, aiding facility managers in overcoming retrofit barriers.	✔		Relies on expert judgments, introducing subjectivity, and its U.S.-specific findings limit generalizability. It also assumes factor independence, overlooking potential correlations.
[131]	Selecting the most suitable elevator for a building’s improvement project, ensuring the best choice is made based on various technical and competence factors.	✔		A potential limitation is the focus on technical criteria, neglecting factors like maintenance costs and user experience, and not addressing stakeholder input subjectivity.
[132]	The model helps optimize long-term maintenance planning for multifamily housing, balancing budgets, stakeholder needs, and building constraints while improving performance and considering environmental and social impacts.		✔	This model’s lack of risk-awareness and failure to account for fluctuating costs or changing conditions, such as new technologies or unexpected deterioration of building components.
[133]	Identifies key KPIs and uses AHP to prioritize facility improvements based on user satisfaction.	✔		Limited research, inconsistent user responses, and potential challenges in generalizing findings to other sports venues.
[134]	FAHP streamlines repair and maintenance decisions for commercial buildings in resource-limited settings by prioritizing key criteria for cost-effective solutions.		✔	Small expert panel from a single country, potential biases in criteria selection, and limited generalizability to other regions or building types.
[135]	The study tackles enhancing energy efficiency in heritage buildings while preserving identity. FAHP identifies and prioritizes retrofitting measures by protection level.		✔	Balancing energy efficiency with heritage preservation is challenging, as interventions can alter building aesthetics. Serbia’s unclear regulations further complicate retrofitting efforts.
[81]	The study develops an AHP-based MCDM model to integrate circularity assessments into building project decision-making, addressing the lack of a holistic framework for lifecycle circularity at the micro level.	✔		The industry prioritizes recycling/reuse over full circularity, with regional and experience-based variations in CA awareness. A real-world application is necessary for validation of this model.
[103]	The study uses Parsimonious-FAHP to identify and prioritize barriers to BIM adoption in FM, helping stakeholders address cost, expertise, and awareness challenges.		✔	Findings are specific to New Zealand, limiting global applicability. The study also lacks in-depth analysis of barrier interrelations and root causes, requiring further research.
[108]	Selecting HVAC systems, relying on expert knowledge without structured consideration of factors like quality, cost, and performance.	✔		The model was only validated for office buildings and does not yet cover other building types or HVAC functions in the selection process.
[136]	The study applies FAHP to select the optimal maintenance strategy for elevator systems, considering safety, reliability, and cost while managing expert judgment uncertainties.		✔	FAHP’s complexity may limit real-world use, and its focus on elevators restricts applicability to other electrical systems. Further research is needed for broader use.
[137]	The study develops a model to prioritize key factors, aiding asset management and infrastructure decisions in healthcare facilities.	✔		The model lacks input from some departments, excludes repair costs, and has a small sample, limiting accuracy. MS Excel also restricts operational capabilities.
[138]	Evaluating factors influencing user satisfaction in private housing estates in Abuja, identifying key aspects like utility, infrastructure, and maintenance.	✔		The research is limited to occupants’ feedback from private estates in Abuja, excluding property managers.
[139]	Using FAHP, the study evaluates cost-effective IEQ factors for sustainable office retrofits, prioritizing occupant satisfaction without raising energy use.		✔	Some survey responses were unreliable due to high consistency ratios. The study also relies solely on expert opinions, missing occupant perspectives.
[99]	The study uses FAHP to identify and rank key criteria for Maintenance and Repair in healthcare buildings in Iraq, focusing on factors like cost, human resources, and quality.		✔	The study is limited by a small sample size and a focus on Iraq, meaning the findings may not be universally applicable.
[140]	Evaluating building conditions and FM practices in Portuguese HEIs, the study highlights maintenance challenges like poor expertise, outdated policies, and inefficiencies.	✔		Lacks full representativeness for Portugal and other building types. Maintenance coordination challenges also impacted on findings.
[141]	Selecting the optimal ventilation system for educational buildings to enhance air quality and thermal comfort while balancing economic, social, and environmental factors	✔		Findings are context-specific, with criteria varying by region and preferences. The method simplifies reality, and no single system fits all—each case needs individual evaluation.

Table 4. AHP applications in FM and Building Maintenance.

Reference	Problem Solved Using the AHP Method	Crisp	Fuzzy	Limitations
[115]	Satisfying stakeholders, minimizing deterioration, and evaluating the impact of owners and users on longevity.	✔		The lack of experienced facility management professionals and the insufficient legal framework in Lithuania.
[113]	Developing a decision analysis model that enhances CMMS functionality, supporting informed maintenance policy decisions and dynamic schedule adaptations.		✔	Data availability, accuracy, and the need for structured fault codes, which could hinder widespread adoption.
[107]	Developing a model for selecting sourcing strategies in facilities management services procurement, aiding decision-making for local authorities.	✔		Limited sample size and focus on the Italian market restrict the generalizability of the findings, requiring further research with a larger and more diverse sample.
[106]	Creating a decision support tool for selecting the optimal sourcing strategy in facilities management for hospital enterprises.	✔		reliance on a small sample of expert judgments limits the generalizability of the results to the broader healthcare sector.
[116]	FAHP and GIS were used to optimize solid waste disposal site selection in urban areas, factoring in socio-economic and environmental aspects.		✔	Resource constraints and complex factors hinder model implementation, especially in cities with limited infrastructure.
[111]	Identifying and prioritizing key criteria for intelligent building system selection, including efficiency, comfort, safety, and cost-effectiveness.	✔		It examines building systems and selection criteria but ignores interrelationships among the criteria.
[114]	By integrating AHP, fuzzy logic, and SWOT analysis, this model offers a systematic approach for better FM outsourcing decisions.		✔	The study’s integration of multiple theories may make the model difficult to implement, especially for organizations lacking the required expertise or resources.
[117]	lack of a systematic decision-making framework for building maintainability, which leads to intuitive judgments, recurrent mistakes, and poor maintainability in building projects.	✔		The model’s effectiveness may be limited by its specificity to certain building types, climates, or regions, requiring case-by-case adaptation and potential refinement over time.
[105]	Improving FM service evaluation by addressing limitations in traditional satisfaction surveys and incorporating both user satisfaction and importance rankings.	✔		The presence of inconsistent judgments in one-third of the responses, making it difficult to achieve fully reliable importance ranking across all FM aspects.
[6]	Creating a comprehensive method for evaluating FM services in high-rise structures by resolving inconsistent user feedback and factoring in both costs and performance.	✔		A challenge encountered was the frequent inconsistency in respondents’ judgments, despite attempts to reduce discrepancies during the survey process.
[5]	Integrating uncertainty in key performance indicators like energy efficiency and thermal comfort to enhance building performance assessment and support informed design decisions.	✔		AHP aids decision-making but often complicates cost-effective design due to performance assessment complexity and uncertainty.
[110,118]	Selecting an appropriate procurement method for building maintenance projects in Malaysian public universities.	✔		It focuses on just one university, which restricts the ability to generalize the results and introduces potential biases based on the personal experiences and views of the interviewees.
[119]	Enhancing reliability in maintenance prioritization for community buildings, reducing subjectivity and inconsistency in traditional models.		✔	The model is limited to initial decision-making and requires further research on budget constraints, lifecycle considerations, and real-world validation through case studies.
[120]	Optimizing decision-making in sustainable building maintenance by integrating AHP with Lean Six Sigma to improve benchmarking and prioritization.	✔		Potential resistance to change and lack of management commitment in implementing Lean Six Sigma, as well as the constraints of the McKinsey 7S framework in assessing organizational readiness.
[76]	Selecting the most suitable procurement method for building maintenance in Malaysian public universities, improving decision-making efficiency and strategic planning.	✔		Potential biases in decision-making based on past experiences, misalignment with actual procurement practices, and a small sample size of nine universities, affecting the framework’s generalizability.
[121]	Lack of a credible system to assess FM performance in hotels highlights the need for a comprehensive Performance Measurement System for services like maintenance, cleaning, security, and catering.	✔		Reliance on subjective judgments in KPI importance during interviews calls for future work to identify quantitative measures for more objective assessments.
[91]	Creating a decision support tool that helps optimize retrofit and maintenance choices for improving energy efficiency in building management.	✔		The need for further validation of the decision support tool across various building types beyond the initial case study to ensure broader applicability.
[109]	Developing a Building Performance Risk Rating Tool that evaluates the performance-risk indicators of higher education buildings, integrating health and safety risks with performance assessment.	✔		Relatively small sample size with an approximate of 55% response rate, and the reliance on expert judgment in the AHP method, which introduces subjectivity into the results.
[122]	Complexity in selecting retrofit measures for existing buildings to balance environmental, economic, and social factors.	✔		AHP results depend on criteria, weighting and structure, requiring consensus for reliable retrofit rankings.
[123]	Selecting an optimal ventilation system for buildings in Saudi Arabia which systematically evaluates multiple criteria to determine whether natural or mechanical ventilation is more suitable for different climatic regions.	✔		Focus on only three regions, potential variations in building types, and the exclusion of other influential factors.
[124]	Prioritizing and weighting criteria to develop an energy efficiency rating system for existing buildings in Egypt, aiming to enhance energy performance in the sector.	✔		Focus on existing buildings in Egypt, requiring methodology modifications for application in other regions and regulatory frameworks.
[7]	Selecting the most effective and cost-efficient maintenance strategy for building facilities by handling uncertainties in maintenance knowledge and expert feedback.		✔	Lack of a systematic approach for recording maintenance strategies, work efficiency, and costs, as well as the reliance on subjective expert opinions, which can lead to inconsistent strategic planning.
[77]	Lack of a systematic and environmentally conscious approach to lighting maintenance by incorporating cost, labor, and CO2 emission considerations to optimize maintenance strategies.	✔		Formulating a clustered network to efficiently allocate labor, address non-emergency maintenance, and manage uncertainties in maintenance modeling.
[101]	Improving procurement in FM by using a cloud-based FAHP and OLAP system for better decision-making, transparency, and efficiency.		✔	The system’s effectiveness depends on accurate data input and handling vague or imprecise information.
[125]	The study tackles undefined maintenance standards, budget constraints, and limited assessment criteria in Polish residential buildings, proposing a budgeting support method.	✔		The model’s implementation may be limited by financial constraints and decision-making complexities in building management.
[78]	Prioritizing critical assets in healthcare facilities for capital renewal by integrating factors like physical condition, infection prevention, life safety, and revenue loss.	✔		The model’s effectiveness relies on data accuracy, subjective weight assignments, and periodic updates to stay relevant.
[126]	Prioritizing facility maintenance work orders in public institutions, particularly in K-12 schools, by providing a structured, evidence-based approach.	✔		The effectiveness of model depends on subjective assessments, and its transferability is limited, requiring customization for different institutional contexts.
[93]	The AHP and Delphi-based Performance Information Model enhances BIM-FM integration by prioritizing critical performance metrics, improving decision-making for healthcare facility maintenance and planning.	✔		The implementation is currently limited to operating room environmental systems, requiring further expansion to other engineering systems and broader BIM-FM data integration.
[127]	A data-driven approach for estimating repair schedules in apartment buildings integrates FAHP and Case-Based Reasoning, enabling proactive maintenance with limited historical data.		✔	The model’s applicability is limited by its general approach, lacking consideration of building materials, environmental conditions, and a standardized system for maintenance data.
[102]	The AHP method, combined with BIM and AR, solves inefficiencies in FM by prioritizing equipment maintenance, integrating data, and enhancing decision-making to reduce costs.		✔	The system’s emphasis on a single equipment component restricts its use to the early phases of operation and maintenance, demanding considerable expertise and experience.
[128]	Creating a tool to quantify and assess sustainable building performance based on key criteria and indicators.	✔		The main challenge was integrating multiple sustainability factors into a simple, user-friendly tool for designers and architects.
[129]	Lack of effective FM tools and strategies for post-independence real estate in Lithuania, leading to inefficiencies and high maintenance costs in the public sector.	✔		The model’s focus on Kaunas social housing limits its broader applicability, requiring further validation for other contexts.
[130]	Evaluating the environmental sustainability of FM in Sri Lanka’s apparel industry by identifying and prioritizing key sustainability indicators.	✔		The model’s clarity and responsiveness were rated moderate by users, requiring refinements such as sub-criteria introduction and IT-based implementation for improved usability.
[112]	Addresses slow adoption of energy-efficient retrofits in the U.S. by prioritizing decision factors like payback period and funding, aiding facility managers in overcoming retrofit barriers.	✔		Relies on expert judgments, introducing subjectivity, and its U.S.-specific findings limit generalizability. It also assumes factor independence, overlooking potential correlations.
[131]	Selecting the most suitable elevator for a building’s improvement project, ensuring the best choice is made based on various technical and competence factors.	✔		A potential limitation is the focus on technical criteria, neglecting factors like maintenance costs and user experience, and not addressing stakeholder input subjectivity.
[132]	The model helps optimize long-term maintenance planning for multifamily housing, balancing budgets, stakeholder needs, and building constraints while improving performance and considering environmental and social impacts.		✔	This model’s lack of risk-awareness and failure to account for fluctuating costs or changing conditions, such as new technologies or unexpected deterioration of building components.
[133]	Identifies key KPIs and uses AHP to prioritize facility improvements based on user satisfaction.	✔		Limited research, inconsistent user responses, and potential challenges in generalizing findings to other sports venues.
[134]	FAHP streamlines repair and maintenance decisions for commercial buildings in resource-limited settings by prioritizing key criteria for cost-effective solutions.		✔	Small expert panel from a single country, potential biases in criteria selection, and limited generalizability to other regions or building types.
[135]	The study tackles enhancing energy efficiency in heritage buildings while preserving identity. FAHP identifies and prioritizes retrofitting measures by protection level.		✔	Balancing energy efficiency with heritage preservation is challenging, as interventions can alter building aesthetics. Serbia’s unclear regulations further complicate retrofitting efforts.
[81]	The study develops an AHP-based MCDM model to integrate circularity assessments into building project decision-making, addressing the lack of a holistic framework for lifecycle circularity at the micro level.	✔		The industry prioritizes recycling/reuse over full circularity, with regional and experience-based variations in CA awareness. A real-world application is necessary for validation of this model.
[103]	The study uses Parsimonious-FAHP to identify and prioritize barriers to BIM adoption in FM, helping stakeholders address cost, expertise, and awareness challenges.		✔	Findings are specific to New Zealand, limiting global applicability. The study also lacks in-depth analysis of barrier interrelations and root causes, requiring further research.
[108]	Selecting HVAC systems, relying on expert knowledge without structured consideration of factors like quality, cost, and performance.	✔		The model was only validated for office buildings and does not yet cover other building types or HVAC functions in the selection process.
[136]	The study applies FAHP to select the optimal maintenance strategy for elevator systems, considering safety, reliability, and cost while managing expert judgment uncertainties.		✔	FAHP’s complexity may limit real-world use, and its focus on elevators restricts applicability to other electrical systems. Further research is needed for broader use.
[137]	The study develops a model to prioritize key factors, aiding asset management and infrastructure decisions in healthcare facilities.	✔		The model lacks input from some departments, excludes repair costs, and has a small sample, limiting accuracy. MS Excel also restricts operational capabilities.
[138]	Evaluating factors influencing user satisfaction in private housing estates in Abuja, identifying key aspects like utility, infrastructure, and maintenance.	✔		The research is limited to occupants’ feedback from private estates in Abuja, excluding property managers.
[139]	Using FAHP, the study evaluates cost-effective IEQ factors for sustainable office retrofits, prioritizing occupant satisfaction without raising energy use.		✔	Some survey responses were unreliable due to high consistency ratios. The study also relies solely on expert opinions, missing occupant perspectives.
[99]	The study uses FAHP to identify and rank key criteria for Maintenance and Repair in healthcare buildings in Iraq, focusing on factors like cost, human resources, and quality.		✔	The study is limited by a small sample size and a focus on Iraq, meaning the findings may not be universally applicable.
[140]	Evaluating building conditions and FM practices in Portuguese HEIs, the study highlights maintenance challenges like poor expertise, outdated policies, and inefficiencies.	✔		Lacks full representativeness for Portugal and other building types. Maintenance coordination challenges also impacted on findings.
[141]	Selecting the optimal ventilation system for educational buildings to enhance air quality and thermal comfort while balancing economic, social, and environmental factors	✔		Findings are context-specific, with criteria varying by region and preferences. The method simplifies reality, and no single system fits all—each case needs individual evaluation.

Analytic Network Process (ANP)

Analytic Network Process (ANP) is developed by Saaty in 1996 [38,39], builds on AHP by capturing interdependencies and feedback among criteria. Unlike hierarchical AHP or distance-based TOPSIS, ANP models complex, dynamic relationships. In FM and building maintenance, it has been used to assess interrelated factors like building conditions and retrofitting priorities. While it enhances realism in decision-making, challenges with scalability and context-specific adaptation persist. ANP has been applied in FM and building maintenance to evaluate complex, interdependent factors such as building conditions, performance indicators, and smart retrofitting priorities, improving decision-making through integrated models, though challenges in scalability and geographic specificity remain.

The study by Faqih & Zayed in 2021 [94] presents a defect-based building condition assessment model for concrete buildings, using ANP for defect weightings, fuzzy logic for judgment uncertainty, and evidential reasoning for integrating data. Implemented on a BIM platform, it improves inspection efficiency and data management. The model showed promising results in a case study but is limited to concrete buildings and may face scalability challenges. Alfalah et al. [87] developed a framework using Fuzzy ANP to assess user satisfaction in post-secondary educational buildings, focusing on sustainability factors like thermal comfort and aesthetics. They addressed the lack of user-focused sustainability models for facility managers. A limitation was the need for larger sample sizes and building type-specific adaptations.

Lai et al. [142] developed an ANP model to evaluate hospital FM performance, selecting ten key performance indicators across safety, physical, financial, and environmental categories. They addressed the challenge of identifying optimal KPIs and determining their weightings for a comprehensive evaluation. The model was validated through a case study in Hong Kong. A limitation of the study was its geographical focus, which may hinder generalization to other hospitals or locations. Peiris et al. [143] studied decision-making criteria for smart retrofitting of office buildings in Hong Kong and Sri Lanka using ANP. They identified differences in priorities, with Hong Kong focusing on upfront financial costs and Sri Lanka emphasizing long-term operational sustainability. The study highlighted the importance of customizing strategies to regional contexts and prioritizing both economic and user-centric concerns. A limitation of the study was its focus on just two regions, which may limit the generalizability to other global contexts.

Decision-Making Trial and Evaluation Laboratory (DEMATEL)

Decision-Making Trial and Evaluation Laboratory (DEMATEL) is introduced by Fontela and Gabus [42,43], converts expert opinions into a quantitative matrix to analyze interdependencies and influence networks. It is often used with methods like AHP to rank attributes and visualize impacts in strategy and management. DEMATEL has been primarily used in FM and building maintenance for assessing repair and maintenance factors, green maintainability, evaluating building performance, and analyzing key influences in maintenance strategies and maturity models. Asmone et al. [79] developed a green maintainability assessment system to enhance sustainability in building projects by identifying critical performance indicators. They addressed the lack of a structured assessment method by using expert interviews and DEMATEL to analyze the interdependencies of these indicators. DEMATEL helped determine the most influential factors affecting green maintainability, thereby overcoming data deficiency challenges. However, the study faced limitations such as respondent fatigue due to extensive pairwise comparisons, subjectivity in expert evaluations, and the exclusion of external influencing factors. Desbalo et al. [95] developed a maturity model using fuzzy-DEMATEL to assess building maintenance practices, addressing the lack of structured FM in Ethiopia. The study identified key influencing factors, with culture and leadership being the most critical. DEMATEL helped analyze interdependencies, but limitations included weight accuracy issues. They suggested that future research can focus on refining weight determination and performance evaluation.

ELimination and Choice Expressing REality (ELECTRE)

ELimination and Choice Expressing REality (ELECTRE) is developed by Roy in 1968 [44,45,144] using outranking to evaluate and rank alternatives by eliminating less attractive options. Due to conflicts and incomplete comparisons, ELECTRE II and III improved accuracy and applicability. They do not require attribute independence and typically convert qualitative data to quantitative for comparison, making them versatile for complex decisions. It has been utilized in FM and building maintenance for HVAC system selection, energy retrofitting, and renovation decisions by integrating simulation, and probabilistic analysis. Avgelis & Papadopoulos [145] tackled the challenge of choosing optimal HVAC systems by developing a method that blends building simulation with multi-criteria decision making. They coupled Transient System Simulation Tool (TRNSYS) and multizone airflow model (COMIS) to simulate system performance, while employing ELECTRE III to rank different design scenarios based on factors like energy use, economic viability, user comfort, and environmental impact. The study by Baseer et al. [146] introduced the pELECTRE Tri method, to address the complexities of energy retrofitting in buildings, incorporating ELECTRE Tri, probability distributions, and Monte Carlo simulations. It applied this method to evaluate energy renovation alternatives for social housing, offering a more robust, probabilistic categorization of alternatives and enhancing decision-making by accounting for uncertainties. The key limitation of the study is the complexity of understanding and managing probability distributions, which may challenge decision-makers and influence results.

Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)

Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) is introduced by Brans et al. [48,147] and it is a widely used multi-criteria decision-making (MCDM) method offering flexible ranking options. PROMETHEE I provides partial rankings, PROMETHEE II complete rankings, and PROMETHEE III interval-based rankings. It accommodates both qualitative and quantitative criteria without assuming independence among them, employing six preference functions to capture diverse decision-maker preferences. In FM and building maintenance, PROMETHEE has been applied to decision areas such as inspection policy design and thermal renovation solutions, balancing cost, performance, and stakeholder preferences. For example, Seddiki et al. [100] combined the Delphi method for criteria selection, the Swing method for weighting, and PROMETHEE for consensus ranking to evaluate renovation options in Algerian masonry buildings. Their study identified exterior insulation and double-glazing as top solutions but highlighted limitations including the reliance on predefined renovation alternatives and the lack of uncertainty handling, indicating areas for further improvement. Similarly, Cavalcante et al. [148] applied PROMETHEE II to optimize inspection policies for residential buildings under warranty, considering cost, downtime, and performance. Their findings recommended inspections every four months; however, challenges remain in tailoring policies to diverse client goals and minimizing disruption in multi-apartment buildings, suggesting the need for enhanced model adaptability.

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is introduced by Hwang et al. in 1981 [50] and it ranks alternatives by identifying the option closest to the positive ideal solution and farthest from the negative one. It identifies the best option by comparing how close each alternative is to an ideal solution, considering both benefits and costs. The method can handle different types of measurable criteria and does not require the preferences between them to be independent. TOPSIS has been extensively applied in FM and building maintenance to address problems such as energy retrofit selection, HVAC maintenance prioritization, and sustainability planning. The method is valued for its ability to manage multiple, often conflicting, criteria and provide clear, structured rankings that support decision-making. Several studies have modified and extended the classical TOPSIS to improve its flexibility and accuracy. Carli et al. in 2017 [149] developed a dual-stage model combining multi-objective optimization with TOPSIS to assess building energy retrofit scenarios. El-Rayes et al. in 2021 [150] proposed a Rank-Weight-Rank algorithm for solar panel selection, which improved speed and accuracy over traditional TOPSIS, especially when dealing with a larger number of criteria. In 2022, Al-Salihat et al. [151] applied fuzzy TOPSIS to heritage building maintenance in Canada, integrating physical and social criteria across 16 expert-defined attributes to support sustainable decision-making. Okoro et al. in 2024 [152] introduced a dynamic HVAC maintenance framework that combines fuzzy TOPSIS and failure mode and effects analysis to adapt to changing system conditions and enhance reliability.

Other enhancements include combining TOPSIS with optimization or predictive tools to improve real-time adaptability. For example, Dai et al. in 2024 [153] employed a multi-objective scheduling model integrated with TOPSIS to select energy sources in green building systems, focusing on maximizing energy efficiency and minimizing costs. While TOPSIS is advantageous for its simplicity and effectiveness, it has limitations. These include sensitivity to weighting methods, reliance on expert judgment, and difficulties in handling qualitative data or inconsistent scales [151,153]. Researchers have responded by developing hybrid models that combine TOPSIS with fuzzy logic, expert systems, or optimization algorithms, making the method more robust for practical applications in complex FM contexts.

Weighted Aggregated Sum Product Assessment (WASPAS)

Weighted Aggregated Sum Product Assessment (WASPAS) is developed by Zavadskas et al. in 2012 [53], and it combines weighted sum and product models to rank alternatives using additive and multiplicative criteria. It handles trade-offs, requires independent attributes, and converts qualitative data to quantitative. In FM and building maintenance, WASPAS has been applied for selecting maintenance strategies, evaluating redevelopment options, and ranking contractors. An advantage of WASPAS is its integrated additive–multiplicative framework, ensuring computational efficiency and reliable rankings [154]. However, a disadvantage is its reliance on precise quantitative data, which limits its effectiveness in handling uncertainty or qualitative assessments. To address this, some studies have combined WASPAS with grey theory to improve its performance in uncertain environments. Pavlovskis et al. [89,90] used WASPAS-G and BIM to evaluate sustainable redevelopment options for former and old industrial buildings, with the “G” representing the grey system, which allowed for better handling of uncertainty in decision-making by refining the assessment of economic, environmental, and technological criteria. Their findings demonstrated that preserving facades and adopting sustainable methods improved efficiency and reduced environmental impact, with BIM supporting execution and lifecycle management. However, they identified challenges in fully integrating BIM, particularly for new constructions, and highlighted the need for broader applications, including GIS and scanning, as well as further research to refine BIM workflows and lifecycle strategies in redevelopment projects.

Choosing by Advantages (CBA)

Choosing by Advantages (CBA) is developed by Suhr in 1999 [41] as a sound decision-making method that evaluates decision alternatives based on their advantages, rather than relying on pairwise comparisons or weighted criteria. In a study by Besiktepe et al. in 2020 [80], key criteria for building maintenance, such as health and safety, code compliance, and condition, were found to be more important than cost. In another study in 2021 Besiktepe et al. [1] applied CBA to building maintenance decisions, evaluating alternatives like repair, replacement, or doing nothing, while considering cost only as a constraint after other factors were assessed. However, the approach had limitations, including its reliance on a hypothetical case study with only one facility management professional, and potential subjectivity due to the lack of weights or pairwise comparisons.

Further, Besiktepe et al. in 2023 [155] reviewed the application of CBA in FM, identifying limitations such as the inadequacy of the Importance of Alternatives (IoA) scale, stakeholder bias, and the absence of weighting in decision factors. Despite these drawbacks, they highlighted CBA’s potential in areas like building maintenance, project planning, and resource allocation. Building on this body of work, Anbari Moghadam and Besiktepe [156] enhanced CBA by integrating Z-numbers, an extension of fuzzy numbers [157], with the Delphi method to reduce subjectivity in the Importance of Advantage scale, thereby improving consensus and managing uncertainty in building maintenance strategy selection.

VIšekriterijumsko KOmpromisno Rangiranje (VIKOR), Stepwise Weight Assessment Ratio Analysis (SWARA), and Best–Worst Method (BWM)

In the evolving landscape of decision-making within FM and building maintenance, modern MCDM techniques such as the SWARA, VIKOR, and the BWM have emerged as flexible and robust alternatives to more traditional approaches. The SWARA method, introduced by Keršuliene et al. in 2010 [49], is grounded in expert-driven evaluation. It involves a structured process where decision-makers sequentially rank criteria, assess their relative importance, and derive weights based on a set of significance coefficients. What makes SWARA especially appealing is its capacity to handle compensatory and independent attributes, which broadens its applicability across various domains including supplier selection, research project prioritization, and facility location planning. In the FM context, Kheradranjbar et al. in 2023 [97] combined the Delphi method with SWARA to identify and rank key building maintenance criteria. Their findings emphasized safety, health, and accessibility as the top-ranked considerations. The study further integrated fuzzy and non-fuzzy MCDM techniques to evaluate maintenance strategies, ultimately identifying Corrective and Breakdown Maintenance as the most appropriate under prevailing conditions. However, the authors noted a critical limitation: limited stakeholder knowledge significantly hindered data collection and practical implementation of the results.

The VIKOR method, introduced by Opricovic in 1998 [51] and extended by Opricovic and Tzeng in 2004 [52], is designed to support decisions involving conflicting criteria by identifying a compromise solution that is closest to the ideal. VIKOR operates on a compensatory foundation and assumes criteria independence, requiring the transformation of qualitative data into quantitative scales. Its simplicity and effectiveness have made it a popular choice in applications such as logistics, urban planning, and supplier selection. In FM and building maintenance, VIKOR is often employed as part of a hybrid methodology. For example, in a study by Kheradranjbar et al. in 2022 [96], the authors first applied the Delphi method and SWARA to identify and prioritize maintenance criteria for residential buildings. VIKOR was then used to rank the best repair strategies. The results once again pointed to Breakdown and Corrective Maintenance as optimal solutions, reaffirming the importance of safety in maintenance decision-making. The study acknowledged limitations related to the insufficient expertise among maintenance professionals and called for future research using fuzzy techniques to enhance accuracy.

BWM, proposed by Rezaei in 2015 [40], offers a streamlined approach to criteria weighting by requiring decision-makers to compare the most and least important criteria against all others. This method reduces inconsistency and cognitive burden while maintaining robust analytical output. In FM research, Olimpio et al. [158] employed a Bayesian extension of BWM to support decision-making for the preventive maintenance and conservation of built heritage in Sobral, Brazil. Their model prioritized criteria such as social contribution and historical significance over structural vulnerability, thus offering valuable guidance for public policy formulation. Nonetheless, they highlighted challenges in building and validating the hierarchical structure of criteria and the inherent subjectivity involved in value-driven decisions.

A more recent application by Gao et al. [83] illustrates the potential of hybrid MCDM models. In their study, the authors integrated BWM with VIKOR under a Fermatean fuzzy environment to select healthcare waste treatment technologies. This model introduced innovative entropy and distance measures for fuzzy sets and utilized hybrid weighting schemes to evaluate alternatives. Applied in a case study, the model demonstrated improved decision reliability and performance compared to conventional methods. However, the study was limited by its small expert panel and acknowledged the need for broader applicability through inclusion of additional MCDM techniques. These studies demonstrate how modern MCDM methods, individually and in hybrid forms, are being adapted to the complex, multidimensional challenges in FM. While each method offers distinct strengths, their successful application depends heavily on expert input, appropriate methodological integration, and sensitivity to contextual limitations.

Other MCDM Methods

While most of the 107 reviewed studies employed well-established MCDM methods, a smaller group introduced less conventional or novel applications of traditional approaches. These studies, though less frequent, offer valuable insights into the flexibility of decision-making in FM and building maintenance, particularly in highly contextual or constrained environments. Some can still be classified as MCDM methods because they involve structured evaluation of multiple criteria, while others do not fully meet the formal definition but still provide useful decision-support insights, especially in situations involving uncertainty or qualitative judgment. This methodological variety reflects the evolving nature of decision-making in FM, where both established and emerging approaches contribute to practice. For instance, in 2005, Zavadskas et al. [159] explored contractor selection under conditions of incomplete information, a common challenge in real-world FM scenarios. Rather than adopting standard MCDM tools, the authors turned to game theory, applying Wald’s and Bayes’s rules to evaluate alternatives. This approach reflects an early attempt to accommodate uncertainty in FM decision-making. However, its transferability remains limited, as the contextual specificity and the subjective nature of stakeholder preferences reduce its broader applicability.

Similarly, Yau in 2012 [160] tackled decision-making in the unique context of aging residential towers in Hong Kong, introducing a nonstructural fuzzy decision support system to capture homeowner perceptions of maintenance priorities. While innovative in its effort to model ambiguity in human judgment, the study’s scope, limited to a single district, raises questions about regional generalizability, a common challenge when applying fuzzy logic in localized urban environments. Game theory surfaced again in the work of Tamošaitienė et al. in 2013 [161], this time to assess FM service providers for commercial properties under dynamic economic conditions. Their method emphasized both performance and cost factors, yet the analysis was potentially weakened by the inherent subjectivity of evaluation criteria and risks tied to data accuracy. Despite these limitations, the study underscores the potential for strategic decision models to reflect market-driven FM service environments. In contrast, Bucoń and Sobotka in 2015 [162] focused on developing a practical, step-by-step framework for selecting building repair strategies. Their model integrated technical, energy, and functional considerations while accounting for budgetary constraints, an important advancement for maintenance planning in resource-constrained settings. However, the complexity of the model, particularly in synthesizing a wide range of influencing factors, presents a barrier to adoption without simplification or computational support.

From a sustainability standpoint, Khadra et al. in 2020 [82] advanced a mathematical model for selecting energy-efficient renovation options by applying the Weighted Sum Method (WSM) across environmental, economic, and social criteria. This attempt at holistic evaluation reflects a growing trend in FM towards multi-dimensional sustainability assessment. Yet, the model’s lack of methodological guidance on weight assignment may limit its consistency and replicability across different project types. Finally, in 2021 Zavadskas et al. [163] proposed a hybrid model integrating fuzzy logic and a Delphi-Eckenrode-based Likert-type scale to evaluate municipal building efficiency. Their CoCoSo approach aimed to handle fuzzy social and sustainability-driven objectives. While promising, the model’s heavy reliance on expert judgment and the absence of a fully structured framework for municipal building portfolios highlight the persistent challenge of operationalizing complex decision environments in public-sector FM.

Hybrid MCDM Approaches

In recent years, there has been a growing trend in the adoption of hybrid MCDM approaches to tackle the challenges in FM and Building Maintenance, as illustrated in

Table 5. Hybrid MCDM approaches utilized in FM and Building Maintenance.

Reference	Methods	Research Focus	Limitation
[164]	WSM, WPM, AHP, Revised AHP, TOPSIS, and COPRAS	Evaluated dwelling maintenance methods in Lithuania using MCDM methods to determine the most effective facility management approach.	Limited FM specialists, weak legislative framework, lack of standardized procedures, and absence of a control system.
[165]	SAW, MEW, COPRAS, AHP	Identified the most cost-effective energy-efficient renovation measures for Swedish apartment buildings from the 1950s–70s.	Results heavily depend on building owners’ preferences, especially on short payback periods, and there is a need for better awareness of energy consumption issues
[166]	AHP, ARAS.	Prioritized the preservation and restoration of Vilnius Old Town’s cultural heritage buildings using MCDM.	Balancing diverse stakeholder interests, handling complex documentation, and integrating cultural, public, and financial considerations.
[167]	Fuzzy AHP, Fuzzy ANP, DEMATEL	Evaluated and prioritized performance indicators for Taipei City Sports Centers to help managers optimize business strategies.	Potential oversight of key criteria, discrepancies in staff quality importance, limited generalizability.
[75]	AHP, ANP, Multi-Attribute Utility Theory (MAUT).	Developed a space-based condition assessment model for buildings, applied to an educational facility in Montreal, to enhance asset management and maintenance decision-making.	Potential biases in survey responses, challenges in model generalizability, dependence on data accuracy.
[168]	DEMATEL, ANP, VIKOR.	Developed an integrated MCDM model to evaluate and select Intelligent Building Management Systems for smart factories under Industry 4.0, considering technological, policy, product, and financial criteria.	Reliance on empirical results, potential challenges in practical implementation, and possible oversight of other critical evaluation factors.
[169]	Fuzzy TOPSIS, ARAS-F, Fuzzy WPM	Developed an integrated fuzzy MCDM model for selecting facilities management strategies under uncertain conditions, incorporating stakeholder preferences.	Results may vary based on initial data type, stakeholder aims, and decision-makers’ preferences. Differences in outcomes can arise from assumptions made during the process.
[170]	SWARA, WASPAS, FAD, ARAS	Developed a MCDM framework to select an appropriate maintenance strategy for public buildings based on sustainability criteria.	The study is limited by the specific methods and interactions between criteria used, with differences in strategy rankings, particularly in terms of economic sustainability.
[171]	AHP, TOPSIS, ELECTRE III, PROMETHEE II	The study compares and applies four MCDM methods to select the optimal building design, providing a holistic decision-making framework incorporating stakeholder preferences and dynamic simulation.	Uncertainties in Building Performance Simulation, subjective stakeholder preferences, and complexity in ELECTRE III and PROMETHEE II due to threshold and preference function selection.
[172]	AHP, PROMETHEE	Contractor selection for the renovation of cultural heritage buildings.	The PROMETHEE method used a “zero” value for some criteria in the algorithms, which may limit the accuracy of the evaluation and decision-making process.
[173]	ARAS, VIKOR, MOORA, COPRAS, WASPAS, SWARA, PROMETHEE II	Contractor selection for building maintenance using 7 MCDM methods and ANN to identify the best method.	Sensitivity to input weights and discrepancies between MCDM methods.
[154]	AHP, TOPSIS, EDAS, PROMETHEE, CRITIC, WASPAS	A comprehensive analysis of MCDM methods has been conducted to evaluate renewable energy technologies in households, categorizing evaluation purposes and criteria for informed decision-making.	Challenges include interdependencies in AHP, indicator variations in TOPSIS, compensatory assumptions in EDAS, and computational complexity in PROMETHEE, limiting expert accessibility.
[174]	ANP, MAUT	Enhancing the prioritization of hospital building assets.	Limited to Canada; needs global validation, more criteria, and alternative algorithms.
[175]	AHP, WASPAS, COPRAS	Developed a comprehensive MCDM model for sustainable municipal building management to rank investment alternatives and optimize decision-making.	Implementation challenges include the need for expert assessments, potential resource constraints, and adapting the model to different municipal contexts.
[176]	Modified SWARA, WASPAS	Developed a hybrid building energy simulation integrated MCDM framework for selecting the most suitable HVAC system for industrial buildings.	Dependency on expert judgments, location-specific HVAC suitability, and potential adaptation difficulties for different industrial contexts.
[177]	AHP, MAUT	Developed a systematic Performance Assessment Model to evaluate mosque facility conditions in Saudi Arabia, optimizing maintenance and management practices.	Findings are specific to mosques in Saudi Arabia, subjectivity in expert input, and limited generalization to other facility types.
[178]	AHP, TOPSIS	A multi-criteria assessment of window retrofitting alternatives in tropical climates to optimize energy consumption and environmental sustainability in buildings.	The study did not consider heating performance or recycling/reuse in the demolition stage.
[179]	AHP, PROMETHEE	Addresses the gap in healthcare FM by identifying and prioritizing key performance indicators using business intelligence and analytics to enable data-driven performance analysis.	The study is based on data from Turkey, with comparisons to China and Hong Kong involving a limited number of KPIs. Regional variations in FM practices may affect the findings.
[96]	SWARA, VIKOR	Determining and prioritizing effective criteria for building repair and maintenance and selecting appropriate maintenance strategies to improve building efficiency, reduce costs, and increase longevity.	Lack of knowledge among individuals involved in building R&M, which hindered collaboration and cooperation.
[98]	DANP (DEMATEL + ANP)	Identification and prioritization of key indicators for building repair and maintenance.	Lack of prior research on building repair and maintenance indices and the identified criteria may differ from those used in other industries.
[180]	interval-valued intuitionistic fuzzy (IVIF) DEMATEL, IVIF ANP	The study identifies and prioritizes critical causal factors to improve Occupational Health and Safety in the Repair, Maintenance, Minor Alteration, and Addition sector.	Sample size was limited due to the COVID-19 pandemic, and it relied on experts from Hong Kong, which could limit the generalizability of the results to other regions.
[85]	ANP, ELECTRE IS	Simplified sustainability decision-making in construction using ANP & ELECTRE IS for quantitative variables.	Increased complexity, prolonged expert intervention, and subjectivity in judgments.
[181]	CRITIC, TOPSIS	Developed a methodology for multi-criteria assessment of household energy systems, considering climate, energy supply, and HVAC systems.	Does not include value added tax, system accessories, or installation costs in LCCA. National subsidies and feed-in from photovoltaic generation are also excluded.
[92]	AHP, TOPSIS	Proposed a framework for evaluating retrofit strategies to enhance the operational performance of mosque buildings, focusing on energy efficiency and comfort.	Limited to a single mosque case study, which may not be representative of all mosques. Some retrofit strategies may not be applicable to different mosque types.
[182]	Fuzzy VISIS (VIkor-topSIS)	Identified and prioritized marketing strategies for Building Energy Management Systems to boost market penetration and sales.	Case-specific; the identified strategies and rankings may not apply universally. Requires adaptation of criteria based on different contexts.
[84]	BWM + TOPSIS	Evaluated and ranked HVAC systems for sustainable office buildings to guide the design of energy-efficient and healthy indoor environments.	Conflicting criteria; the need for additional criteria and a larger sample size for more comprehensive results.
[183]	Pythagorean Fuzzy-AHP, IVPF-AHP d, IVPF-AHP p	Developed a scoring model to assess the “smartness” of public buildings, considering indicators such as green building construction, energy management, and occupant comfort.	Variations in perceptions of smartness among participants; adaptation required for different contexts.
[83]	Fermatean fuzzy BWM-VIKOR	Developed an integrated MCDM approach for selecting healthcare waste treatment technologies, addressing the complexity and uncertainty of decision-making in medical waste management.	A small expert group in the case study may affect result reliability; reliance on VIKOR method limits exploration of other MCDM methods.
[184]	TOPSIS, VIKOR, WASPAS, MULTIMOORA	Proposed a robust multi-criteria decision-making framework to select thermal insulation materials for building energy retrofitting, considering conflicting stakeholder interests and ensuring robustness in ranking results.	Limited consideration of variability in performance for different climates or building types, and challenges in achieving full compromise among stakeholders.

. These approaches combine multiple MCDM methods, each chosen for its ability to address specific aspects of the problem, thereby capitalizing on the strengths of each while compensating for the limitations of individual methods. This results in more informed and reliable decision-making. For instance, methods like ANP are particularly useful for capturing interdependencies and feedback among criteria, something that traditional hierarchical methods like AHP may miss. Hybrid methods, such as combining DEMATEL with ANP, allow decision-makers to derive weightings that reflect the relationships and influences between criteria, leading to a more thorough and nuanced evaluation.

Table 5 also includes studies that separately apply multiple MCDM methods and compare their results, aiming to make more informed decisions and address the limitations of each method through direct comparison. Despite the benefits of hybrid approaches, many studies emphasize the importance of regional specificity and stakeholder-driven decision-making. These factors often reflect local policies, cultural heritage, and economic constraints [164,165,166,167,175,176,177]. Additionally, challenges such as data limitations and expert subjectivity remain significant, highlighting the difficulty of standardizing MCDM applications across different contexts [75,83,168,169,170,171,172,176]

4.2.3. Future Research Directions

Based on the analysis of 107 relevant studies, this section highlights key gaps and challenges identified in existing literature concerning the use of MCDM techniques in FM and Building Maintenance. These limitations range from methodological issues and data uncertainty to practical barriers in real-world implementation. In response, this section outlines future research directions aimed at overcoming these obstacles and improving the effectiveness of MCDM approaches in the field. The suggestions are organized into three focus areas: addressing methodological limitations specific to FM and maintenance contexts, minimizing subjectivity and uncertainty in expert-based decision-making, and enhancing the usability and practical integration of MCDM methods in evolving technological environments.

Addressing Limitations in MCDM Techniques Applied to FM and Building Maintenance

There are several emerging MCDM methods whose potential has yet to be fully explored in the context of FM and Building Maintenance. Some of these methods offer features that could effectively address common challenges identified in literature, such as incomplete or uncertain data [96,159]. One such method is the Ordinal Priority Approach (OPA), introduced by Ataei et al. in 2020 [185]. OPA offers a novel solution for both individual and group decision-making by eliminating the need for conventional tools like pairwise comparison matrices, numerical decision matrices, normalization procedures, and averaging techniques for aggregating expert opinions. Instead, it leverages ordinal data and simple comparisons to streamline the decision-making process. One of the key strengths of OPA is its ability to handle incomplete input, allowing experts to contribute only where they have relevant knowledge. This not only makes the process more efficient but also more adaptable to real-world decision-making, particularly in group settings where expertise may be distributed unevenly

The KEMIRA method represents another promising yet underexplored MCDM approach in the context of FM and building maintenance. A potential future direction involves integrating KEMIRA with the Choosing by Advantages method to enhance the robustness of maintenance-related decision-making. While CBA is valued for its transparent, advantage-based evaluation of alternatives, it lacks a formal mechanism for assigning weights to decision criteria, which can introduce subjectivity and hinder scalability [1]. By incorporating KEMIRA’s systematic approach to attribute prioritization and weight estimation, this hybrid model could offer a more balanced and data-driven framework. Such integration would retain the practical strengths of CBA while improving its analytical rigor, allowing for more objective comparisons across maintenance strategies.

Future research should investigate the applicability of this combined approach through empirical studies, particularly in scenarios where structured yet flexible decision support is essential for effective facility management. Real-time and dynamic MCDM approaches also can be useful in FM and building maintenance [78,117] by adapting to changing priorities and conditions, combining historical data with future insights like expert predictions or expected trends, these approaches help make smarter decisions, improve maintenance planning, optimize resource use, and reduce long-term costs, much like how supplier selection models use both past performance and future expectations to find the best alternatives, dynamic MCDM in building maintenance can minimize risks and boost efficiency by considering both current and future factors.

Minimizing Subjectivity, Uncertainty, and Unreliability

Many of the identified studies introduced uncertainty and subjectivity in expert judgment as a main limitation for their MCDM method for various reasons [78,79,85,109]. To address this issue, studies have applied theories designed to minimize subjectivity. One such theory is fuzzy logic [86], which has proven to be a valuable tool for managing subjectivity and uncertainty in MCDM, especially in fields where expert opinion is essential [119,135]. By expressing human judgments in qualitative terms such as “moderately high” or “very high,” fuzzy logic captures the inherent vagueness of expert input. These linguistic terms are then mathematically modeled using fuzzy sets and membership functions, which assign degrees of membership on a continuous scale (e.g., between 0 and 1). This process transforms vague qualitative input into structured data, enabling a more nuanced and realistic representation of uncertainty in decision-making. Methods like Fuzzy AHP and Fuzzy TOPSIS have demonstrated their effectiveness for imprecise or incomplete data, thereby reducing the impact of subjective bias on decision-making outcomes.

Building on fuzzy logic, Z-numbers, introduced by Zadeh in 2011 [157], offer an advanced method for handling uncertainty. Unlike traditional fuzzy numbers, Z-numbers incorporate an additional component that represents the reliability or confidence level of an estimate. This added dimension helps distinguish between high-confidence and low-confidence expert opinions, which is particularly useful in high-uncertainty environments. By reducing the impact of unreliable inputs, Z-numbers can enhance decision-making in contexts with significant uncertainty. However, despite their potential, Z-number-based MCDM has been explored only to a limited extent in the field of FM and building maintenance [156]. Furthermore, Grey System Theory [88], which is powerful for handling incomplete, uncertain, and small datasets, has seen limited application in FM and building maintenance. Grey theory excels when decision-making relies on limited or ambiguous information. A future direction for MCDM in FM is to integrate Grey Systems Theory with the Ordinal Priority Approach. This Method addresses OPA’s limitations in handling multiple ranks and uncertainty by using grey theory to manage uncertainty without complex membership functions. Proven effective [186], OPA-G can possibly offer a robust, flexible, and reliable decision-making framework, ideal for real-world, uncertain scenarios in FM and building maintenance.

Enhancing the Practical Implementation of MCDM Methods

When selecting an MCDM method for use in a case study or real-world application, it is crucial that the method is both understandable and easy for experts to apply. However, several studies identify the complexity of implementation as a significant challenge, sometimes even influencing the choice of method [1,114]. This challenge even becomes more noticeable when researchers want to combine their methods with new technologies such as BIM [103], a topic that continues to evolve.

In addition, Siksnelyte-Butkiene et al. [154] encourage researchers to focus on experts’ qualifications and Vilutienė & Zavadskas [164] highlight the shortage of experienced facility managers in certain regions, further complicating the application of complex decision-making methods. Based on the data presented in Table 4, 50 studies employed the AHP as their preferred MCDM method, likely due to its intuitive structure and the widespread availability of user-friendly software that facilitates its implementation. Nonetheless, ease of implementation should not be the sole criterion for method selection. The strengths and limitations of each approach must be carefully weighed against the specific problem context to support more informed decision-making. Future research could address these challenges by first identifying the most suitable MCDM methods for the problem at hand, followed by selecting those that balance ease of use with methodological rigor. This process might involve employing questionnaires, statistical analyses, and other relevant research techniques. Moreover, providing targeted training for experts is essential, particularly when integrating MCDM methods with new technologies. Researchers should also consider assigning weights to expert opinions based on their experience and judgment reliability. Incorporating approaches such as Z-numbers, which allow experts to express their confidence levels in their judgments, can help reduce bias and uncertainty.

AI and Decision Making in FM and Building Maintenance

AI and Machine Learning (ML) algorithms offer considerable potential to MCDM in FM and building maintenance by automating data processing and generating predictive insights. However, as Jayasena et al. [130] and Salem et al. [78] emphasize, the value of such technologies depends on their usability and alignment with end-user needs. In FM, where decisions often involve interdisciplinary trade-offs under uncertainty, transparent and interpretable AI models are critical. Integrating Explainable AI (XAI) into MCDM frameworks can improve trust and adoption by enabling practitioners to understand the rationale behind recommendations.

Current applications of AI in FM are constrained by data sparsity, labeling demands, and the opaque nature of deep learning systems [26,187]. These limitations highlight the need for hybrid models that combine domain knowledge with machine learning to ensure robustness, generalizability, and contextual relevance. AI-enhanced MCDM methods can support dynamic risk assessment, fault detection, and optimal resource allocation while addressing bias and uncertainty. Lessons from the financial sector, where the integration of AI with MCDM frameworks has improved decision reliability, offer valuable insights for FM applications [188]. Future research should also examine human-AI collaboration, develop intuitive interfaces, and embed ethical and context-aware decision support systems that align with the workflows and cognitive patterns of facility managers.

A promising research direction involves the development of user-friendly AI-powered platforms capable of selecting the most appropriate MCDM method based on expert-defined criteria and FM-specific contexts. In such systems, users would describe the decision problem, define relevant factors and alternatives, and rely on the platform to recommend and execute a suitable method using context-sensitive weightings. By incorporating explainable logic, expert knowledge, and adaptive learning capabilities, these platforms can make advanced decision support tools accessible to non-specialists while ensuring methodological rigor and transparency. As FM environments grow in complexity, such intelligent systems could play a critical role in supporting effective and scalable maintenance strategy selection.

4.2.4. MCDM Selection Matrix for FM and Building Maintenance

The MCDM Method Selection Matrix for FM and Building Maintenance, presented in Figure 5, was developed to guide practitioners in selecting appropriate decision-making methods for a wide range of FM-related challenges. Drawing on an analysis of 107 published studies, the matrix not only identifies which MCDM methods have been applied in specific FM contexts but also recommends suitable techniques based on their inherent characteristics. By doing so, it streamlines the selection process and highlights methods that are both widely adopted and particularly well suited to distinct types of decisions. For instance, the Analytic Hierarchy Process (AHP) is among the most frequently employed methods, with applications in maintenance prioritization, procurement strategy, contractor evaluation, energy retrofit selection, and healthcare planning. It is particularly valued for its ability to structure complex problems and facilitate consistent comparison of criteria.

Ranking-based approaches such as TOPSIS and PROMETHEE are frequently applied to problems that require performance-based prioritization, including equipment selection, maintenance strategies, and energy retrofits. VIKOR is often chosen for contexts involving trade-offs between conflicting objectives, such as healthcare planning, contractor selection, and strategic decision-making. Similarly, MOORA and COPRAS have been employed where multiple performance indicators must be evaluated simultaneously. The Analytic Network Process (ANP) is especially useful when decision criteria are interdependent, as in sustainability performance evaluations and maintenance strategy selection, while DEMATEL, which identifies causal relationships among criteria, typically serves as a supporting tool in combination with ANP or AHP rather than as a primary decision method.

Other methods address more specialized contexts. KEMIRA and ELECTRE are applied in policy-driven or strategic planning decisions, such as energy retrofitting, where ranking-based approaches alone may be insufficient. Methods such as WASPAS and ARAS, when combined with fuzzy logic, are particularly effective when both qualitative and quantitative data must be integrated, especially under uncertain or limited information, with common applications in contractor evaluation, strategy development, and sustainability assessment. The base WASPAS and ARAS methods are purely quantitative, and any qualitative information must be converted to numerical values before evaluation. OPA and CBA similarly contribute to contractor selection and maintenance prioritization, offering structured and transparent evaluations

The development of the MCDM Method Selection Matrix (Figure 5) constitutes a significant step toward bridging theory and practice in FM and building maintenance decision-making. By systematically mapping methods to specific problem domains using evidence from 107 published studies, the matrix provides both descriptive insights into existing methodological applications and prescriptive guidance for practitioners. Its primary value lies in simplifying the complex process of method selection, enabling decision-makers to align methodological capabilities with the unique characteristics of FM-related problems. The frequent application of AHP highlights its robustness and usability in structuring complex decision problems and facilitating consistent comparisons across criteria, while the roles of TOPSIS, PROMETHEE, and VIKOR highlight the utility of ranking-based approaches for contexts requiring performance evaluation, prioritization, and trade-off analysis. Moving beyond common approaches, the use of contextually valuable yet less widely applied methods broaden the range of MCDM options available to FM practitioners. For example, MOORA is effective for simultaneously evaluating multiple performance indicators, CBA and OPA offer structured frameworks for contractor selection and maintenance prioritization, and KEMIRA or ELECTRE are well suited for complex, policy-driven or strategic planning decisions. Beyond cataloging past applications, the matrix offers a practical reference framework that enhances transparency, comparability, and methodological rigor in FM decision-making. In doing so, it fosters more consistent, evidence-based decisions while advancing sustainable and cost-effective outcomes in the built environment.

5. Conclusions

This study sets out to provide structured guidance on the application of MCDM methods in FM and building maintenance, addressing a critical gap in method selection for complex decision-making problems. Through a systematic literature review guided by the PRISMA framework and supported by scientometric analysis of 107 studies, the research synthesized current trends, categorized widely used methods, and developed the Contextual MCDM Method Selection Matrix. This framework links problem-specific FM contexts to suitable MCDM techniques, thereby bridging theory and practice while supporting more consistent, evidence-based decision-making.

The major contribution of the study lies in offering both descriptive and prescriptive insights. On one hand, the scientometric analysis provides a comprehensive overview of the evolution of MCDM applications in FM and building maintenance, highlighting dominant methods such as AHP, TOPSIS, PROMETHEE, and VIKOR, while also recognizing the potential of less widely applied techniques like CBA, OPA, KEMIRA, and ELECTRE. On the other hand, the Method Selection Matrix translates these findings into a practical tool that helps practitioners align methodological strengths with the specific requirements of FM problems. In doing so, the study advances methodological rigor, enhances transparency, and promotes efficiency and sustainability in FM decision-making.

Alongside these contributions, the study also acknowledges certain areas that present opportunities for further improvement and exploration as well as limitations. Empirical testing of the Method Selection Matrix in real-world FM and building maintenance projects would strengthen its practical validity and adaptability. The review draws primarily on peer-reviewed publications indexed in major databases such as Scopus and Web of Science, ensuring a high level of quality and reliability in the analyzed studies. To further strengthen coverage, supplementary searches were conducted across key academic publisher platforms (e.g., ASCE Library, SpringerLink, Elsevier’s ScienceDirect) and ResearchGate, complemented by citation tracking through backward snowballing, which helped capture additional relevant records. Even with these efforts, studies from less accessible platforms may not have been fully captured. While some gray literature, industry reports, or studies from less accessible platforms may not have been included, this focus enhances the methodological rigor and transparency of the review, providing a robust foundation for future research to expand into these complementary sources.

The rapid transformation of FM and building maintenance highlights the need for future research that advances both methodological and technological frontiers. Further research could also explore integrating emerging technologies, such as AI, digital twins, and IoT-enabled predictive systems, into MCDM frameworks to enhance data-driven decision-making. Additionally, cross-disciplinary studies involving behavioral science and organizational management may help reduce subjectivity in expert-driven models while addressing the social dimensions of FM decisions. Expanding hybrid approaches that combine quantitative rigor with qualitative stakeholder perspectives also represent a promising avenue for advancing decision-support systems. Greater exploration of underutilized MCDM methods, such as OPA and KEMIRA, is warranted, as these approaches offer potential advantages in managing incomplete data and supporting systematic attribute weighting.

In summary, this study consolidates existing knowledge on MCDM applications in FM and building maintenance and introduces the Contextual MCDM Method Selection Matrix as a practical decision-support tool. While further empirical validation and exploration of emerging methods and technologies remain necessary, the study provides a robust foundation for advancing evidence-based, transparent, and adaptive decision-making that supports more sustainable and efficient outcomes in the built environment.