Decision Support System for Contractor Selection in Integrated Project Delivery (IPD): A Low Carbon, Sustainability-Oriented Model for Urban Infrastructure Projects

Abstract

As cities pursue smarter, more resilient infrastructure, conventional contractor selection focuses narrowly on cost, which often neglects holistic sustainability. This study addresses a critical gap by introducing a novel, sustainability-oriented contractor evaluation model within Integrated Project Delivery (IPD). While IPD enables early collaboration, its integration with structured sustainability metrics remains underutilized. We develop a Multi-Criteria Decision-Making (MCDM) framework that operationalizes the three pillars of sustainability, economic, social, and environmental sustainability, with the inclusion of the technical pillar through an expanded environmental, social, and economic model incorporating sub-criteria such as constructability, workforce competence, collaborative readiness, etc. The innovative inclusion of technical sustainability deepens contractor assessment and enhances alignment with smart urban priorities. The model is embedded in a custom Decision Support System (DSS), combining Fuzzy AHP for weighting and Fuzzy VIKOR for ranking. A real-world IPD scenario, which is a proposed multi-purpose banquet facility, illustrates the tool’s functionality. Nine experts evaluated four contractors across 16 sub-criteria, with the results reflecting structured, priority-weighted decision logic. The DSS offers a transparent, replicable framework for aligning procurement with smart city goals. Its novelty lies in advancing sustainability through sustainability-oriented contractor selection, supporting policymakers, project teams, and cities in meeting integrated infrastructure objectives.

1. Introduction

A Project Delivery Method (PDM) is the framework that defines how a construction project is organized, designed, and executed. It outlines the contractual relationships, roles, and responsibilities among key stakeholders such as owners, designers, and contractors throughout the project lifecycle [1]. The choice of a PDM significantly influences project performance, including cost, schedule, collaboration, risk allocation, and overall quality outcomes. Traditionally, the most widely adopted PDM is the Design–Bid–Build (DBB) method. This linear model separates the design and construction phases, where the owner first contracts a designer, and then, after the design is complete, awards a construction contract to the lowest bidder. While DBB is favored in many public sector projects due to its transparency and role clarity, it has been widely criticized for fostering fragmentation, adversarial relationships, and inefficiencies in project execution [2].

This fragmentation, between design, construction, and operation, is one of the most persistent challenges in the architecture, engineering, and construction (AEC) industry. It often results in teams working in silos, concentrating only on their designated phase and neglecting early-stage coordination, such as design integration and lifecycle performance considerations. As a result, the owner bears significant risk, particularly when design omissions or inefficiencies surface during construction or operation [3]. Fragmented delivery further undermines efforts to optimize sustainability and long-term project outcomes, as it inhibits holistic thinking and system-level innovation.

Recent research indicates that fragmented PDMs frequently contribute to miscommunication, delays, rework, and diminished quality, especially in large or complex urban infrastructure projects [4]. These challenges are particularly problematic in the context of city development, where digital integration, cross-disciplinary collaboration, and sustainability performance are key success drivers. In response, collaborative delivery methods, such as Design–Build (DB), Construction Manager (CM) models, and particularly Integrated Project Delivery (IPD), have gained traction. These methods emphasize early stakeholder engagement, shared objectives, and transparent decision-making processes. Among them, IPD has emerged as a promising PDM for delivering sustainable infrastructure, as it promotes contractor involvement during the design phase, lifecycle-based evaluation, and shared accountability across the project team [5].

However, while IPD offers significant potential to improve sustainability outcomes, the contractor selection process within IPD remains underdeveloped. Most existing selection models are rooted in traditional procurement logic, prioritizing cost or past experience over collaboration readiness, sustainability performance, or technical innovation. This misalignment presents a barrier to achieving smart city goals, especially when sustainability targets and emissions reduction strategies must be embedded from the earliest project stages. To address this gap, the present study proposes a structured Decision Support System (DSS) for contractor selection under IPD, with emphasis on decarbonization. The model integrates Multi-Criteria Decision-Making (MCDM) techniques, specifically Fuzzy AHP and Fuzzy VIKOR, to enable a structured evaluation process based on economic, environmental, social, and technical sustainability factors. By integrating expert knowledge with literature-based criteria, this approach helps overcome the limitations of fragmented procurement and supports better-informed, transparent, and sustainability-aligned decisions in contractor prequalification through Qualification-Based Selection (QBS).

2. Literature Review

2.1. Fragmentation in the Architecture, Engineering, and Construction (AEC) Industry and the Need for Digital Transformation

The rise of specialized domains within architecture, engineering, and construction (AEC) has driven the adoption of modern project delivery methods designed to manage increasingly complex planning, design, and execution demands. As specialization intensifies, so does the segmentation of roles, resulting in systemic fragmentation across the industry. This disjointed structure introduces inefficiencies, coordination failures, and interoperability gaps that diminish project performance [3]. The compartmentalization of responsibilities among stakeholders hampers integrated delivery and stifles collaborative innovation [6].

Fragmented models often lead contractors and subcontractors to focus narrowly on their assigned phases, disregarding early-stage integration and long-term implications. This siloed mindset elevates project risk, particularly when latent design flaws emerge during construction or operations. As noted by [6], such fragmentation is compounded by structural barriers, misaligned incentives, unfamiliarity with risk-sharing mechanisms, and resistance to collaborative norms, despite the growing appeal of Integrated Project Delivery (IPD) in North America.

Figure 1 presents a structured conceptual framework of the primary evaluation areas influencing built environment project outcomes, highlighting the central role of technical integrity and hydrological resource use alongside interrelated performance dimensions spanning safety, cost–schedule efficiency, environmental state, resource consumption, waste generation, and human capital effectiveness. As cities pursue ambitious development goals and net-zero mandates, the limitations of traditional delivery models have become more than procedural; they are strategic liabilities. The inability of these frameworks to support real-time, cross-functional coordination undermines both innovation and sustainability. Therefore, overcoming these limitations requires a fundamental recalibration of project delivery, one that embeds digital integration, transparency, and shared accountability from the earliest project stages [1,4].

Figure 1. Primary Evaluation Areas for Built Environment Project Outcomes.

2.2. Implementation and Experiences in Collaborative Project Delivery Methods

Collaboration in construction has become essential, evolving into both synchronous and asynchronous forms. Synchronous collaboration has leveraged technologies, such as video conferencing and instant messaging, while newer tools, like 3D virtual and mixed reality environments, further enable real-time communication. These technologies foster construction innovation by supporting relationship building and the development of unique capabilities across firms [7]. External collaborations, in particular, play a critical role in driving innovation [6]. Omissions and design errors account for up to 79% of contract modification costs, translating to an average of 9.5% of total project costs [8]. Over the past decade, studies have increasingly examined the effectiveness of collaborative delivery models, including the use of selection frameworks based on Request for Proposal (RFP) evaluations and statistical modeling [9].

Furthermore, the complexity of construction projects and evolving stakeholder expectations have accelerated the adoption of Integrated Project Delivery (IPD). IPD emphasizes early involvement, trust, and shared goals among all key participants, ultimately minimizing adversarial dynamics [5]. Collaborative agreements under IPD help align incentives and reduce fragmentation [10]. The IPD model exemplifies the shift from siloed approaches to integrated ecosystems that promote innovation and sustainable construction. When enhanced with digital tools, such as Building Information Modeling (BIM), cloud-based project dashboards, and AI-supported communication, these collaborative frameworks become even more.

2.3. Types of Collaborative Project Delivery

Collaborative Project Delivery (CPD) methods, including IPD, Design–Build (DB), Construction Manager at Risk (CMAR), and Project Alliance (Alliancing), have proven instrumental in advancing sustainability in construction. These methods integrate key participants from the outset to form cohesive teams capable of managing complex challenges [11]. This structure aligns with sustainable construction goals by reducing waste, optimizing resources, and establishing accountability throughout the project lifecycle.

As CPD evolves, there is a clear trend toward shared risk and open communication, both essential for trust and mutual project success [12,13]. IPD, in particular, is associated with improved outcomes and reduced waste, thereby enhancing environmental sustainability [14]. This shift reflects the industry’s emphasis on lifecycle thinking, where construction is viewed not as a standalone phase but as part of an integrated continuum encompassing design, operation, maintenance, and renewal.

IPD enables comprehensive stakeholder integration from design through to operation and eventual deconstruction, embodying the cradle-to-cradle philosophy [15]. This integrated view is essential for building sustainable infrastructure and highlights how digital and collaborative methods can transform the role of construction in urban resilience. When coupled with digital twins, real-time analytics, and AI-powered procurement tools, CPD becomes more than a delivery method; it becomes a strategic system.

Alliancing, as an innovative CPD model, brings owners, designers, and contractors under one contract to foster early and continuous collaboration. It emphasizes joint accountability and a no-blame culture, enhancing transparency, risk management, and overall project performance [16].

2.4. Integrated Project Delivery Method (IPD)

Integrated Project Delivery (IPD) is a collaborative project delivery methodology that unites stakeholders, systems, and digital processes to enhance construction outcomes by improving efficiency, reducing waste, and maximizing value across all project stages, from design to construction and handover [17,18]. A defining advantage of IPD is the early engagement of key participants, designers, contractors, and owners, allowing for timely issue resolution, innovation integration, and the minimization of design conflicts or costly revisions [19,20,21]. This early collaboration is particularly beneficial for complex urban projects, where interdependencies and stakeholder expectations are high. IPD promotes a culture of transparency and trust through joint decision-making and open budgeting mechanisms. It implements shared risk and reward structures that reduce adversarial relationships and foster equitable engagement among all parties [22,23]. Importantly, financial incentives in IPD are tied to overall project success rather than isolated performance metrics, encouraging alignment in cost, schedule, and quality objectives [9,10]. This collaborative model enhances team integration and is well-suited for infrastructure, where holistic performance and stakeholder alignment are critical. Despite its advantages, IPD requires supportive legal and contractual frameworks that differ significantly from conventional agreements [5,24]. The success of IPD hinges on a foundation of mutual trust and cooperative culture, both of which are essential for effective communication and shared accountability. Transitioning to IPD may also demand a cultural shift within organizations, as it challenges established practices and hierarchical decision structures. Moreover, IPD’s effectiveness is amplified by the integration of digital technologies such as Building Information Modeling (BIM), real-time collaboration platforms, and cloud-based dashboards [25].

2.5. Bias and Methodological Challenges in Sustainable Contractor Selection

In the realm of sustainable contractor selection, various biases significantly influence decision-making processes and may hinder the adoption of environmentally responsible practices. Cognitive biases such as cultural bias, confirmation bias, and short-termism have been shown to adversely affect construction professionals’ perception of environmental compliance risks [26]. These biases often result in the underestimation of environmental risks and the prioritization of immediate gains over long-term sustainability objectives, thereby impeding the selection of contractors committed to sustainable practices [27,28]. Empirical evidence from developing regions, including Tanzania and Ghana, highlights these obstacles as critical impediments to sustainability-oriented decision-making in construction projects [29].

2.6. Trust as a Cornerstone of Integrated Project Delivery IPD

The evolution of IPD has underscored the increasing importance of shared risk and transparent communication as prerequisites for building trust and achieving mutual project objectives [12,13]. Within this paradigm, IPD has demonstrated tangible improvements in project outcomes, notably through waste reduction and enhanced efficiency [14]. Firms lacking technological readiness or integration capability may struggle to realize the full benefits of IPD [25]. Research in construction management consistently identifies trust as a foundational driver of project performance. “Honest communication, reliance, and delivery of outcomes” are the primary determinants of trust in construction [30]. Likewise, the broader literature highlights trust’s role in enabling transparency, accountability, and a no-blame culture across project teams [31,32,33,34,35].

2.7. Trust at Scale (IPD Value) Lessons from Industry Leaders for IPD

As presented in Table 1, the frequency of literature engagement across IPD sub-criteria illustrates varying levels of scholarly emphasis, thereby indicating both well-established concepts and areas with limited academic investigation. Beyond academia, industry leaders reinforce this perspective. Daniel Elk, cofounder of Spotify in 2006, observed that trust is “one of the most under-discussed forces in organizations” precisely because it is difficult to scale, yet its absence is the most common reason for organizational breakdown. In line with expert judgement, similarly, Charlie Munger, Vice Chairman (1978–2023) of Berkshire Hathaway, described trust as “one of the greatest economic forces in the world,” stressing the need to cultivate a “seamless web of deserved trust with great people.” Sir Michael Latham, chair of the UK Government/Industry Review and author of Constructing the Team (1994), succinctly captured that “Good relationships based on mutual trust benefit clients.” Such insights align with the construction industry’s experience, where trust functions as both a social and economic force, enabling sustainable collaboration, reducing disputes, and supporting long-term performance [36].

Table 1. IPD values.

IPD Values	Publication List
Procedural fairness and equitable treatment with a collaborative culture	A = [5] B = [17] D = [19] E = [20] F = [22] G = [23] I = [25] J = [37] K = [38] L = [39] M = [40] N = [41] O = [42] P = [43] Q = [44] R = [45] S = [46] T = [47] U = [48] V = [49] W = [50]
Pooled risk/reward with a target cost and an allowable cost ceiling	R = [45] V = [49] W = [50] X = [51] Y = [52]
Early stakeholder integration in planning and design	A = [5] B = [17] C = [18] I = [25] J = [37] K = [38] M = [40] N = [41] O = [42] P = [43] Q = [44] R = [45] T = [47] U = [48] Z = [53]
Lower change order incidence, compressed schedules, and reduced RFI volume	R = [45] T = [47] U = [48] V = [50]
Deliberate cultivation of relational trust among parties	B = [17] C = [18] D = [19] F = [22] G = [23] Z = [53]
Process efficiency gains with defect/error reduction	J = [37] P = [43] Q = [44] R = [45] W = [50]
Reduce schedule time	A = [5] D = [19] E = [20] K = [38] I = [25] M = [40] N = [41] O = [42] Q = [44]
Reduce waste	R = [45] E = [20] D = [19] O = [42] N = [41] J = [37] K = [38] I = [25] Q = [44]
Reduce project duration and liability by fast-tracking design and construction	A = [5] G = [23] Q = [44] R = [45] U = [48]
Shared cost, risk, reward, and responsibilities	B = [17] C = [18] D = [19] K = [38] L = [25] M = [44] M = [40] J = [37] Z = [53] E = [20]
Open communication/transparent information flow with rigorous schedule governance	D = [19] E = [20] F = [22] I = [25] K = [38] Q = [44] U = [48] AA = [54]
Multi-party agreement and noncompetitive bidding	A = [5] J = [37] K = [38] L = [25] M = [40] N = [41] O = [42] Q = [44]
Cross-functional decision-making for designs and shared design responsibilities	E = [20] M = [40] N = [41] O = [42] R = [45] T = [47] V = [49]
A = [5] B = [17] C = [18] D = [19] E = [20] F = [22] G = [23] I = [25] J = [37] K = [38] L = [39] M = [40] N = [41] O = [42] P = [43] Q = [44] R = [45] S = [46] T = [47] U = [48] V = [49] W = [50] X = [51] Y = [52] Z = [53] AA = [54]

In sum, trust is not merely a relational ideal but a measurable determinant of project success. Within IPD, it underpins the structures of transparency, shared governance, and collaborative accountability.

2.8. Evaluating Low-Carbon Urban Contractors

Urban infrastructure demands a robust Decision Support System (DSS) integrated within Integrated Project Delivery (IPD) frameworks to assess contractors on low-carbon, sustainability, and performance criteria. Employing advanced Multi-Criteria Decision-Making (MCDM) tools, such as interval type 2 fuzzy ANP and TOPSIS, enables structured handling of matrix complexities under uncertainty [55] and applies interval-fuzzy MCDM and game theory to street designs, identifying safety as the most critical benchmark, with green infrastructure and smart technologies. Similarly, the AHP is used to rank effectiveness and risk management in smart water management systems [56]. Embedding these criteria, weighted based on stakeholder input, ensures the DSS filters and prioritizes contractors capable of delivering measurable low-carbon outcomes.

Equally essential is evaluating digital innovation and community integration within a city context. Diffusion of innovation theory demonstrated that perceived advantages, compatibility, and observability critically influence eMobility sharing uptake, indicating that contractors’ digital platform readiness should be a formal evaluation criterion [57]. Low-carbon behaviors like cycling, energy savings, and reduced plastic use are shaped by socioeconomic and gender factors, underscoring the importance of context-sensitive design and community outreach [58]. A comprehensive DSS will integrate technical, digital, and social scoring modules, complete with evaluation rubrics and pilot-tested KPIs, empowering IPD teams to award contracts to firms that are not only technically proficient but also committed to advancing low-carbon, socially responsive, and future-ready urban infrastructure.

2.9. Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP), developed by Saaty in 1980, is one of the most widely used Multi-Criteria Decision-Making (MCDM) techniques in construction [59]. It systematically breaks down complex problems into a hierarchy of goals, criteria, and alternatives, allowing for pairwise comparisons to assign precise weights based on relative importance [60]. This structured approach promotes objectivity and ranks options, with a consistency ratio validating the results. AHP’s straightforward methodology makes it particularly valuable for tasks, like contractor selection, material evaluation, and project ranking, where subjective judgment is essential [60,61]. However, because it relies on definitive input values, the AHP can struggle with uncertainty, prompting the development of fuzzy logic extensions, such as Fuzzy AHP. Even so, the method’s capacity to quantify qualitative criteria and maintain consistency ensures its ongoing significance in construction decision-making.

2.10. VIKOR in Multi-Criteria Optimization

VIKOR focuses on ranking alternatives for multi-criteria optimization, especially when conflicting and non-commensurable criteria exist [62]. VIKOR employs linear normalization to eliminate units of criteria functions. It introduces a compromise solution by minimizing group utility (S) and individual regret (R) [63]. Extensions like Fuzzy VIKOR have been utilized to handle uncertainties, producing reliable results for MCDM applications [64]. In construction, VIKOR has proven effective in ranking contractors, assessing sustainable options, and balancing technical, economic, and social dimensions. Its ability to generate solutions that align with stakeholders’ priorities makes it increasingly relevant in sustainable infrastructure decision-making.

2.11. Fuzzy Logic in Multi-Criteria Decision-Making

Fuzzy logic, introduced by Zadeh [65], is widely used to manage uncertainty, ambiguity, and subjectivity in complex decision-making. In construction, where expert judgment plays a major role, it is often integrated with MCDM methods such as AHP, TOPSIS, and DEMATEL to better interpret qualitative or imprecise information [66]. Central to fuzzy logic is the use of linguistic variables (e.g., poor, average, excellent) represented through membership functions. Triangular functions, defined by (x, y, z), are preferred for their computational simplicity, while trapezoidal forms offer greater flexibility in higher-uncertainty contexts [67]. Techniques like Fuzzy AHP help derive criterion weights, while Fuzzy TOPSIS assists in ranking alternatives under uncertainty [68]. Because of its flexibility, intuitive structure, and compatibility with sustainability-oriented decision frameworks, fuzzy logic continues to gain prominence in engineering applications and contractor selection research [69].

2.12. Hybrid MCDM Approaches in Construction

The complexity of modern construction projects has driven the adoption of hybrid MCDM methods that combine the strengths of multiple techniques. For instance, AHP has been integrated with TOPSIS, DEMATEL, and fuzzy logic to enhance decision-making accuracy and handle uncertainties [70]. Hybrid approaches, such as Fuzzy–AHP–TOPSIS and DEMATEL–AHP, enable decision-makers to assign weights, assess interdependencies, and rank alternatives simultaneously. These methods have been applied to contractor selection, sustainability assessments, and project prioritization. Hybrid techniques offer greater flexibility and robustness compared to single methods, as they address subjective judgments and complex criteria interrelationships [66,71]. Their increasing use reflects the need for comprehensive tools to manage multi-criteria, multi-dimensional construction problems effectively.

2.13. Evaluating Contractors for Urban Infrastructure

Integrating advanced decision-making into urban infrastructure is essential for low-carbon outcomes. A descriptive analytics and ML study on several years of CO and weather data reveals clear seasonal patterns and key pollution drivers, providing actionable inputs for effective low-carbon strategies [65]. This is complemented by a six-step community empowerment framework that includes action planning, footprint tracking, flexible energy systems, and digital monitoring, which prioritizes stakeholder engagement, local resource use, and carbon-zero targets [66]. These approaches underpin the need for weighted evaluation metrics spanning technical, environmental, and social dimensions within a contractor-focused DSS. Building on this, social, digital, and policy dimensions must be captured. Millennials’ low-carbon behaviors, such as cycling, energy saving, and reducing plastic use, are sustained by enabling policies, stakeholder collaboration, and alignment with everyday consumption habits [51]. Accordingly, a practical DSS should include structured KPI modules, e.g., CO₂ reduction, digital engagement, and public outreach [50].

2.14. Technical Sustainability

Technical sustainability focuses on a structure’s performance, quality, and durability over its lifecycle [72,73,74]. A list of technical sub-criteria obtained through the literature review, which is utilized in the sustainability assessment of different construction works, is presented in Table 2.

Table 2. Technical sub-criteria list used in assessing sustainability.

Sub-Criteria	Description/Statement from Published Authors	Dimension	Authors
Communication and collaboration between project teams	The continual communication between the client and design team enables enhanced trust and facilitates knowledge sharing between participants, which is crucial to the continual development and technological trajectory of the construction industry in sustainable building [13].	Qualitative	C = [13] D = [17] E = [19] F = [20] G = [22] L = [50] Q = [75] R = [76] S = [35] T = [77] U = [32]
Current workload	This criterion illustrates the current works undertaken by the contractor before starting the particular project. The net worth of a contractor and its current assets and capacity of bid reflects whether or not the contractor is capable of executing the next project [78].	Qualitative	A = [5] K = [44] L = [51] M = [51] P = [60] V = [78] W = [79]
Construction approach and technology	Construction approach and technology establish the company’s capacity for technology and innovation for increasing the sustainability of both the construction process and its resultant assets [80].	Qualitative	E = [19] H = [25] I = [37] J = [43] X = [80] Y = [81]
Qualification of personnel	Qualification of personnel establishes that the qualification and experience level of company staff, as well as their track record, are important in assessing candidate contractors’ abilities [82].	Qualitative	B = [12] N = [52] Z = [82] AA = [83]
A = [5] B = [12] C = [13] U = [33] S = [36] I = [38] D = [17] E = [19] F = [20] G = [22] H = [26] J = [44] K = [45] L = [51] M = [52] N = [53] P = [61] Q = [76] R = [77] T = [78] V = [79] W = [80] X = [81] Y = [82] Z = [83] AA = [84]

2.15. Social Sustainability

Social sustainability emphasizes creating healthy, balanced, and prosperous communities through equitable and just practices. A list of social sub-criteria obtained through the literature review, which is utilized in the sustainability assessment of different construction works, is presented in Table 3.

Table 3. Social sub-criteria list used in assessing sustainability.

Sub-Criteria	Description/Statement from Published Authors	Dimension	Author
Trust among project teams	Honest communication, reliance, and delivery of outcomes are the main factors that govern trust in the construction industry [30].	Qualitative	A = [17] B = [18] C = [19] D = [22] F = [53] H = [35] I = [32] K = [30] L = [34]
Company image/reputation	The clients’ and consultants’ satisfaction and past awards measure the reputation. Certificates of appreciation awarded by different agencies can further measure this parameter, and it covers the types of complex projects successfully finished in the past [78].	Qualitative	J = [78] M = [84] N = [85] O = [86]
Geographic familiarity	This inspects the familiarity of the contractor with the local environment of the site or area where the project is to be executed. Experience in a particular region helps contractors to know the current politics, environment, local government, local laborers, and suppliers [78].	Qualitative	J = [78] Q = [87] R = [88] V = [89]
Claims and litigation history	Claims and litigation history is a critical sub-criterion under the social sub-criteria for assessing sustainability in water and wastewater Public–Private Partnership (PPP) projects. These projects involve various stakeholders and risks, including those related to investors, consultants, and external factors. Evaluating claims and litigation history helps address uncertainties, such as poor decision-making and operational disruptions, thereby promoting trust and mitigating conflicts, essential for the long-term success of sustainability initiatives [90].	Qualitative	E = [42] S = [90] T = [91] U = [92]
A = [17] B = [18] C = [19] D = [22] K = [30] I = [32] L = [34] H = [35] E = [42] F = [53] J = [78] M = [84] N = [85] O = [86] Q = [87] R = [88] V = [89] S = [90] T = [91] U = [92]

2.16. Economic Sustainability

Table 4 shows that economic sustainability involves factors that ensure financial health while supporting the economic development of local and global communities. It includes improving construction performance and durability to minimize lifecycle costs, operational expenses, and resource wastage [93].

Table 4. Economic sub-criteria list used in assessing sustainability.

Sub-Criteria	Description/Statement from Published Authors	Dimension	Author
Financial strength	The financial strength of the contractor is the degree to which the contractor can resist financial challenges without running into bankruptcy [79].	Quantitative	J = [94] I = [79] K = [95]
Performance bond	The concept of a performance bond, guarantee, and any insurance-like scheme has been adapted into the construction contract as a financial mechanism to ensure project delivery [96].	Quantitative	L = [96] E = [47] F = [52] A = [20] C = [42] B = [25] D = [44]
Cost management	Cost management can be defined as the management of cost-related activities achieved by collecting, analyzing, evaluating, and reporting cost information used for budgeting, estimating, forecasting, and monitoring costs [97].	Qualitative	H = [77] N = [98] O = [99] G = [35] M = [97]
Financial liquidity	Bidder’s financial liquidity is the bidder’s ability to fulfil (repay) in due time the most chargeable (short-term) liabilities (e.g., related to the payment to business partners for products and services, payment of remuneration, etc.) [100].	Quantitative	P = [100] Q = [101]
A = [20] B = [25] C = [42] E = [47] F = [52] G = [35] H = [77] I = [79] J = [94] K = [95] L = [96] M = [97] N = [98] O = [99] P = [100] Q = [101]

2.17. Environmental Sustainability

Environmental sustainability focuses on minimizing the negative impact of construction on the natural environment by promoting efficient use of resources, renewable energy, and pollution control [102]. A list of environmental sub-criteria obtained through the literature review, which is utilized in the sustainability assessment of different construction works, is presented in Table 5.

Table 5. Environmental sub-criteria list used in assessing sustainability.

Sub-Criteria	Description/Statement from Published Authors	Dimension	Author
Wastage in production	Identifying the causes of construction waste production will help us decide better how to control this sort of waste [103].	Qualitative	A = [79] B = [103]
Contractor safety performance	Contractor safety performance among major selection criteria can ultimately bring significant improvements to the building industry’s safety [104].	Qualitative	C = [104] D = [105] E = [106]
GHG emission containment	Growing concern about global warming and climate change, the construction industry, as a major contributor to greenhouse gas (GHG) emissions, has begun to realize its essential role in improving the environment by reducing emissions through the lifecycle of buildings and infrastructures [107].	Quantitative	F = [107] G = [108] H = [109] I = [110] J = [111]
Reuse material	New research [112] establishes that the reuse of materials improves material efficiency across all economic sectors and represents the second-best choice after waste prevention to decrease resource consumption and carbon emissions and divert demolition waste from landfills [112].	Quantitative	K = [112] L = [113] M = [114] N = [115] O = [116]
A = [79] B = [103] C = [104] D = [105] E = [106] F = [107] G = [108] H = [109] I = [110] J = [111] K = [112] L = [113] M = [114] N = [115] O = [116]

3. Methodological Structure

3.1. Deliberations and Assumptions

Figure 2 illustrates how the study considered four alternative contractors using the combinations of IPD as a PDM and QBS procurement systems (IPD and QBS), incorporating Fuzzy AHP and Fuzzy VIKOR.

Figure 2. Methodological framework.

3.2. Rationale for FAHP and Fuzzy VIKOR in Contractor Selection

The selection of sustainable contractors is a complex decision-making process that requires evaluating both qualitative and quantitative factors under uncertainty. To address this complexity, this study adopts a hybrid Multi-Criteria Decision-Making (MCDM) approach, combining the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy VIKOR. This combination is selected due to its complementary strengths in handling vague human judgment and optimizing multi-criteria trade-offs [72,73].

3.3. Enhancing DSS for Sustainable Contractor Selection Through IPD and QBS Integration

Table 6 shows that the effectiveness of any contractor selection system depends on the quality, relevance, and structure of its evaluation criteria. Traditional contractor selection tools often rely on generic or cost-driven criteria, failing to reflect the collaborative and sustainability-focused needs of modern construction projects. By integrating criteria drawn from Integrated Project Delivery (IPD) and Qualifications-Based Selection (QBS) frameworks, this research develops a Decision Support System (DSS) that offers a more comprehensive, context-sensitive, and sustainability-aligned contractor selection process.

Table 6. Combined impact of IPD and QBS: a holistic and superior DSS.

Feature	Traditional DSS	Proposed DSS (IPD + QBS)
Primary Selection Focus	Price, basic capability	Collaboration, sustainability, and qualifications
Contractor Involvement Timing	Post-design	Early (IPD-aligned)
Evaluation Depth	Technical specs only	Lifecycle, innovation, and sustainability criteria
Decision-Making Structure	Owner-led	Multi-criteria and stakeholder-inclusive
Bias Control and Objectivity	Limited	Enhanced via structured QBS + MCDM tools

3.4. Development of the Model

As part of the methodology, a prequalification process was established as shown in Table 7 to ensure that only qualified contractors were considered for sustainable project delivery. This involved the submission of mandatory documentation, including acknowledgement of all addenda, proof of general liability insurance coverage of CAD 10 million in accordance with CCDC 30, and valid Certificates of Recognition (COR) for both Alberta and Saskatchewan. Additional requirements included Workers’ Compensation Board (WCB) letters of clearance for both provinces, along with signed affidavits confirming non-collusion, equal employment opportunity compliance, and the team’s commitment and availability. These criteria were essential in maintaining transparency, safety, and capacity standards throughout the contractor selection process.

Table 7. Prequalification checklist and mandatory submission requirements.

Item No.	Prequalification List	Requirement
1	Acknowledgement of all addenda, if any	Mandatory
2	Evidence provided of general liability coverage for CAD 10 M and CCDC30	Mandatory
3	Evidence provided for COR for Alberta and Saskatchewan	Mandatory
4	Evidence provided for WCB letters of clearance for AB and SK	Mandatory
5	Non-collusion affidavit	Mandatory
6	Equal employment opportunities employer affidavit	Mandatory
7	Team commitment and availability affidavit	Mandatory

3.5. Post-Prequalification Differentiation of Contractors Across Sustainability Dimensions

Contractor A demonstrated strong technical integration, collaboration, and process efficiency. Contractor B excelled financially, showing superior liquidity, cost control, and overall fiscal reliability. Contractor C distinguished itself socially through high community trust, a clean litigation record, and strong stakeholder alignment. Contractor D emerged as the sustainability leader, outperforming others in carbon reduction, material circularity, and safety performance. In this study, Figure 3 depicts the use of sixteen sub-criteria and four contractor alternatives, which were purposefully adopted to preserve the methodological coherence and computational reliability of the FAHP–Fuzzy VIKOR framework. Increasing the number of sub-criteria beyond this threshold or expanding the alternatives above four would create excessive complexity in the pairwise comparison process, reduce judgment consistency, and diminish the discriminative capacity of the VIKOR ranking procedure. Consequently, maintaining this structure ensures that the decision support model remains stable, interpretable, and analytically effective.

Figure 3. Hierarchy of the decision problem.

3.6. Converting Objective Values into Subjective Inputs

Figure 4 illustrates the sequential integration of the Fuzzy AHP and Fuzzy VIKOR procedures from goal definition and weight calculation to compromise ranking of alternatives forming the complete decision support framework. This study generated an Excel extension that integrates subjective and objective weight. The advantage of the developed approach is that it uses decision makers’ experience and the tangible (numerical input) information from end users throughout the decision-making process. In addition to subjective weights determined by decision-makers, this study derived subjective weights from objective values using Shannon’s entropy as a basis [117]. The Shannon entropy method is introduced to objectively quantify the information content and variability within each criterion, ensuring that data-driven weights complement rather than replace the subjective FAHP weights. To determine objective weights using the entropy measure, the decision matrix was normalized for each criterion $C_j$ ($j=\mathrm{1,2},\dots ,n$), where $n$ denotes the number of criteria, in order to obtain the projection value $p_{ij}$ for each criterion. Alongside the subjective weights provided by decision-makers, this study also calculated objective weights based on Shannon’s entropy method.

Figure 4. Fuzzy AHP weight derivation and integration with the Fuzzy VIKOR process for ranking alternatives in Multi-Criteria Decision-Making.

Step 1.

To calculate objective weights through the entropy method, each criterion in the decision matrix must first be normalized criterion $C_j$ ($j=\mathrm{1,2},\dots ,n$), where $n$ denotes the number of criteria (to obtain the projection value $p_{ij}$) of each as follows:

$${\mathrm{P}}_{\mathrm{i}\mathrm{j}}={\mathrm{X}}_{\mathrm{i}\mathrm{j}}/\sum (\mathrm{i}=1\text{ to m})·{\mathrm{X}}_{\mathrm{i}\mathrm{j}}$$

(1)

where m is the number of alternatives.

Step 2.

Once the decision matrix is normalized, the Shannon entropy index can be computed as follows:

$$\mathrm{H}=-\sum (j=1\mathrm{to}m)\mathrm{value}{p}_{ij}\text{× }\ln (\mathrm{value}{p}_{ij})$$

(2)

Step 3.

Now, the following equation is used to find out the Shannon equitability index or the entropy to measure the evenness of the values in a particular criterion. The entropy value is denoted as e_j as follows:

$${\mathrm{e}}_{\mathrm{j}}=\mathrm{H}/\text{ln(n)}$$

(3)

where m is the total number of alternatives considered in the decision-making process.

Step 4.

The divergence degree is then determined using d_j = 1 − e_j. A higher d_j, value indicates a greater level of divergence. In the matrix, criteria with higher divergence values are prioritized when distributing subjective values across the range.

3.7. Steps for Computing Criteria Weights Using Fuzzy AHP

Problem Definition and Objective Identification

All sixteen criteria are assessed using established MCDM procedures, as illustrated in Figure 4 and detailed in the subsequent sections.

Step 1: Constructing the Comparison Matrix

The hierarchical decision-making framework evaluates the objective across four sustainability dimensions, technical, economic, social, and environmental, each with four sub-criteria, totaling sixteen. These criteria are assessed using a standard MCDM procedure and elaborated in the following sections. The mathematical formulation for comparisons is a_ij = w_i/w_j. Therefore, Table 8 shows how fuzzy pairwise comparisons are used to capture expert judgments. Fuzzy AHP was first used to convert experts’ linguistic assessments of relative importance into fuzzy numbers through pairwise comparisons of the 16 sub-criteria. For example, if an expert judged Energy Efficiency (C₁) to be “strongly more important” than Material Carbon Impact (C₂), this linguistic term was mapped to the crisp value 5 and then to the corresponding fuzzy number (3, 4, 5, 6), while the reciprocal judgment became 1/6, 1/5, 1/4, and 1/3.

Table 8. Comparison matrix for criteria weight derivation.

	Criteria 1	Criteria 2	Criteria 3	…	Criteria n
Criteria 1	1	a12	a13	…	a1n
Criteria 2	a21	1	a23	…	a2n
Criteria 3	a31	a32	1	…	a3n
…	…	…	…	…	…
Criteria n	an1	an2	an3	…	1

Step 2: Normalizing the Matrix

After knowing the comparison of its criteria in Table 9, the next thing is to normalize the matrix. This is performed by dividing each cell by the summation of that column’s value. Here

$${\mathrm{x}}_{\left(\mathrm{ij}\right)}={\mathrm{a}}_{\left(\mathrm{ij}\right)}/\sum ·{\mathrm{a}}_{\left(\mathrm{ij}\right)}$$

(4)

Table 9. Pairwise comparison of criteria.

Importance Index	Definition of Importance Index
1	Equally Important Preferred
2	Equally to Moderately Important Preferred
3	Moderately Important Preferred
4	Moderately to Strongly Important Preferred
5	Strongly Important Preferred
6	Strongly to Very Strongly Important Preferred
7	Very Strongly Important Preferred
8	Very Strongly to Extremely Important Preferred
9	Extremely Important Preferred

Step 3: Calculating Criteria Weight

After establishing the pairwise comparison values in Table 9, the matrix was normalized by dividing each entry by the total of its corresponding column. This procedure standardizes the expert judgments and produces a normalized comparison matrix from which the relative weight of each sustainability sub-criterion can be derived for subsequent FAHP processing.

$${\overline{\mathrm{a}}}_{(\mathrm{ij})}=(1/\mathrm{n})\times \sum ·{\mathrm{x}}_{(\mathrm{ij})}$$

(5)

Step 4: Verifying Consistency

The pairwise comparison matrix was evaluated using Saaty’s Consistency Index (CI) and a consistency ratio (CR) to ensure the logical coherence of expert judgments before fuzzification. The normalized eigenvector was derived and used to compute the maximum eigenvalue ${\lambda }_{\mathrm{m}\mathrm{a}\mathrm{x}}$, from which the CI and CR were calculated using the corresponding random index. For example, a 3 × 3 matrix in this study produced ${\lambda }_{\mathrm{m}\mathrm{a}\mathrm{x}}=3.09$, yielding $CI=0.045$ and $CR=0.078$, both within the acceptable threshold (CR < 0.10), confirming that the expert inputs were consistent and suitable for subsequent FAHP weighting.

Step 5: Fuzzification. The given weights need to be fuzzified based on Table 10, given below.

Table 10. Importance index and fuzzy numbers.

Importance Index	Crisp Number	Fuzzy Number (l, m, n, p)
Extremely more important	9	7, 8, 9, 10
Very strongly more important	7	5, 6, 7, 8
Strongly more important	5	3, 4, 5, 6
Moderately more important	3	1, 2, 3, 4
Equal importance	1	1, 1, 1, 1
Moderately less important	1/3	1/4, 1/3, 1/2, 1
Strongly less important	1/5	1/6, 1/5, 1/4, 1/3
Very strongly less important	1/7	1/8, 1/7, 1/6, 1/5
Extremely less important	1/9	1/10, 1/9, 1/8, 1/7

Step 6: Fuzzified Normalized Weight and Global Ranking. Finally, normalized fuzzy weights are calculated as follows:

$$\mathrm{wj}=\left(l_j,m_j,n_j,p_j\right)/4$$

(6)

where, j = 1, 2, 3, …, m (number of criteria)

For this study, the fuzzified normalized weights were obtained by aggregating the lower, middle, and upper fuzzy values $\left(l_j,m_j,n_j,p_j\right)$ for each criterion using the geometric mean and then normalizing each component by the total of its respective column. This process produced the final normalized fuzzy weights used as inputs for the Fuzzy VIKOR procedure, ensuring that each sustainability sub-criterion contributed proportionally and consistently to the overall contractor ranking.

3.8. Ranking of Alternatives with Fuzzy VIKOR (Using Fuzzy AHP Weightage)

As shown in Figure 5, the Fuzzy AHP–Fuzzy VIKOR procedure was operationalized in this study through a sequential workflow beginning with the elicitation of expert judgments for the 16 sub-criteria using linguistic terms. These linguistic evaluations were converted into fuzzy scales and used to derive the FAHP importance weights, which subsequently informed the construction and normalization of the fuzzy decision matrix for each contractor. Following the defuzzification step and the computation of the utility and regret measures, the final VIKOR index was calculated, enabling the ranking of contractor alternatives in a transparent and sustainability-oriented manner consistent with the structure outlined in Figure 5.

Figure 5. Calculation steps of Fuzzy VIKOR.

4. Case Study and Application

4.1. Overview of the Case Study Application

Fuzzy AHP and a decision matrix were applied to derive criteria weights, followed by an independent Fuzzy VIKOR evaluation to rank the contractor alternatives. The process incorporated stakeholder input consistent with the IPD framework and culminated in the development of an automated multi-criteria DSS using specialized software tools.

4.2. Details of Case Study, MCDM, and DSS Templates

Table 11 provides details of the case study involving a two-story multi-purpose banquet center that was utilized to validate the Decision Support System (DSS) developed in this research. The choice of structure was intentional to align with Integrated Project Delivery (IPD) requirements through a Request for Proposal framework. While Table 12 presents the expert evaluators’ roles, professional affiliations, and industry experience involved in the case study.

Table 11. Study parameters.

Parameters	Details
Project location	Edmonton
Building type	Banquet Centre
Project Delivery Method	IPD
Contractor options	4
Building height	20.2 s (m)
Number of floors	2
Gross floor area	1798.26 m2
Structural components considered	Columns and beams

Table 12. Details of the respondents who took part in the case study.

Team	Role in the Case Study	Profession/Position	Company/Institution	Work Experience (Years)
Team 1	Evaluator 1a	Construction Manager	Brook Atlantic	25–30
	Evaluator 2a	Quantity Surveyor	Stantec	25–30
	Evaluator 3a	Financial Analyst	Clark Builders	20–25
Team 2	Evaluator 1b	Construction Manager	University of Alberta	20–25
	Evaluator 2b	Quantity Surveyor	ABMK Engineering	30–35
	Evaluator 3c	Financial Analyst	Bird Construction	25–35
Team 3	Evaluator 1c	Construction Manager	University of Alberta	25–25
	Evaluator 2c	Quantity Surveyor	Chandos Construction	25–30
	Evaluator 3c	Financial Analyst	Alberta Masonry Council, Wood Works	20–25

The building frame, rendering, and typical floor plan are shown in Figure 6, Figure 7 and Figure 8.

Figure 6. MBC frame.

Figure 7. MBC rendering.

Figure 8. Floor plan.

4.3. Details of the MCDM

In the selection of contractors for sustainable construction projects, decision-makers rely on a combination of objective and subjective evaluations. Objective values are quantifiable and measurable data points, while subjective values involve qualitative assessments based on expert judgment, industry benchmarks, and stakeholder preferences. The integration of subjective values ensures a more comprehensive decision-making process, accounting for factors such as risk perception, experience, and sustainability priorities. This section explains how objective values in contractor evaluation criteria can be translated into subjective ratings.

4.4. Objective to Subjective Transformation Framework

The transformation of numerical (objective) data into qualitative (subjective) ratings involves setting predefined ranges that correspond to linguistic categories such as excellent, good, average, poor, and very poor. This allows for an intuitive understanding of contractor performance in various criteria. The methodology follows these steps.

The normalization of objective values involves converting values to a uniform scale.

$${\mathrm{X}}_{\mathrm{i}}^{\prime }=({\mathrm{X}}_{\mathrm{i}}-{\mathrm{X}}_{\min })/{\mathrm{X}}_{\max }-{\mathrm{X}}_{\min }$$

(7)

where X′ is the normalized value and X is the actual raw value.

X_min is the minimum value in the dataset for that criterion.

X_max is the maximum value in the dataset for that criterion.

The Shannon diversity index calculation involves evaluating diversity in the criteria distribution to measure variability in contractor performance.

$$H^{\prime }=-\sum _{\left\{i=1\right\}}^{\left\{n\right\}}{p}_{i}ln{p}_i$$

(8)

where

P_i is the proportion of each category (e.g., bid score, project count, sustainability rating, etc.) and ln is the natural logarithm Shannon equitability index calculation—determining how evenly performance is distributed across contractors.

$$\text{EH}={\mathrm{H}}^{\prime }/\text{ln (S)}$$

(9)

where

H′ is the Shannon diversity index, SSS is the total number of categories (e.g., species, contractor alternatives, etc.), and ln(S) is the natural logarithm of the number of categories.

4.5. Conversion of Objective User Input to Subjective Value

Step 1. The inputs of Table 13 are normalized by dividing each cell value by the sum value of each column (total criteria values for all alternatives). This normalization process yields the normalized matrix, as presented in Table 14. The criterion weight is computed using a hybrid aggregation of subjective FAHP weights and objective Shannon entropy weights, expressed as $w_j^{*}=\alpha w_j^{FAHP}+(1-\alpha )w_j^{ENT}$. This combined weight is then normalized across all criteria to yield $w_j^{final}=w_j^{*}/\sum w_j^{*}$, ensuring that both expert judgment and data-driven variability jointly determine the criterion’s importance in a fully reproducible manner.

Table 13. Linguistic term.

Linguistic Term	Conversion Scale in Normalized Matrix
Very High	>0.89
High	0.80 to 0.89
Medium	0.70 to 0.79
Low	0.60 to 0.69
Very Low	<60

Table 14. Converted into a normalized matrix.

Alternatives	Current Workload (%/Week)	Financial Strength (CAD/Project)	Performance Bond (CAD/Project)	Cost Management (CAD/Project)
Contractor 1	0.3216	0.2857	0.1724	0.3333
Contractor 2	0.2807	0.2161	0.3275	0.3188
Contractor 3	0.1754	0.1597	0.2758	0.0724
Contractor 4	0.2222	0.3383	0.2241	0.2753
Alternatives	Financial Liquidity (CAD/Project)	Contractor Safety Performance (CAD/Project)	Wastage in Production (CAD/Project)	GHG Emission Containment (kg CO2 Equivalent/Sqm)
Contractor 1	0.2216	0.2857	0.1724	0.3333
Contractor 2	0.2807	0.2422	0.3275	0.3188
Contractor 3	0.1988 4	0.2511	0.1821	0.0812
Contractor 4	0.2222	0.3383	0.2241	0.2753

Step 2. By adding the column values, where each cell value is multiplied by its logarithm (ln) value, Shannon’s diversity index is calculated. The Shannon diversity index measures the diversity of range values for any criterion among the alternatives, while the Shannon equitability index evaluates the evenness of the distribution across categories. The results are presented sequentially in Table 15 and Table 16. Linguistic performance ratings were converted into normalized numerical ranges (Table 17) and assembled into a subjective assessment matrix for all alternatives and criteria (Table 18), which served as the basis for computing Shannon’s diversity and equitability indices.

Table 15. Shannon diversity index.

Alternatives	Current Workload (%/Project)	Financial Strength (CAD/Project)	Performance Bond (CAD/Project)	Cost Management (CAD/Project)
Shannon diversity index	1.36	1.35	1.36	1.34
Alternatives	Financial Liquidity (CAD/Project)	Contractor Safety Performance (CAD/Project)	Wastage in Production (CAD/Project)	Greenhouse Gas Emissions (kg CO2 Equivalent/sqm)
Shannon diversity index	1.37	1.32	1.38	1.29

Table 16. Shannon equitability index.

Alternatives	Current Workload (CAD/Project)	Financial Strength (CAD/Project)	Performance Bond (CAD/Project)	Cost Management (CAD/Project)
Shannon equitability index	0.995	0.997	1.00	0.999
Alternatives	Financial Liquidity (CAD/Project)	Contractor Safety Performance (CAD/Project)	Wastage in Production (CAD/Project)	Greenhouse Gas Emissions (kg CO2 Equivalent/sqm)
Shannon equitability index	1.00	0.986	0.992	0.99

Table 17. Range determined.

Linguistic Term	Conversion Scale in Normalized Matrix
Very High	>0.2750
High	0.2235 to 0.2750
Medium	0.1700 to 0.2235
Low	0.1225 to 0.1700
Very Low	<0.12225

Table 18. Output of the subjective result.

Alternatives	Current Workload (CAD/Project)	Financial Strength (CAD/Project)	Performance Bond (CAD/Project)	Cost Management (CAD/Project)
Contractor 1	Very High	Very High	Low	Very High
Contractor 2	High	Medium	Very High	Very High
Contractor 3	Low	Low	High	Very Low
Contractor 4	Medium	Very High	Medium	High
Alternatives	Financial Liquidity (CAD/Project)	Contractor Safety Performance (CAD/Project)	Wastage in Production (CAD/Project)	Greenhouse Gas Emission (kg CO2 Equivalent/sqm)
Contractor 1	Very High	High	Low	High
Contractor 2	High	Medium	Very High	Very High
Contractor 3	Medium	Medium	Very High	Low
Contractor 4	High	Very High	Medium	High

4.6. Computation of Criterion Weights and Consistency Verification

Consistency was verified through λₘₐₓ, CI, and CR calculations, all of which met the acceptable CR < 0.10 threshold. The consistent matrices were then converted into fuzzy representations using linguistic–triangular scales, and fuzzified normalized weights were generated following the FAHP formulation. These final fuzzy weights, summarized in Table 19, represent the relative importance of each criterion.

Table 19. Pairwise comparison matrix.

Criteria	Fuzzy Normalized Weight	Weight-Based Rank of Criteria
Communication/collaboration	0.0668	8
Current workload	0.0347	14
Construction methodology	0.0547	11
Qualification of personnel	0.07	10
Financial strength	0.0417	12
Performance bond	0.088	2
Cost management	0.070	7
Financial liquidity	0.0365	13
Trust	0.08	4
Company image/reputation	0.0844	3
Geographic familiarity	0.0274	15
Claims and litigation history	0.066	9
Wastage in production	0.074	6
Contractor safety performance	0.104	1
GHG emission containment	0.077	5
Reuse of material	0.0269	16

Because the comparison matrix reflects individual inputs, fuzzified normalized weights vary across evaluators. Figure 9 summarizes and visualizes the overall importance and between-rater variation. Across stakeholders, current workload, construction methodology, financial strength, personnel qualification, and material reuse emerge as the dominant drivers of contractor selection, whereas reputation, safety performance, and GHG emission control receive comparatively lower emphasis.

Figure 9. Summary of normalized weighting.

Step 9. Similarly, Steps 1 to 8 were repeated for Evaluator 2 and Evaluator 3, respectively. The combined result of the stakeholders of Team 3 is thus obtained and shown in Table 20.

Table 20. Ranking of alternatives for Team 3.

	Qi (Evaluator 1)	Qi (Evaluator 2)	Qi (Evaluator 3)	Weighted Qi	Rank
Importance of Opinion	0.4	0.3	0.3
Contractor 1	1.000	1.000	1.000	1.000	4
Contractor 2	0.948	0.699	0.854	0.845	3
Contractor 3	0.000	0.000	0.000	0.040	1
Contractor 4	0.634	0.663	0.796	0.692	2

Step 10. Similar analysis was performed on the user inputs from Teams 1 and 2 to rank the alternatives. All the results are compiled in Table 21 below.

Table 21. The overall result of Fuzzy VIKOR for different teams.

	Team 1		Team 2		Team 3
Alternatives	Weighted Qi	Rank	Weighted Qi	Rank	Weighted Qi	Rank
Contractor 1	0.027	1	0.306	2	1.000	4
Contractor 2	0.342	2	0.199	1	0.845	3
Contractor 3	0.997	4	0.850	4	0.040	1
Contractor 4	0.946	3	0.580	3	0.692	2

4.7. Fuzzy VIKOR Analysis

Table 21 and Figure 10 present the comparative ranking of contractors based on the weighted Fuzzy VIKOR index (Qi) calculated from evaluations by three distinct assessment teams. Contractor 2 emerged as the most favorable candidate, consistently ranking first, second, and third across all evaluation teams with the lowest Qi values (Team 1: 0.0342; Team 2: 0.199; Team 3: 0.845), indicating strong alignment with sustainability objectives and minimal deviation from the ideal solution. Contractor 1 and Contractor 3 exhibited inconsistent rankings, reflecting varied sustainability performance perceptions among evaluators. Contractor 4 displayed intermediate performance stability but never achieved the highest ranking, indicating moderate suitability.

Figure 10. Summary of the final result ranking.

4.8. Digital Implementation of Contractor Evaluation

To enhance decision-making accuracy in contractor selection within Integrated Project Delivery (IPD) environments, this study introduces a Digital Decision Support System (DSS) that integrates the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Fuzzy VIKOR, implemented in the C programming language.

As shown in Figure 11, the DSS captures two key input stages. It displays the weight-allocation screen where stakeholders assign 0–100 weights to the four main criteria and distribute them across sub-criteria (totals constrained to 100%), structuring priorities across the technical, economic, social, and environmental dimensions with a simple validation step. Figure 11 presents the evaluator scoring interface, in which Evaluator 1 rates each alternative against every sub-criterion.

Figure 11. Distribution of weightage. Evaluator 1.

The results dashboard (Figure 12) synthesizes each evaluator’s criterion weights visualized as a bar chart across the technical, economic, social, and environmental pillars. For every evaluator, a results table lists all alternatives with their computed closeness coefficient (CC) and corresponding rank, while a right-hand control sets evaluator weights (“importance of opinion,” in %). Selecting calculate generates the aggregate ranking, combining evaluator weights with the underlying multi-criteria analysis.

Figure 12. Weightage distribution and evaluator rankings.

At the final ranking stage, the interface reports each alternative’s closeness coefficient (CC) together with its overall rank. An accompanying bar chart visualizes the aggregated outcomes across alternatives, enabling rapid identification of the preferred option (see Figure 13).

Figure 13. Final ranking.

5. Verification and Validation

5.1. Model Verification and Validation Framework

This chapter outlined the approaches used to verify and validate calculations and the development of the Decision Support System (DSS). The input and output validation of the DSS was conducted based on expert feedback and recommendations, while the verification of calculations and the DSS was carried out using sensitivity analysis.

5.2. Sensitivity Analysis

Sensitivity analysis evaluates how changes in input factors affect a system’s output and helps verify whether the model behaves as intended under varying conditions. In this study, sensitivity analysis was conducted by testing the model across different scenarios to confirm its responsiveness to input changes and the reliability of its results. The multi-criteria Decision Support System (DSS) depends on two main user inputs: criteria weight assignments and preference ratings for ranking alternatives (e.g., “Very High,” “High,” etc.).

5.3. Criteria Weightage Sensitivity

The sensitivity of user inputs and criteria weights was assessed by evaluating four scenarios, each applying a different set of weights, as shown in Table 22. In each scenario, one sustainability pillar was given higher weight relative to the others, and the resulting impact on the $C{C}_i$ values and alternative rankings was examined. As reported and illustrated in Figure 14, adjusting the pillar weights produced notable changes in the $C{C}_i$ values, demonstrating that increasing the priority of a specific sustainability dimension leads to a corresponding shift in the ranking of alternatives.

Table 22. Scenario/situation assessment by sustainability pillars.

Pillars of Sustainability	Criteria	Base Weight	Situation 1 (CW)	Situation 2 (CW)	Situation 3 (CW)	Situation 4 (CW)
Technical	Communication/collaboration	0.05	0.10	0.05	0.05	0.05
	Current workload	0.05	0.10	0.05	0.05	0.05
	Construction methodology	0.05	0.10	0.05	0.05	0.0
	Qualification of personnel	0.05	0.10	0.05	0.05	0.05
Economic	Financial strength	0.05	0.05	0.10	0.05	0.05
	Performance bond	0.05	0.05	0.10	0.05	0.05
	Cost management	0.05	0.05	0.10	0.05	0.05
	Financial liquidity	0.05	0.05	0.10	0.05	0.05
Social	Trust	0.05	0.05	0.05	0.10	0.05
	Company image/reputation	0.05	0.05	0.05	0.10	0.05
	Geographic familiarity	0.05	0.05	0.05	0.10	0.05
	Claims and litigation history	0.05	0.05	0.05	0.10	0.05
Environmental	Wastage in production, contractor safety performance, GHG emission containment, and reuse of material	0.05	0.05	0.05	0.05	0.10
		0.05	0.05	0.05	0.05	0.10
		0.05	0.05	0.05	0.05	0.10
		0.05	0.05	0.05	0.05	0.10

Figure 14. Radar chart showing the sensitivity of the model for the criteria weightage.

In Table 22, the criteria within each sustainability pillar were selectively emphasized by assigning four criteria a weight of 0.10 and weighting the remaining criteria at 0.05. This variation was designed to examine how shifting sustainability priorities influences decision outcomes while keeping user preferences constant across all scenarios. Using the Fuzzy VIKOR method, alternatives that aligned more closely with the highly weighted criteria achieved higher scores, whereas those aligned with less emphasized criteria ranked lower. The resulting outputs, presented in Table 23, demonstrate how changes in criteria weight distribution alone can significantly alter the evaluation and ranking of alternatives.

Table 23. CCi values for four situations.

Situation 1						Situation 2
Contractor	Evaluator 1	Evaluator 1	Evaluator 1	Overall	Rank	Evaluator 2	Evaluator 2	Evaluator 2	Overall	Rank
C1	0.277	0.759	0.760	0.512	1	0.593	0.593	0.593	0.794	4
C2	0.542	0.509	0.540	0.366	2	0.680	0.378	0.654	0.571	2
C2	0.348	0.348	0.470	0.343	3	0.593	0.733	0.785	0.686	1
C3	0.368	0.366	0.367	0.337	4	0.450	0.378	0.601	0.476	3
Situation 3						Situation 4
Contractor	Evaluator 3	Evaluator 3	Evaluator 3	Overall	Rank	Evaluator 1	Evaluator 2	Evaluator 3	Overall	Rank
C1	0.478	0.449	0.538	0.488	3	0.402	0.502	0.724	0.543	2
C2	0.568	0.431	0.618	0.539	2	0.323	0.458	0.786	0.522	3
C3	0.333	0.303	0.789	0.475	4	0.647	0.338	0.493	0.493	4
C4	0.659	0.572	0.754	0.662	1	0.388	0.722	0.777	0.629	1

5.4. Sensitivity Analysis of Preference-Based Evaluation of Alternatives

Table 23 summarizes the results for Contractors C1–C4 across the four scenarios, presenting evaluator-level scores, aggregated totals, and the resulting ranks to show how contractor standing changes with different sustainability priorities. Figure 14 illustrates these outcomes using a radar chart that breaks performance into the technical, economic, social, and environmental pillars for each evaluator and for the overall team. Larger radii indicate stronger performance, while balanced or skewed polygon shapes reveal capability symmetry or trade-offs; alignment across evaluator polygons reflects consensus, whereas variation indicates differing assessments. The “Overall Impact Team 3” polygon integrates these perspectives and accounts for the final rankings reported in Table 23.

5.5. Combined Input Table for All Scenarios

Table 24 summarizes the evaluator judgments that feed the DSS by mapping each contractor’s performance to linguistic ratings with corresponding numeric bands (e.g., Very High = 7–10, High = 5–8, Medium = 3–6, Low = 1–4). The table is organized by scenario and criteria category (technical, economic, social, environmental) and distinguishes beneficial versus cost criteria (where a “Low” rating indicates better performance). These calibrated inputs are subsequently normalized, weighted, and aggregated within the DSS to produce the pillar scores visualized in Figure 14 (radar chart) and the overall scores and ranks reported in Table 23.

Table 24. Effective ratings for alternatives.

Criteria Category	Alternative 1	Alternative 2	Alternative 3	Alternative 4	Scenario
Technical Criteria	Very High (7, 8, 9, 10)	High (5, 6, 7, 8)	Medium (3, 4, 5, 6)	Low (1, 2, 3, 4)	Situation 1
Economic Criteria	Low (1, 2, 3, 4)	Very High (7, 8, 9, 10)	High (5, 6, 7, 8)	Medium (3, 4, 5, 6)	Situation 1
Social Criteria	Medium (3, 4, 5, 6)	Low (1, 2, 3, 4)	Very High (7, 8, 9, 10)	High (5, 6, 7, 8)	Situation 1
Environmental Criteria	High (5, 6, 7, 8)	Medium (3, 4, 5, 6)	Low (1, 2, 3, 4)	Very High (7, 8, 9, 10)	Situation 1
Technical Criteria	Very High (7, 8, 9, 10)	High (5, 6, 7, 8)	Medium (3, 4, 5, 6)	Low (1, 2, 3, 4)	Situation 2
Economic Criteria	Low (1, 2, 3, 4)	Very High (7, 8, 9, 10)	High (5, 6, 7, 8)	Medium (3, 4, 5, 6)	Situation 2
Social Criteria	Medium (3, 4, 5, 6)	Low (1, 2, 3, 4)	Very High (7, 8, 9, 10)	High (5, 6, 7, 8)	Situation 2
Environmental Criteria	High (5, 6, 7, 8)	Medium (3, 4, 5, 6)	Low (1, 2, 3, 4)	Very High (7, 8, 9, 10)	Situation 2
Beneficial Criteria	Very High (7, 8, 9, 10)	High (5, 6, 7, 8)	Medium (3, 4, 5, 6)	Low (1, 2, 3, 4)	Situation 3
Cost Criteria	Low (1, 2, 3, 4)	Medium (3, 4, 5, 6)	High (5, 6, 7, 8)	Very High (7, 8, 9, 10)	Situation 3
Beneficial Criteria	Very High (7, 8, 9, 10)	High (5, 6, 7, 8)	Medium (3, 4, 5, 6)	Low (1, 2, 3, 4)	Situation 4
Cost Criteria	Low (1, 2, 3, 4)	Medium (3, 4, 5, 6)	High (5, 6, 7, 8)	Very High (7, 8, 9, 10)	Situation 4

5.6. Combined CCi Results Across All Scenarios

The table below presents a consolidated view of the Closeness Coefficient Index (CCi) values and ranking results for all four scenarios used in validating the Decision Support System (DSS) for sustainable contractor selection. This unified table facilitates a comparative analysis of how each alternative performs across varying preference conditions. Table 25 consolidates the Closeness Coefficient Index (CCi), the proximity of each alternative to the ideal solution, and the induced rank for all four scenarios, providing a single view of performance under different preference settings. Higher CCi values indicate stronger overall performance, and the “Best/Worst Alternative” ticks highlight the scenario winners and laggards at a glance. By aligning the four scenarios vertically, the table enables cross-scenario stability checks (e.g., whether a contractor remains competitive as priorities shift), makes ties or near-ties visible where CCi values converge (a cue for managerial tie-break rules), and supports decision rationale by linking rank movements to scenario intent. In sum, Table 25 serves as the validation summary for the DSS, demonstrating how the same set of bidders responds to varying weight structures and identifying both scenario-optimal and robust choices across contexts.

Table 25. Situation assessment by sustainability pillars.

Scenario	Alternative	CCi	Rank	Best Alternative	Worst Alternative
Situation 1	Alternative 1	0.5135	3
Situation 1	Alternative 2	0.5155	1	✔
Situation 1	Alternative 3	0.5135	3
Situation 1	Alternative 4	0.5155	1	✔
Situation 2	Alternative 1	0.3035	4		✔
Situation 2	Alternative 2	0.5155	1	✔
Situation 2	Alternative 3	0.4023	3
Situation 2	Alternative 4	0.4515	2
Situation 3	Alternative 1	0.998	1	✔
Situation 3	Alternative 2	0.5023	2
Situation 3	Alternative 3	0.3035	3
Situation 3	Alternative 4	0.005	4		✔
Situation 4	Alternative 1	0.08	4		✔
Situation 4	Alternative 2	0.2035	3
Situation 4	Alternative 3	0.5023	2
Situation 4	Alternative 4	0.889	1	✔

The line chart in Figure 15 complements Table 25 by showing how each alternative’s normalized criterion values evolve across the four situations. Solid lines plot the beneficial pillars (where higher is better), while dashed lines plot the cost criterion (where lower is better). The convergence at Situation 2 (all ≈0.50) reflects the neutral baseline in Table 25; thereafter, the series diverge. Alternative 1 accelerates to ≈1.00 on beneficial criteria while its dashed cost line remains controlled, Alternative 4 rises strongly with a modest cost response, Alternative 3 improves steadily, and Alternative 2 dips on cost at Situation 3 before partially recovering. Line crossings indicate rank reversals across scenarios, and the slope of the solid vs. dashed segments reveals how the DSS weights simultaneous improvements in benefits against cost containment mechanics that produce the CCi spreads and ranks summarized in Table 25.

Figure 15. User preference sensitivity for all scenarios.

6. Limitations of the Research

While this study successfully developed and demonstrated a Decision Support System (DSS) for sustainability-oriented contractor selection using a case study of a multi-purpose banquet center, several limitations were identified. First, the reliance on expert judgment, though systematically structured, introduces potential subjectivity and variability across different evaluators. Additionally, the selection criteria, though derived from literature and expert consensus, may not fully encapsulate the breadth of emerging sustainability metrics or capture the views of broader stakeholder groups, including clients, regulators, and end-users. The validation process was further constrained by the lack of access to comprehensive contractor performance datasets, which limited real-world benchmarking.

The DSS framework is currently based on a static criteria hierarchy and does not support dynamic, real-time decision environments where situational variables shift rapidly. Its scalability and transferability may be impacted by regional disparities in construction practices, digital maturity, and levels of Integrated Project Delivery (IPD) adoption. Furthermore, simplifying sustainability into four broad pillars, technical, economic, environmental, and social, risks overlooking the complex, interdependent nature of sustainability dimensions in contemporary infrastructure delivery. The application of a Qualification-Based Selection (QBS) model, while emphasizing merit, may unintentionally marginalize emerging or non-traditional contractors who bring innovation but lack a long track record. Additionally, the framework’s reliance on advanced digital tools may inhibit adoption among small- and medium-sized enterprises (SMEs) operating with limited technological infrastructure. Furthermore, applying the DSS to large-scale urban infrastructure (e.g., transit systems, public facilities, utility networks) remains an important direction for future research.

6.1. Recommendations for Future Work

6.1.1. Integrate Advanced MCDM Techniques and Real-Time Adaptivity

Future DSSs should fuse network-aware, uncertainty-tolerant MCDM, such as AHP/ANP with fuzzy or hybrid extensions, for weighting and ranking with live, event-driven data streams so rankings adapt as conditions evolve [118,119,120,121]. In parallel, DT/BIM–IoT pipelines now enable continuous sensing and real-time visualization/decisioning validated in pilots [122,123,124]. Bibliometric evidence in construction DSS supports combining MCDM structures with streaming data and dashboarded feedback loops [125].

6.1.2. Promote Inclusive, Scalable, and Policy-Responsive Frameworks

Broadening stakeholder involvement across public agencies, private developers, end-users, and community representatives strengthens legitimacy and balances competing priorities, a finding repeatedly evidenced in smart cities through empirical analyses of participatory/e-participation governance and stakeholder mapping [126,127,128,129,130,131,132,133]. To ensure scalability and institutional fit, the DSS should be configurable to multiple delivery systems (DB, CMAR, Alliance/IPD) and procurement frameworks, aligning with Journal of Cleaner Production studies that link governance design and stakeholder engagement to sustainable public procurement performance and smart city outcomes [134,135,136,137]. This calibration enables transferability across regulatory and organizational contexts while maintaining a transparent, auditable path from stakeholder inputs to final rankings.

6.1.3. Integrate BIM, IoT, and Lifecycle Intelligence

Future DSS models should embed real-time dashboards and sensor-integrated data via BIM/IoT to automate data flows, support lifecycle tracking, and enable early risk mitigation. Robust evidence confirms effective BIM–LCC/LCA integration for decision support [138], while recent studies validate IoT/DT pipelines for real-time monitoring and analytics in construction, including safety-oriented applications [123,139]. Embedding Lifecycle Costing (LCC) alongside contractor performance tracking thus fosters iterative learning and accountability across the asset lifecycle [138,140].

6.1.4. Enhance Usability and Transferability Through Interface Design and Comparative Contexts

Automating sustainability criteria updates ensures regulatory alignment and market relevance, while simplified interfaces improve DSS usability for SMEs [116,141,142,143]. To evidence generalizability, the DSS should be benchmarked through comparative case studies across jurisdictions and delivery models, using standardized usability/adoption metrics (e.g., SUS, task success rate, time on task, error rate) and an auditable decision log that traces stakeholder inputs to outcomes [129,143,144,145,146]. Additionally, comparative case studies across diverse regulatory and cultural settings can assess the DSS’s adaptability and generalizability. BIM-integrated frameworks have already demonstrated success in enhancing regional decision-making through visualization and structured data [54,123,147,148].

7. Conclusions

This study delivers a significant contribution to sustainability-oriented contractor selection by developing an integrated Decision Support System (DSS) that combines Fuzzy AHP and Fuzzy VIKOR within the collaborative structure of Integrated Project Delivery (IPD). By incorporating technical, economic, social, and environmental dimensions rather than limiting evaluation to financial or productivity metrics, the framework addresses a persistent gap in prior research and aligns with broader theoretical foundations such as the sustainability pillars, SDGs, lifecycle value thinking, and collaborative decision-making theory. This comprehensive orientation strengthens the academic positioning of the study and advances the evolving discourse on sustainability-driven procurement.

The findings also offer deeper analytical insight by demonstrating how different contractors express trade-offs across the four sustainability dimensions and how compromise-based ranking can resolve ambiguity under uncertain, incomplete, or expert-derived data conditions. When contrasted with earlier studies that primarily emphasize cost and schedule, the findings demonstrate a substantial methodological and conceptual advancement, supported by a systematic examination of over fifty peer-reviewed publications and reinforced through verification and validation by nine industry and academic experts. This dual evidentiary base ensures that the framework is theoretically grounded, contextually sensitive, and robustly aligned with contemporary stakeholder-driven and fuzzy logic-enhanced decision-making practices. The real-world case study further reinforces the empirical robustness of the framework, illustrating its capability to support transparent, defensible, and multi-stakeholder decision processes in practice.

Practically, the DSS provides a scalable and policy-aligned tool for project owners, public agencies, and practitioners seeking to embed ESG performance, collaboration, and risk sharing into contractor selection. Its adaptability makes it suitable for infrastructure, building, and urban development settings where sustainability outcomes must be prioritized alongside cost and technical performance. Theoretically, the study contributes a structured and reproducible model that bridges MCDM methods with sustainability assessment and IPD procurement, offering a valuable reference point for future research and practice in sustainable construction management.