Circular Economy-Based Decision-Making Model for Contractor Selection
Abstract
1. Introduction
- The adoption of OSC in the construction industry provides substantial benefits aligned with the three pillars of sustainability: economic, social, and environmental sustainability. Implicated in various SDGs, such as SDG 3, 6, 7, 8, 9, 11, 12, 13, 15, and 17, the concept of CE offers a notable opportunity for the advancement of OSC and the applicability of CE principles. However, the development of contractor selection models for OSC based on CE principles remains underexplored [6]. The existing literature primarily encompasses foundational definitions, factors, and strategies [1], the adaptation of technologies to CE principles [10], platform or framework proposals [11,12], and other dimensions of circular economy investigation. Therefore, the selection of contractors who are skilled in implementing CE principles through the proposed model can significantly enhance the adoption of these principles within construction projects [13].
- As robust multi-criteria decision-making (MCDM) methods, the fuzzy analytic hierarchy process (FAHP) and fuzzy TOPSIS techniques were integrated and employed for contractor selection in OSC. The hybrid fuzzy MCDM approach of criteria evaluation and alternative assessment stands as one of the most frequently employed methodologies in contractor selection [14]. While MCDM methods have been widely applied in various selection problems, including supplier selection, to the best of our knowledge, this study represents one of the first attempts to specifically employ a hybrid fuzzy MCDM approach for contractor selection in OSC. By utilizing this hybrid model, decision-makers aiming to achieve CE objectives can enhance the reliability of selection processes and improve circularity-driven decision-making.
- Among the barriers to OSC, “the lack of skilled contractors” ranks in the top three according to the literature [15]. There exists a gap in the literature regarding this issue in studies related to OSC. Contractor selections have been explored in the construction industry for different project types (i.e., infrastructure projects like road works, tunnel projects, superstructures, etc.) [16]. Therefore, this study represents a pioneering and original contribution to the field by addressing contractor selection in OSCs, such as prefabrication, modular construction, and industrialized building systems [17].
2. Literature Review
2.1. Circular Economy and the Construction Industry
2.2. Circular Economy and Its Relationship with Off-Site Construction Projects
2.3. Contractor Selection Studies for Construction Projects
3. Research Methodology
3.1. Validation of CE-Based Contractor Selection Criteria with Focus Group Discussion
3.2. Application of Hybrid Fuzzy MCDM in Circular Contractor Selection
3.2.1. Fuzzy AHP Application for MCDM
3.2.2. Fuzzy TOPSIS Application for MCDM
4. Results
4.1. Fuzzy AHP Analysis Results
4.2. Fuzzy TOPSIS Analysis Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Philosophical Approaches of Circular Economy | Explanation of the Philosophy | References |
---|---|---|
Material flow model | The material flow model consists of three interconnected cycling loops, encompassing natural materials, bio-compatible materials, and non-bio-compatible materials. | [23] |
3R framework | Established an economic mechanism to replace the concept of “end-of-life” with the reduction, reuse, and recycling of materials in manufacturing, distribution, and consumption processes. | [24] |
4R framework | Official EU policy framework for CE, adding “recovery” to the most commonly 3Rs (reduction, reuse, recycling). | [1] |
6Rs framework | The R concept is expanded to the 6Rs (with the addition of redesign and remanufacture) | [25] |
9Rs framework | Later evolved into the R concept (with the further addition of refurbish, repair, and refuse). | [26] |
10Rs framework | Principles of sustainable resource management and waste reduction. | [27,28] |
Slowing resource loops | Extends the lifespan of products and slows down the flow of resources by promoting long-lasting goods and product life extension services like repair and remanufacturing. | [1] |
Closing resource Loops | Value creation involves recovering and reusing building products from end-of-service-life buildings. | [21] |
Narrowing Resource Loops | Reducing resource consumption in products and production processes. Reusing materials requires techniques like recycling. Unlike slowing loops, it does not affect product flow speed or entail service loops like repairs. | [29] |
Macro-system | This perspective emphasizes the necessity to adapt the industrial composition and structure of the entire economy. | [1] |
Meso-systems | The typical perspective centers on eco-industrial parks as systems. | [30] |
Micro-systems | Examine goods, individual businesses, and the necessary steps for enhancement. | [31] |
ReSOLVE | The framework applies six circular principles (regenerate, share, optimize, loop, virtualize, and exchange) to improve asset use, extend lifespan, and shift resource use from finite to renewable sources. | [32] |
6 CE Principles | It defines a fundamental collection of six CE principles (system thinking, stewardship, transparency, collaboration, innovation, value optimization) to which all organizations are recommended to adhere. | [20] |
CE implementation in businesses, organizations, and production systems. | The implementation of sustainable consumer products in businesses, using indicators for restoration, regeneration, resource efficiency, climate, energy, and value preservation. | [19] |
CE monitoring framework | The proposed framework categorizes and classifies indicators based on the logic of what and how, encompassing functions, products, components, materials, embodied energy, and a reference scenario. | [33] |
The Focus of the Study | Project Type | Numbers of Criteria and Contractors | Method for Evaluating Criteria | Method for Evaluating Alternative | Reference |
---|---|---|---|---|---|
Used AHP and ANP to prioritize competitive contractors | Bridge | 48/4 | Fuzzy Delphi | AHP and ANP | [48] |
Using the OPA for selecting contractors in construction projects, overcoming MCDM limitations. | Construction | 20/4 | OPA using Delphi and PCA | OPA-based ranking | [49] |
Selection of a green contractor for the solar power plant project. | Solar power plant | 6/6 | SWARA | CRADIS | [50] |
Machine-learning-based framework for contractor selection considering sustainability, risk, and safety. | Public | 8/15 | Machine Learning models | Multi-objective math model | [51] |
Performance analysis models for CS with decision support and consistency control. | Construction | 8/- | Fuzzy AHP | Fuzzy AHP | [52] |
Investigating critical risk factors in selecting joint venture contractors | Infrastructure | 32/- | Factor analysis and risk ranking | Expert judgment and survey-based | [53] |
Enhancing contractor selection through fuzzy techniques. | Public | 8/7 | Fuzzy Buckley method | Fuzzy TOPSIS and fuzzy SAW | [54] |
Community-responsive model for post-disaster reconstruction using ‘Build Back Better’. | Post-disaster reconstruction | 39/4 | BBWM | Fuzzy VIKOR | [55] |
Systematic model for contractor prequalification. | Green building | 25/6 | AHP | AHP | [56] |
Used historical procurement data, to create a pre-tendering CS analysis model. | Historical residential | 3/38 | PreCSAM | PreCSAM | [57] |
Handles bid evaluation complexities by introducing input-output indices and using DEA cross-efficiency and AHP for decisions. | Construction | 7/10 | AHP | DEA | [58] |
Presents a DM framework to indicate the best-fit contractor for the IPD project. | Highway | 13/- | FIS modeling | Fuzzy Toolbox | [59] |
Developing a sustainable circular contractor selection model. | Hydropower | 36/4 | Fuzzy AHP | Fuzzy TOPSIS | [8] |
Established a robust framework, focusing on contractor prioritization and various criteria. | Residential | 40/3 | Standard Deviation | MAUT | [60] |
A unique model based on HFSS and LINMAP developed. | Infrastructure | 14/3 | LINMAP | Similarity Measure | [61] |
Indetermination, imprecision, and uncertainty of bid evaluations. | Reconstruction | 10/5 | HFLTSs | ELECTRE III | [62] |
Developing an AHP with PROMETHEE model together. | Construction | 7/5 | AHP | PROMETHEE | [63] |
Developing a CE-based DM model for contractor selection | Off-site Construction | 29/4 | Fuzzy AHP | Fuzzy TOPSIS | Current study |
Expert | Degree | Education | Expertise | Stakeholder Type | Experience (in Years) |
---|---|---|---|---|---|
E1 | M.Sc. | Civil Eng. | Project control management | Contractor | TE: 10, ME: 5, OSC: 5, CE: 6 |
E2 | B.Sc. | Civil Eng. | Project control management | Owner | TE: 10, ME: 5, OSC: 5, CE: 5 |
E3 | Ph.D. | Civil Eng. | Project control management | Owner | TE: 15, ME: 6, OSC: 10, CE: 5 |
E4 | M.Sc. | Civil Eng. | Project management | Contractor | TE: 12, ME: 5, OSC: 10, CE: 6 |
E5 | M.Sc. | Architecture | Sustainable construction | Consultant | TE: 10, ME: 5, OSC: 5, CE: 5 |
E6 | Ph.D. | Civil Eng. | Project planning and scheduling | Contractor | TE: 14, ME: 6, OSC: 7, CE: 5 |
E7 | M.Sc. | Geotechnical Eng. | Geotechnical engineering | Designer | TE: 17, ME: 7, OSC: 15, CE: 8 |
E8 | Ph.D. | Civil Eng. | Project management | Owner | TE: 26, ME: 16, OSC: 16, CE: 5 |
E9 | Ph.D. | Civil Eng. | Contract management | Contractor | TE: 10, ME: 5, OSC: 5, CE: 5 |
E10 | Ph.D. | Civil Eng. | Project planning and scheduling | Consultant | TE: 19, ME: 17, OSC: 8, CE: 7 |
E12 | M.Sc. | Civil Eng. | Project management | Owner | TE: 12, ME: 5, OSC: 5, CE: 5 |
E13 | M.Sc. | Civil Eng. | Project management | Sub-Contractor | TE: 22, ME: 10, OSC: 13, CE: 5 |
E14 | Ph.D. | Environmental Eng. | Sustainable construction | Consultant | TE: 25, ME: 15, OSC: 10, CE: 9 |
E15 | M.Sc. | Civil Eng. | Project manager | Sub-contractor | TE: 11, ME: 5, OSC: 10, CE: 5 |
Main CE Indicators | ID | Criteria for Contractor Selection | Descriptions | References |
---|---|---|---|---|
Functional Circularity | FC1 | Organization structure and CE model | The function of a contractor includes project management, which can influence circularity by being supported with CE approaches like 10R, 6 CE principles, or circular economy sub-business models. | [27] |
FC2 | Social progress | Social factors can impact the functions and production performance of contractors, thereby indirectly affecting circularity. | [68] | |
FC3 | Procurement method experience in IPD and CM procurement | In a comprehensive study conducted on OSC projects, IPD and CM were selected as the best project delivery systems. This pertains to management strategies and experience aimed at achieving CE objectives. | [42] | |
FC4 | Onsite circularity | Although on-site tasks are minimized when selecting OSC, they should still be conducted by circularity functions. | [69] | |
Product Circularity | PC1 | Green procurement | The green procurement process, which encourages more efficient use of material resources, prefers environmentally friendly products, and reduces waste, influences circularity in construction projects. | [70] |
PC2 | Supply chain circularity | Involved in integrating circular economy principles in product design’s early stages, encouraging consumer involvement in the circular economy, and establishing clear criteria for CE partnerships. | [13,44] | |
PC3 | Product control for defective manufacture | In OSC projects, distances are planned considering carbon emissions from transportation. Repeated transport due to product errors increases the carbon footprint and lowers the circularity rate. | [7,71] | |
PC4 | Product-as-a-service models. | Instead of purchasing certain products, the option of leasing or utilizing them as a service is offered, allowing the provider to handle the maintenance, repair, and end-of-life management, thus contributing to the CE. | [21,33] | |
Componential Circularity | CC1 | Usability of components in different projects. | Disassembling components for reuse in different projects extends the product’s lifespan, thereby increasing resource efficiency and contributing to the circular economy. | [7,43] |
CC2 | Enhance building structure durability through components. | Increasing the durability of building structures allows structural elements to be used for longer periods, thus reducing construction waste and material inefficiency, resulting in cost savings in the long term. | [18] | |
CC3 | Prioritization of modular elements. | The use of standardized and modular elements requires a smaller variety of materials and components. This situation facilitates production processes, reduces costs, and enables easy maintenance, repair, and reuse or upgradability of components at the end of their life. | [72] | |
CC4 | Employing reversible interconnections among components in varied layers | The capability to efficiently and autonomously remove, adapt, reuse, repair, renew, or replace components with abbreviated life spans significantly impacts CE. | [7,18] | |
CC5 | Having a disassembly manual document for the structure | Even after the end of use, dismantling and recycling of components contain crucial information. Ease of recycling in the CE provides opportunities such as waste reduction, efficient resource utilization, and increased reuse. | [13,73] | |
Material Circularity | MC1 | Resource consumption and integrated utilization rate | When determining the consumption rate of different resource types in a project, consideration is given to how these resources are used together. In addition to calculating mineral and energy consumption expressed as unit GDP, the evaluation also considers the combined and efficient utilization of resources. | [74] |
MC2 | Reduction in material waste and the lean production chain | The aim is to reduce waste generated during the construction process and establish an efficient, low-waste production chain. This criterion aims to increase material usage efficiency through waste reduction strategies and the optimization of material flow. | [43,75] | |
MC3 | Utilization of recycled raw materials | Using engineered wood and recycled-content concrete among other bio-based materials enhances circularity. | [76] | |
MC4 | Building material passport document for the project | Resource usage in circular construction should be monitored, evaluated, and optimized. Having detailed and accessible information is indispensable for recovering and reusing building components and materials. | [11,35] | |
MC5 | Assess material toxicology through the Bill of Materials (BoL) and mitigate monstrous hybrids. | Determining material toxicology via the BoL facilitates the preference for more environmentally friendly and healthier materials. This eases the adoption of safer, eco-friendly, and sustainable products, preventing the use of harmful materials and thus contributing to a CE. | [72,77] | |
Energy Circularity | EC1 | Using renewable energy in production | Integrating renewable energy sources into production reduces fossil fuel dependency and carbon footprint, promoting sustainability in CE. | [41,68] |
EC2 | Production machinery or plant efficiency | Enhancing machinery and plant efficiency minimizes energy use and waste by adopting energy-saving technologies and optimizing workflows within CE. | [69] | |
EC3 | Provide sufficient information for LCA | Conducting Life Cycle Assessments (LCAs) helps evaluate a project’s carbon performance, identifying carbon-intensive materials and aiding in the development of carbon reduction strategies to contribute to the CE. | [37,40] | |
EC4 | Transportational energy | The amount of energy used during the transportation of modular building components to the construction site affects Energy Circularity through transportation. The distance and the type of logistics used significantly impact the amount of energy consumed. | [7] | |
EC5 | Use of waste organic material | The criterion examined under the biological circularity of the CE contributes to circularity through the transformation or conversion of biogas into energy. Examples of organic waste include food waste, agricultural residues, wood waste, and similar organic materials. | Expert evaluation | |
Referential Circularity | RC1 | Financial resilience | The effective management of the project’s financial resources involves budgeting, allocation of financial resources, and cost control. | [14] |
RC2 | Technological capability and innovation readiness | The contractor’s existing technical infrastructure includes innovative tools, digital systems, and technological advancements in business processes (such as the use of BIM, big data analysis, etc.). | [11] | |
RC3 | Quality assurance | Monitoring and determining quality standards throughout the lifecycle of materials used in circular construction projects, from production to recycling processes, contributes to the longevity of products, increases their recycling potential, and reduces waste, thereby helping achieve the goals of the CE. | [41,70] | |
RC4 | Experience in construction projects | Internal expertise reduces the risk levels in completed tasks. This enhances efficiency in construction projects, thereby enabling gains in resources, time, and economy. | [41,63] | |
RC5 | Occupational safety and workforce wellbeing | It concerns the contractor’s ability to uphold occupational health and safety standards, implement procedures, and ensure safe working conditions for the personnel. | [41] |
Linguistic Variables | TFN | TFR | Linguistic Variables | TFN | ||
---|---|---|---|---|---|---|
Fuzzy AHP | Equally important | (1, 1, 1) | (1, 1, 1) | Fuzzy TOPSIS | Worst | (0, 0, 1) |
Slightly important | (1/2, 1, 3/2) | (2/3, 1, 2) | Very Poor | (0, 1, 3) | ||
Moderately important | (1, 3/2, 2) | (1/2, 2/3, 1) | Poor | (1, 3, 5) | ||
Important | (3/2, 2, 5/2) | (2/5, 1/2, 2/3) | Fair | (3, 5, 7) | ||
Strongly important | (2, 5/2, 3) | (1/3, 2/5, 1/2) | Good | (5, 7, 9) | ||
Extremely important | (5/2, 3, 7/2) | (2/7, 1/3, 2/5) | Very Good | (7, 9, 10) | ||
Excellent | (9, 10, 10) |
Experts | Main Criteria | Functional Circularity | Product Circularity | Componential Circularity | Material Circularity | Energy Circularity | Referential Circularity |
---|---|---|---|---|---|---|---|
E1 | 0.87 | 4.18 | 4.53 | 4.44 | 4.35 | 7.22 | 7.23 |
E2 | 8.97 | 6.90 | 2.25 | 3.67 | 6.22 | 1.58 | 6.78 |
E3 | 2.92 | 7.03 | 4.41 | 5.33 | 7.55 | 1.89 | 5.65 |
E4 | 8.98 | 1.23 | 4.36 | 1.83 | 7.93 | 4.49 | 6.77 |
E5 | 3.81 | 0.28 | 5.80 | 6.03 | 4.60 | 2.19 | 6.01 |
E6 | 9.48 | 4.97 | 5.37 | 2.22 | 7.42 | 1.73 | 9.44 |
E7 | 3.52 | 8.47 | 4.41 | 8.15 | 8.44 | 3.12 | 7.12 |
E8 | 5.43 | 4.98 | 0.00 | 4.92 | 1.74 | 1.83 | 3.58 |
E9 | 3.15 | 1.63 | 3.97 | 5.57 | 8.96 | 4.17 | 2.95 |
E10 | 7.41 | 4.99 | 8.06 | 7.33 | 6.93 | 5.33 | 7.46 |
E11 | 6.02 | 1.63 | 2.25 | 8.93 | 7.45 | 8.15 | 3.88 |
E12 | 8.45 | 5.85 | 6.14 | 6.52 | 9.28 | 4.02 | 6.06 |
E13 | 2.54 | 2.19 | 6.86 | 4.31 | 4.23 | 1.19 | 5.75 |
E14 | 1.29 | 6.62 | 6.86 | 6.96 | 6.93 | 5.23 | 7.35 |
E15 | 1.78 | 6.90 | 2.25 | 3.10 | 6.22 | 1.58 | 8.72 |
Main Criteria and Sub-Criteria | Normalized Weight | Main Ranking | Criteria Ranking | Overall Weight | Rank |
---|---|---|---|---|---|
Functional Circularity | 0.1513 | 5 | |||
FC1 | 0.2236 | 4 | 0.0338 | 17 | |
FC2 | 0.2339 | 3 | 0.0354 | 13 | |
FC3 | 0.2812 | 1 | 0.0426 | 3 | |
FC4 | 0.2612 | 2 | 0.0395 | 9 | |
Product Circularity | 0.1702 | 3 | |||
PC1 | 0.2630 | 1 | 0.0448 | 1 | |
PC2 | 0.2586 | 2 | 0.0440 | 2 | |
PC3 | 0.2372 | 4 | 0.0404 | 6 | |
PC4 | 0.2413 | 3 | 0.0411 | 4 | |
Componential Circularity | 0.1620 | 4 | |||
CC1 | 0.1817 | 5 | 0.0294 | 24 | |
CC2 | 0.2066 | 2 | 0.0335 | 19 | |
CC3 | 0.2163 | 1 | 0.0350 | 15 | |
CC4 | 0.1904 | 4 | 0.0309 | 23 | |
CC5 | 0.2049 | 3 | 0.0332 | 20 | |
Material Circularity | 0.1928 | 1 | |||
MC1 | 0.1927 | 4 | 0.0372 | 12 | |
MC2 | 0.2064 | 3 | 0.0398 | 8 | |
MC3 | 0.2111 | 1 | 0.0407 | 5 | |
MC4 | 0.2089 | 2 | 0.0403 | 7 | |
MC5 | 0.1809 | 5 | 0.0349 | 16 | |
Energy Circularity | 0.1790 | 2 | |||
EC1 | 0.2165 | 2 | 0.0388 | 10 | |
EC2 | 0.1965 | 3 | 0.0352 | 14 | |
EC3 | 0.2166 | 1 | 0.0388 | 11 | |
EC4 | 0.1829 | 5 | 0.0327 | 22 | |
EC5 | 0.1875 | 4 | 0.0336 | 18 | |
Referential Circularity | 0.1447 | 6 | |||
RC1 | 0.2276 | 1 | 0.0329 | 21 | |
RC2 | 0.2083 | 2 | 0.0301 | 25 | |
RC3 | 0.1919 | 4 | 0.0278 | 27 | |
RC4 | 0.1921 | 3 | 0.0278 | 26 | |
RC5 | 0.1801 | 5 | 0.0261 | 28 |
Contractors | CC | Rank |
---|---|---|
A | 0.1416 | 4 |
B | 0.2991 | 2 |
C | 0.3347 | 1 |
D | 0.2246 | 3 |
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Demirbağ, A.T.; Aladağ, H.; Işık, Z.; Skibniewski, M.J. Circular Economy-Based Decision-Making Model for Contractor Selection. Buildings 2025, 15, 1665. https://doi.org/10.3390/buildings15101665
Demirbağ AT, Aladağ H, Işık Z, Skibniewski MJ. Circular Economy-Based Decision-Making Model for Contractor Selection. Buildings. 2025; 15(10):1665. https://doi.org/10.3390/buildings15101665
Chicago/Turabian StyleDemirbağ, Alperen Taha, Hande Aladağ, Zeynep Işık, and Miroslaw J. Skibniewski. 2025. "Circular Economy-Based Decision-Making Model for Contractor Selection" Buildings 15, no. 10: 1665. https://doi.org/10.3390/buildings15101665
APA StyleDemirbağ, A. T., Aladağ, H., Işık, Z., & Skibniewski, M. J. (2025). Circular Economy-Based Decision-Making Model for Contractor Selection. Buildings, 15(10), 1665. https://doi.org/10.3390/buildings15101665