Next Article in Journal / Special Issue
An Overview of Smart Materials and Technologies for Concrete Construction in Cold Weather
Previous Article in Journal / Special Issue
Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Eng
Volume 4
Issue 2
10.3390/eng4020088
eng-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Integrating Multi-Criteria Decision-Making Methods with Sustainable Engineering: A Comprehensive Review of Current Practices

by
Anđelka Štilić
1,* and
Adis Puška
2
1
The College of Tourism, Academy of Applied Studies Belgrade, Bulevar Zorana Đinđića 152a, 11070 Belgrade, Serbia
2
Department of Public Safety, Government of Brčko District, 76100 Brčko, Bosnia and Herzegovina
*
Author to whom correspondence should be addressed.
Eng 2023, 4(2), 1536-1549; https://doi.org/10.3390/eng4020088
Submission received: 3 May 2023 / Revised: 24 May 2023 / Accepted: 30 May 2023 / Published: 31 May 2023
(This article belongs to the Special Issue Feature Papers in Eng 2023)
Browse Figures
Versions Notes

Abstract

:
Multi-criteria decision-making (MCDM) methods have gained increased attention in sustainable engineering, where complex decision-making problems require consideration of multiple criteria and stakeholder perspectives. This review paper provides a comprehensive overview of the different MCDM methods, their applications in sustainable engineering, and their strengths and weaknesses. The paper discusses the concept of sustainable engineering, its principles, and the different areas where MCDM methods have been applied, including energy, manufacturing, transportation, and environmental engineering. Case studies of real-world applications are presented and analyzed, highlighting the main findings and implications for engineering practice. Finally, the challenges and limitations of MCDM methods in sustainable engineering are discussed, and future research directions are proposed. This review contributes to the understanding of the role of MCDM methods in sustainable engineering and provides guidance for researchers and practitioners.

1. Introduction

Multi-criteria decision-making (MCDM) methods have become a necessary tool throughout contemporary engineering practice [1]. They enable decision-makers to assess complex problems involving multiple criteria, trade-offs, and uncertainties. The methods used in MCDM are especially beneficial when applied to sustainable engineering, where decision-making requires balancing economic, environmental, and social considerations [2,3].
Sustainable engineering aims to design and implement engineering solutions that are environmentally friendly, socially acceptable, and economically viable [4]. Achieving sustainability requires the consideration of multiple criteria, such as resource conservation, pollution prevention, energy efficiency, economic feasibility, social equity, and stakeholder participation [5,6,7]. Sustainable engineering challenges decision-makers to balance these criteria and make trade-offs among them to identify the best solutions [8].
MCDM methods can assist in the decision-making process by offering an organized and transparent framework for assessing alternative solutions based on a variety of criteria. These methods enable decision-makers to identify optimal solutions by conducting quantitative and qualitative assessments, taking into account the preferences and priorities of different stakeholders [9].
The purpose of this review paper is to provide a thorough overview of the different MCDM methods and their applications in sustainable engineering. The review explores how MCDM methods could be utilized in various areas of sustainable engineering, including energy, manufacturing, transportation, and environmental engineering. The review also analyzes case studies of real-world applications of MCDM methods and highlights the strengths and weaknesses of each approach.
The motivation for this review is to address the need for a comprehensive and up-to-date overview related to MCDM methods regarding sustainable engineering. The need arises from the increasing importance of sustainability in contemporary engineering practice [1] and the growing complexity of decision-making problems [10]. This review paper seeks to contribute to the existing literature by highlighting the most effective MCDM methods for sustainable engineering problems and by proposing future research directions.
Its primary objectives are:
  • To offer a comprehensive overview of different MCDM approaches and how they are used in sustainable engineering;
  • To analyze case studies of real-world applications of MCDM methods in sustainable engineering and to highlight their outcomes;
  • To identify the strengths and weaknesses of each MCDM technique in sustainable engineering and to compare and contrast them;
  • To propose future research directions and discuss how MCDM methods can be further developed to enhance their effectiveness and applicability in sustainable engineering.
This review paper aims to contribute to the ongoing efforts to develop sustainable engineering solutions by providing decision-makers with a framework for selecting the most effective MCDM methods for their specific problems. By doing so, the review paper aims to enhance the effectiveness and applicability of MCDM methods in sustainable engineering and grow what is currently the state-of-the-art in the field.
To achieve the objectives, the review paper is organized in the following manner. After the Introduction, Section 2 is presented. The Primary Results are presented, followed by a presentation of the Detailed Review Results. A summary of the principles and applications of MCDM methods is presented, highlighting their importance and relevance to sustainable engineering. Next, the specific areas of sustainable engineering where MCDM methods have been applied are discussed, and the outcomes achieved are reviewed. Case studies of real-world applications of MCDM methods in sustainable engineering are then presented, and the main findings and implications for engineering practice are analyzed. Following this, the challenges and limitations of MCDM methods in sustainable engineering are then discussed, and prospective research recommendations are proposed. Finally, the main contributions of this review paper are summarized, and the implications for decision-making in sustainable engineering are suggested.

2. Materials and Methods

The knowledge used to conduct this research was obtained from various sources, including published research papers, technical reports, and case studies in academic journals. A thorough search was conducted using the Web of Science (WoS) Core Collection Database, as well as the online EBSCO Discovery Service engine. The search terms used included MCDM, sustainability, and sustainable engineering, with various combinations of these keywords also utilized. The search was limited to papers published between 2018 and 2023, written in English, and focused on MCDM methodologies employed for sustainable engineering.
After an initial screening of search results based on the titles and abstracts, a total of 36,490 articles associated with MCDM methods across different disciplines was identified, with 12,879 of these articles specifically addressing MCDM methods in sustainable engineering.
The case studies presented in this paper were selected based on their relevance and representativeness to the applications of MCDM methodologies in sustainable engineering. The cases were analyzed using a systematic approach to identify the decision-making problems, the criteria used, and the outcomes achieved. The case studies were also used to illustrate the strengths and limitations of various MCDM methods, as well as to identify the challenges and opportunities for future research in this area.

3. Primary Results

The publishers of articles pertaining to the use of MCDM methods across various fields of sustainable engineering are diverse and include well-known names such as Springer Nature, Elsevier, Wiley-Blackwell, and Taylor & Francis Ltd. (Figure 1). However, the publisher with the most articles published in this field is MDPI, with 4264 articles. Other publishers with a significant number of articles include Hindawi Limited, Emerald Publishing Limited, and IOS Press. The list also includes smaller publishers such as the Rural Outreach Program and Dr. M.N. Khan, indicating a wide range of contributors to this field. The diversity of publishers reflects the multidisciplinary nature of sustainable engineering, where different fields intersect and collaborate to achieve sustainable solutions.
The journal Sustainability has the most publications on the topic, with 2290 articles, followed by the Journal of Intelligent & Fuzzy Systems and Mathematical Problems in Engineering, with 440 articles each (Figure 2). PLoS ONE and Energies also have a significant number of publications, with 401 and 220 articles, respectively. The topics covered by the publications include environmental management, energy production and consumption, transportation, water management, and quality and reliability management, among others. The use of MCDM methods allows for the consideration of multiple criteria in decision-making, which is considered essential in achieving sustainability in engineering practices.
The assortment of literature pertaining to the application of MCDM methods in sustainable engineering covers a wide range of subjects, as shown in the list of the most frequent keywords (Figure 3). Decision-making and MCDM are the most common subjects, with a total of 1433 and 1318 articles, respectively, followed closely by the analytic hierarchy process (AHP), with 1293 articles. Sustainability, sustainable development, fuzzy sets, and supply chains are also important subjects, with over 500 articles each. Other notable subjects include risk assessment, renewable energy sources, geographic information systems, fuzzy logic, and multi-criteria decision-making. The literature also covers specific applications such as company business management, construction projects, logistics, waste management, water supply, and power resources. The research regarding the use of MCDM methods in sustainable engineering is diverse and covers a wide range of subjects, reflecting the broad scope of sustainable engineering as a field of study.
In sustainable engineering, the researched literature demonstrates that the researchers have frequently employed MCDM methods to address complex decision-making challenges. AHP has been the most frequently used method, with 1986 articles published, followed by TOPSIS (939), ANP (281), and DEMATEL (227). DEMATEL, BWM, and VIKTOR have been used in sustainable engineering, with 227, 174, and 168 articles published, respectively. Finally, Fuzzy sets have been widely used in various fields, with 1471 articles published, and Fuzzy AHP and Fuzzy TOPSIS are also popular. These methods (Figure 4) have been used to address various decision-making problems in sustainable engineering, ranging from environmental management to energy management.

4. Detailed Review Results

4.1. Sustainable Engineering

Sustainable engineering is a multidisciplinary approach [7] to designing and managing engineering systems that meets the demands of present-day society without jeopardizing future generations’ ability to meet their specific requirements. The concept of sustainability has its roots in environmentalism and conservationism [11], but it has evolved to encompass social and economic aspects as well [12]. Sustainable engineering considers the environmental, social, and economic impacts of engineering systems throughout their entire life cycle, from design and construction to operation and decommissioning [13,14]. The goal of sustainable engineering is to create systems that are resilient [15,16,17], adaptive [18], and regenerative [19], and that contribute to the well-being of humans and the planet.
The principles of sustainable engineering include minimizing resource use and waste generation, reducing carbon emissions and other environmental impacts, enhancing social equity and inclusion, promoting economic prosperity and resilience, and embracing systems thinking and innovation [20,21,22]. Sustainable engineering is essential in contemporary engineering practice as it addresses the challenges of climate change, resource depletion, population growth, and urbanization, and contributes to the fulfillment of the Sustainable Development Goals established by the United Nations [23,24].
The use of MCDM methods in sustainable engineering is motivated by the need to make informed decisions that balance environmental, social, and economic considerations, and that account for the interdependencies of different criteria and stakeholders. MCDM methods provide a systematic and transparent approach to evaluating alternatives and trade-offs, considering multiple criteria and preferences, and identifying the most preferred options [25]. The use of MCDM methods in sustainable engineering has increased in recent years due to advances in computing power, data availability, and stakeholder engagement, as well as the growing recognition of the significance of sustainability in engineering practice [26].
MCDM methodologies have been adopted in various areas of sustainable engineering, including energy systems [27,28,29,30,31], transportation systems [32,33,34,35,36], water and wastewater systems [37,38,39,40,41], building design and construction [42,43,44,45,46], and industrial processes [47,48,49,50,51]. These applications aim to identify the most sustainable options among a set of alternatives, considering various criteria and stakeholders’ preferences. For example, different MCDM methods were put to use to identify the most adequate renewable energy technology for a given location, considering technical, economic, environmental, and social criteria [31]. MCDM methods have also been used to identify the sustainability performance of buildings and infrastructure projects, considering criteria such as energy efficiency, carbon emissions, water use, and social and economic impacts [44].
The outcomes of these applications have shown that MCDM methods can provide valuable insights into the sustainability trade-offs and synergies among different criteria and alternatives, and can support informed decision-making that balances environmental, social, and economic considerations. However, the success of MCDM methods in sustainable engineering depends on the quality and availability of data, the validity and reliability of the criteria and indicators used [52,53,54], and the participation and engagement of stakeholders in the decision-making process [55,56,57]. To address these challenges, ongoing research is focused on developing more sophisticated MCDM methods that can handle complex and uncertain data, incorporate dynamic and feedback processes, and integrate qualitative and quantitative information [58,59,60,61,62].

4.2. MCDM Methods

MCDM methods are a set of tools used to evaluate alternatives that satisfy multiple criteria or objectives. In sustainable engineering, MCDM methods are widely used to support decision-making processes that involve complex and conflicting criteria such as environmental impact, economic viability, social equity, and technological feasibility. MCDM methods aim to provide an organized and transparent framework for assessing alternatives by considering a variety of criteria and identifying the most preferred alternative.
There are many different MCDM methods, each with its own set of theoretical foundations and applications. Some of the most commonly used MCDM methods in sustainable engineering are listed hereinafter.
The Analytic Hierarchy Process (AHP), developed by Thomas L. Saaty in the 1970s, is a widely used method for decomposing a complex decision problem into a hierarchy of simpler sub-problems and evaluating the relative importance of each criterion and alternative [63]. AHP is particularly useful when the decision problem is complex and involves a large number of criteria and alternatives [64]. AHP has been used in 1986 articles pertaining to the application of MCDM in sustainable engineering, indicating its popularity.
The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), developed by Hwang and Yoon [65], is a method for ranking alternatives based on their distance from the ideal solution to the worst solution. The TOPSIS has been widely used in various fields, including sustainable engineering, with 939 articles on its application in MCDM methods.
Fuzzy sets are used to represent imprecise and uncertain information in decision-making processes [66]. Fuzzy logic, which is based on fuzzy sets theory, is useful for handling uncertainty and imprecision in decision-making [67]. Fuzzy sets and fuzzy logic have been used in 1471 articles pertaining to the application of MCDM in sustainable engineering. Fuzzy AHP and Fuzzy TOPSIS are two commonly used extensions of AHP and TOPSIS that incorporate fuzzy sets.
The Analytic Network Process (ANP), developed by Saaty [68], is a generalization of the AHP that can model feedback and dependence among criteria and alternatives [69]. The ANP has been used in 281 articles pertaining to the application of MCDM in sustainable engineering.
The Decision-Making Trial and Evaluation Laboratory (DEMATEL), developed by Gabus and Fontela [70], is a method for modeling and analyzing the causal relationships between criteria and alternatives [71]. The DEMATEL has been used in 227 articles pertaining to the application of MCDM in sustainable engineering.
The Best Worst Method (BWM), developed by Rezaei [72], is a method for evaluating and ranking alternatives based on their best and worst performance with respect to a set of criteria [73]. The BWM has been used in 174 articles pertaining to the application of MCDM in sustainable engineering.
The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, published by Duckstein and Opricovic [74], is a method for ranking and selecting alternatives based on their proximity to the ideal and anti-ideal solutions. VIKOR has been used in 168 articles pertaining to the application of MCDM in sustainable engineering.
Grey Relational Analysis (GRA), developed by Deng [75], is a method for analyzing and ranking alternatives based on their similarities and differences with respect to a reference alternative. GRA has been used in 106 articles pertaining to the application of MCDM in sustainable engineering.
The entropy method is a method for weighting criteria based on their relative importance and uncertainty [76]. It has been used in 122 articles pertaining to the application of MCDM in sustainable engineering.
Each MCDM technique has its own strengths and weaknesses, depending on the specific problem and application. The choice of MCDM technique depends on the specific problem and application, as well as the availability of data, and stakeholder preferences [77]. Therefore, it is essential to carefully evaluate the strengths and weaknesses of each technique and select the most appropriate one for the given problem and context.
In addition to the specific problem and application, the availability of data and expertise can also influence the choice of MCDM technique. For example, some MCDM methods, such as AHP and TOPSIS, require pairwise comparison matrices [78] that may be difficult to obtain or may involve subjective judgments. Other methods, such as fuzzy logic and entropy, can handle uncertain and imprecise information, but may require significant expertise in fuzzy set theory or information theory [79]. Therefore, it is important to consider the availability and quality of data and expertise when selecting an MCDM technique.
Stakeholder preferences can also influence the choice of MCDM methods. Different methods may be more suitable for different types of stakeholders or decision contexts. For example, PROMETHEE and ELECTRE are particularly useful for handling conflicting preferences and priorities among stakeholders [80], while fuzzy logic can be useful for representing vague or ambiguous preferences [81]. Therefore, it is important to include stakeholders in the decision-making process and consider their preferences and perspectives when selecting an MCDM technique. By carefully evaluating the strengths and weaknesses of each technique and selecting the most appropriate one for the given problem and context, decision-makers can make more informed and effective decisions that balance multiple criteria and objectives.

4.3. Case Studies: Applications of MCDM Methods in Sustainable Engineering

In this section, exemplary case studies of real-world applications of MCDM methods in sustainable engineering are presented. The presented case studies aim to provide a deeper understanding of how MCDM methods could be utilized to address complex decision-making problems in different areas of sustainable engineering.
MCDM methods have been frequently employed in various areas of sustainable engineering to support decision-making that considers multiple criteria and stakeholders’ preferences. In this section, we will discuss some specific examples of MCDM applications in energy, manufacturing, transportation, and environmental engineering, highlighting the criteria considered and the outcomes achieved.
For example, energy engineering is an area where MCDM methods have been extensively utilized to determine the most sustainable options among different renewable and non-renewable energy sources [82,83,84], considering technical, economic, environmental, and social criteria. For instance, MCDM methods have been used to select the most appropriate renewable energy technology for a given location, taking into account factors such as resource availability, technical feasibility, economic viability, and social acceptance. These methods have been applied to various renewable energy sources, including solar, wind, geothermal, and hydroelectric power. The outcomes of these applications have shown that MCDM methods can provide valuable insights into the trade-offs among different criteria and help identify the most sustainable options. Additionally, a study by Alhakami [85] addresses the need for a comprehensive security evaluation approach and proposes an MCDM methodology to assess security risks in power control technology and communication networks of energy management and control systems.
Manufacturing and production engineering is another area in which MCDM methods have been used to support sustainable decision-making. For example, MCDM methods have been used to evaluate the sustainability performance of manufacturing processes, considering criteria such as energy efficiency, waste generation, water use, and social and economic impacts [86,87,88]. These methods have also been applied to support product design and development [89,90,91], considering criteria such as material selection, energy consumption, and end-of-life disposal. The outcomes of these applications have shown that MCDM methods can help to identify the most sustainable manufacturing processes and products and support the transition towards a circular economy. Furthermore, applying these methods in the specific context [92] can enhance sustainable decision-making practices.
Transportation engineering is a critical area for sustainable engineering as transportation systems are responsible for a significant portion of greenhouse gas emissions and other environmental impacts [93]. MCDM methods have been used to support decision-making in transportation engineering [94,95,96], considering criteria such as energy efficiency, emissions reduction, safety, and social and economic impacts. For example, MCDM methods have been used to evaluate the sustainability performance of different modes of transportation, such as cars [97], buses [98], trains [99], and airplanes [100], and to identify the most sustainable options for a given transportation problem. These methods can also be applied to support the design and planning of transportation infrastructure, such as roads [101], bridges [102], and airports [103], considering criteria such as energy consumption, environmental impacts, and social and economic benefits, and even assess potential suppliers based on their ability to address specific challenges such as the COVID-19 epidemic [104]. Furthermore, MCDM methodologies are also proposed to monitor customer satisfaction in the airline service industry, aiming to enhance service quality and meet consumer expectations [105].
Environmental engineering is a broad area that encompasses various disciplines, such as water and wastewater treatment, air pollution control, and solid waste management. MCDM methods have been used to support decision-making in environmental engineering, considering criteria such as environmental impacts, economic costs, and social benefits. For example, MCDM methods have been used to identify the most sustainable options for water and wastewater treatment [106,107,108,109,110], considering criteria such as treatment efficiency, energy consumption, and social acceptance. These methods can also be applied to support the management of solid waste [111], considering criteria such as waste reduction, recycling, and disposal options. In a similar manner, the evaluation of environmental quality in specific contexts [112] utilizes fuzzy MCDM methods incorporating multiple factors to guide decision-making in environmental protection research and future renovation planning, and the compatibility between MCDM methods in assessing erosion risk highlights the fuzzy methods as an effective tool for evaluating erosion risk in semi-arid areas and guiding erosion prevention actions [113].
The applications of MCDM methods in sustainable engineering have shown that these methods can provide valuable insights into the sustainability trade-offs and synergies among different criteria and alternatives, and can support informed decision-making that balances environmental, social, and economic considerations. However, the success of these applications depends on the quality and availability of data, the validity and reliability of the criteria and indicators used, and the participation and engagement of stakeholders in the decision-making process.

5. Challenges and Future Directions

MCDM methods face several challenges when applied to sustainable engineering problems. One of the key challenges of MCDM methods is the availability and quality of data. Sustainable engineering problems often involve multiple criteria and sources of information [114], and it can be difficult to obtain reliable data that represent the complexity of the problem. In addition, the data may be incomplete, inconsistent, or subjective, which can affect the decision-making process’ reliability and accuracy [115].
Another challenge of MCDM methods is model uncertainty. Many of these methods are based on mathematical models that may not accurately reflect the complexity and dynamics of sustainable engineering problems [116]. This can lead to errors in the estimation of criteria weights, rankings, and overall scores, which can affect the credibility and acceptability of the decision-making process.
MCDM methods also face challenges related to stakeholder engagement. Sustainable engineering problems often involve multiple stakeholders with different perspectives, values, and interests [117]. It can be difficult to engage stakeholders effectively in the decision-making process and to ensure that their voices are heard and their concerns are addressed. In addition, stakeholders may have different levels of expertise and understanding of the decision-making process and the MCDM approaches employed, which can affect the quality and acceptability of the decision.
Despite these challenges, MCDM methods have an opportunity to be vital in sustainable engineering practice. One promising research direction is the blend of MCDM methods, artificial intelligence (AI), and machine learning (ML) methods. AI and ML may enhance the accuracy and efficiency of the decision-making process by enabling the automated processing and analysis of large and complex data sets [118]. The combination of MCDM methods with AI and ML can also facilitate the incorporation of expert knowledge, uncertainty, and risk into the decision-making process.
Another future research direction is the incorporation of dynamic and complex systems into the decision-making process. Many sustainable engineering problems involve complex systems that are characterized by non-linear relationships, feedback loops, and emergent properties. MCDM methods can be further developed to account for these complexities by incorporating methods such as system dynamics, agent-based modeling, and network analysis.
A third future research direction is the enhancement of multi-stakeholder decision-making. This involves developing MCDM methods that can facilitate effective stakeholder engagement by incorporating methods such as participatory decision-making, collaborative modeling, and multi-criteria deliberation. The development of user-friendly and transparent decision support tools can also help to enhance stakeholder engagement and improve the acceptability the process of making decisions.
Finally, upcoming research can concentrate on the development of user-friendly and transparent decision support tools. MCDM methods can be complex and difficult to understand for non-experts, which can limit their use in practice. User-friendly and transparent decision support tools can help to bridge this gap by providing intuitive and accessible interfaces, visualizations, and explanations.
MCDM methods have an important function in encouraging sustainable engineering practices. However, to realize their full potential, it is essential to address the challenges and limitations they face and explore new research directions that can enhance their effectiveness and applicability in real-world decision-making contexts.

6. Conclusions

This review has provided a comprehensive overview of the applications of MCDM methods in sustainable engineering. The review discussed the theoretical foundations and applications of various MCDM methods, including their strengths, weaknesses, and comparisons. It also highlighted the importance of sustainable engineering and discussed the different areas in which MCDM methods have been applied, such as energy, manufacturing, transportation, and environmental engineering. Furthermore, this review presented case studies of real-world applications of MCDM methods in sustainable engineering and analyzed the main findings and implications for engineering practice. Finally, the review discussed the challenges and limitations of MCDM methods in sustainable engineering and proposed future research directions to enhance their effectiveness and applicability.
The review has demonstrated that MCDM methods have the potential to address complex decision-making problems in sustainable engineering by considering multiple criteria and stakeholder perspectives. However, the effective implementation of these methods requires issues related to data availability, model uncertainty, and stakeholder engagement to be addressed. Future research directions include the development of more robust and transparent MCDM models, the integration of new data sources, and the incorporation of emerging technologies such as artificial intelligence and machine learning. The findings of this review have important implications for engineering practice and research and can inform the development of more sustainable and efficient engineering solutions in the future.

Author Contributions

Conceptualization, A.Š. and A.P.; methodology, A.Š.; writing—original draft preparation, A.Š. and A.P.; writing—review and editing, A.Š. and A.P.; visualization, A.Š.; supervision, A.P.; project administration, A.Š. and A.P.; funding acquisition, A.Š. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Stojcic, M.; Zavadskas, E.K.; Pamučar, D.; Stević, Ž.; Mardani, A. Application of MCDM Methods in Sustainability Engineering: A Literature Review 2008–2018. Symmetry 2019, 11, 350. [Google Scholar] [CrossRef]
  2. Banasik, A.; Bloemhof-Ruwaard, J.M.; Kanellopoulos, A.; Claassen, G.; Van Der Vorst, J.G. Multi-criteria decision making approaches for green supply chains: A review. Flex. Serv. Manuf. J. 2018, 30, 366–396. [Google Scholar] [CrossRef]
  3. Guarnieri, P.; Trojan, F. Decision making on supplier selection based on social, ethical, and environmental criteria: A study in the textile industry. Resour. Conserv. Recycl. 2019, 141, 347–361. [Google Scholar] [CrossRef]
  4. Kharat, M.G.; Murthy, S.; Kamble, S.J.; Raut, R.D.; Kamble, S.S.; Kharat, M.G. Fuzzy multi-criteria decision analysis for environmentally conscious solid waste treatment and disposal technology selection. Technol. Soc. 2019, 57, 20–29. [Google Scholar] [CrossRef]
  5. Alhama, C.C.; Igual-Antón, D. Corporate Social Responsibility Strategies in Spanish Electric Cooperatives. Analysis of Stakeholder Engagement. Sustainability 2021, 13, 6810. [Google Scholar] [CrossRef]
  6. Geng, Y.; Fujita, T.; Bleischwitz, R.; Chiu, A.S.; Sarkis, J. Accelerating the transition to equitable, sustainable, and livable cities: Toward post-fossil carbon societies. J. Clean. Prod. 2019, 239, 118020. [Google Scholar] [CrossRef]
  7. Glavič, P. Updated Principles of Sustainable Engineering. Processes 2022, 10, 870. [Google Scholar] [CrossRef]
  8. Durmić, E.; Herzegovina Stević, Ž.; Chatterjee, P.; Vasiljević, M.; Tomašević, M. Sustainable supplier selection using combined FUCOM—Rough SAW model. Rep. Mech. Eng. 2020, 1, 34–43. [Google Scholar] [CrossRef]
  9. Raut, R.D.; Kharat, M.G.; Kamble, S.J.; Kumar, C.M. Sustainable evaluation and selection of potential third-party logistics (3PL) providers. Benchmarking Int. J. 2018, 25, 76–97. [Google Scholar] [CrossRef]
  10. Tang, M.; Liao, H. From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey. Omega 2021, 100, 102141. [Google Scholar] [CrossRef]
  11. Restrepo, J.M.; Morales-Pinzón, T. Urban metabolism and sustainability: Precedents, genesis and research perspectives. Resour. Conserv. Recycl. 2018, 131, 216–224. [Google Scholar] [CrossRef]
  12. Toli, A.M.; Murtagh, N. The Concept of Sustainability in Smart City Definitions. Front. Built Environ. 2020, 6, 77. [Google Scholar] [CrossRef]
  13. Iskandar, M.; Nelson, D.; Tehrani, F.M. Managing Sustainability and Resilience of the Built Environment in Developing Communities. CivilEng 2022, 3, 427–440. [Google Scholar] [CrossRef]
  14. Jamieson, M.V.; Lefsrud, L.; Sattari, F.; Donald, J.A. Sustainable leadership and management of complex engineering systems: A team based structured case study approach. Educ. Chem. Eng. 2021, 35, 37–46. [Google Scholar] [CrossRef]
  15. Marchese, D.; Reynolds, E.; Bates, M.; Morgan, H.; Clark, S.J.; Linkov, I. Resilience and sustainability: Similarities and differences in environmental management applications. Sci. Total Environ. 2018, 613–614, 1275–1283. [Google Scholar] [CrossRef]
  16. Mihelcic, J.R.; Zimmerman, J.B. Environmental Engineering: Fundamentals, Sustainability, Design; John Wiley & Sons: Hoboken, NJ, USA, 2021. [Google Scholar]
  17. Sharma, N.; Tabandeh, A.; Gardoni, P. Resilience analysis: A mathematical formulation to model resilience of engineering systems. Sustain. Resilient Infrastruct. 2018, 3, 49–67. [Google Scholar] [CrossRef]
  18. Thacker, S.; Adshead, D.; Fay, M.; Hallegatte, S.; Harvey, M.S.; Meller, H.; O’Regan, N.; Rozenberg, J.; Watkins, G.; Hall, J.W. Infrastructure for sustainable development. Nat. Sustain. 2019, 2, 324–331. [Google Scholar] [CrossRef]
  19. Suárez-Eiroa, B.; Fernández, E.M.; Méndez-Martínez, G.; Soto-Oñate, D. Operational principles of circular economy for sustainable development: Linking theory and practice. J. Clean. Prod. 2019, 214, 952–961. [Google Scholar] [CrossRef]
  20. Dogaru, L. Green Economy and Green Growth—Opportunities for Sustainable Development. Proceedings 2021, 63, 70. [Google Scholar] [CrossRef]
  21. Lehmann, S. Implementing the Urban Nexus approach for improved resource-efficiency of developing cities in Southeast-Asia. City Cult. Soc. 2017, 13, 46–56. [Google Scholar] [CrossRef]
  22. Sachs, J.D.; Schmidt-Traub, G.; Mazzucato, M.; Messner, D.; Nakicenovic, N.; Rockström, J. Six Transformations to achieve the Sustainable Development Goals. Nat. Sustain. 2019, 2, 805–814. [Google Scholar] [CrossRef]
  23. Štilić, A.; Puška, A.; Đurić, A.; Božanić, D.K. Electric Vehicles Selection Based on Brčko District Taxi Service Demands, a Multi-Criteria Approach. Urban Sci. 2022, 6, 73. [Google Scholar] [CrossRef]
  24. United Nations [UN]. THE 17 GOALS|Sustainable Development. United Nations, Department of Economic and Social Affairs, Sustainable Development. Available online: https://sdgs.un.org/goals (accessed on 2 May 2023).
  25. Bhardwaj, A.; Joshi, M.; Khosla, R.; Dubash, N.K. More priorities, more problems? Decision-making with multiple energy, development and climate objectives. Energy Res. Soc. Sci. 2019, 49, 143–157. [Google Scholar] [CrossRef]
  26. Tseng, M.; Tran, T.H.; Ha, H.M.; Bui, T.; Lim, M.K. Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: A data driven analysis. J. Ind. Prod. Eng. 2021, 38, 581–598. [Google Scholar] [CrossRef]
  27. Bohra, S.S.; Shafie-Khah, M. A comprehensive review on applications of multicriteria decision-making methods in power and energy systems. Int. J. Energy Res. 2021, 46, 4088–4118. [Google Scholar] [CrossRef]
  28. Cao, Q.; Esangbedo, M.O.; Bai, S.; Esangbedo, C.O. Grey SWARA-FUCOM Weighting Method for Contractor Selection MCDM Problem: A Case Study of Floating Solar Panel Energy System Installation. Energies 2019, 12, 2481. [Google Scholar] [CrossRef]
  29. Jahangiri, M.; Shamsabadi, A.A.; Mostafaeipour, A.; Rezaei, M.; Yousefi, Y.; Pomares, L.A. Using fuzzy MCDM technique to find the best location in Qatar for exploiting wind and solar energy to generate hydrogen and electricity. Int. J. Hydrogen Energy 2020, 45, 13862–13875. [Google Scholar] [CrossRef]
  30. Lee, H.; Chang, C. Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renew. Sustain. Energy Rev. 2018, 92, 883–896. [Google Scholar] [CrossRef]
  31. Siksnelyte-Butkiene, I.; Zavadskas, E.K.; Streimikiene, D. Multi-Criteria Decision-Making (MCDM) for the Assessment of Renewable Energy Technologies in a Household: A Review. Energies 2020, 13, 1164. [Google Scholar] [CrossRef]
  32. Ferreira, J.J.; Ilander, G.O.P.; Ferreira, J.J. MCDM/A in practice: Methodological developments and real-world applications. Manag. Decis. 2019, 57, 295–299. [Google Scholar] [CrossRef]
  33. Görçün, Ö.F. Evaluation of the selection of proper metro and tram vehicle for urban transportation by using a novel integrated MCDM approach. Sci. Prog. 2021, 104, 003685042095012. [Google Scholar] [CrossRef] [PubMed]
  34. Kiciński, M.; Solecka, K. Application of MCDA/MCDM methods for an integrated urban public transportation system—Case study, city of Cracow. Arch. Transp. 2018, 46, 71–84. [Google Scholar] [CrossRef]
  35. Moradi, S.; Sierpiński, G.; Masoumi, H.E. System Dynamics Modeling and Fuzzy MCDM Approach as Support for Assessment of Sustainability Management on the Example of Transport Sector Company. Energies 2022, 15, 4917. [Google Scholar] [CrossRef]
  36. Wang, C.; Le, T.; Chang, K.; Dang, T. Measuring Road Transport Sustainability Using MCDM-Based Entropy Objective Weighting Method. Symmetry 2022, 14, 1033. [Google Scholar] [CrossRef]
  37. Ali, Y.; Pervez, H.; Khan, J. Selection of the Most Feasible Wastewater Treatment Technology in Pakistan Using Multi-Criteria Decision-Making (MCDM). Water Conserv. Sci. Eng. 2020, 5, 199–213. [Google Scholar] [CrossRef]
  38. Gichamo, T.; Gökçekuş, H.; Ozsahin, D.U.; Gelete, G.; Uzun, B. Ranking of Natural Wastewater Treatment Techniques by Multi-criteria Decision Making (MCDM) Methods. In Professional Practice in Earth Sciences; Springer International Publishing: Berlin/Heidelberg, Germany, 2021; pp. 87–100. [Google Scholar] [CrossRef]
  39. Narayanamoorthy, S.; Brainy, J.V.; Sulaiman, R.; Ferrara, M.; Ahmadian, A.; Kang, D. An integrated decision making approach for selecting a sustainable waste water treatment technology. Chemosphere 2022, 301, 134568. [Google Scholar] [CrossRef]
  40. Radmehr, A.; Bozorg-Haddad, O.; Loáiciga, H.A. Developing Strategies for Agricultural Water Management of Large Irrigation and Drainage Networks with Fuzzy MCDM. Water Resour. Manag. 2022, 36, 4885–4912. [Google Scholar] [CrossRef]
  41. Zolfaghary, P.; Zakerinia, M.; Kazemi, H. A model for the use of urban treated wastewater in agriculture using multiple criteria decision making (MCDM) and geographic information system (GIS). Agric. Water Manag. 2021, 243, 106490. [Google Scholar] [CrossRef]
  42. Chalekaee, A.; Turskis, Z.; Khanzadi, M.; Amiri, G.G.; Keršulienė, V. A New Hybrid MCDM Model with Grey Numbers for the Construction Delay Change Response Problem. Sustainability 2019, 11, 776. [Google Scholar] [CrossRef]
  43. Haruna, A.; Shafiq, N.; Montasir, O. Building information modelling application for developing sustainable building (Multi criteria decision making approach). Ain Shams Eng. J. 2021, 12, 293–302. [Google Scholar] [CrossRef]
  44. Mathiyazhagan, K.; Gnanavelbabu, A.; Prabhuraj, B.L. A sustainable assessment model for material selection in construction industries perspective using hybrid MCDM approaches. J. Adv. Manag. Res. 2019, 16, 234–259. [Google Scholar] [CrossRef]
  45. Matić, B.; Jovanovic, S.; Das, D.K.; Zavadskas, E.K.; Stević, Ž.; Sremac, S.; Marinković, M. A New Hybrid MCDM Model: Sustainable Supplier Selection in a Construction Company. Symmetry 2019, 11, 353. [Google Scholar] [CrossRef]
  46. Zolfani, S.H.; Pourhossein, M.; Yazdani, M.; Zavadskas, E.K. Evaluating construction projects of hotels based on environmental sustainability with MCDM framework. Alex. Eng. J. 2017, 57, 357–365. [Google Scholar] [CrossRef]
  47. Chandra, M.; Shahab, F.; Kek, V.; Rajak, S. Selection for additive manufacturing using hybrid MCDM technique considering sustainable concepts. Rapid Prototyp. J. 2022, 28, 1297–1311. [Google Scholar] [CrossRef]
  48. Marhavilas, P.K.; Filippidis, M.; Koulinas, G.K.; Koulouriotis, D.E. A HAZOP with MCDM Based Risk-Assessment Approach: Focusing on the Deviations with Economic/Health/Environmental Impacts in a Process Industry. Sustainability 2020, 12, 993. [Google Scholar] [CrossRef]
  49. Nguyen, T.H.O.; Nguyen, P.H.; Pham, H.T.; Nguyen, T.; Nguyen, D.K.; Tran, T.; Le, H.; Phung, H. A Novel Integrating Data Envelopment Analysis and Spherical Fuzzy MCDM Approach for Sustainable Supplier Selection in Steel Industry. Mathematics 2022, 10, 1897. [Google Scholar] [CrossRef]
  50. Nguyen, V.T. Sustainable Energy Source Selection for Industrial Complex in Vietnam: A Fuzzy MCMD Approach. IEEE Access 2022, 10, 50692–50701. [Google Scholar] [CrossRef]
  51. Van Thanh, N.; Lan, N.P.H. A New Hybrid Triple Bottom Line Metrics and Fuzzy MCDM Model: Sustainable Supplier Selection in the Food-Processing Industry. Axioms 2022, 11, 57. [Google Scholar] [CrossRef]
  52. Balaei, B.; Wilkinson, S.; Potangaroa, R.; Hassani, N.; Alavi-Shoshtari, M. Developing a Framework for Measuring Water Supply Resilience. Nat. Hazards Rev. 2018, 19, 04018013. [Google Scholar] [CrossRef]
  53. Bhat, S.; Antony, J.; Gijo, E.; Cudney, E.A. Lean Six Sigma for the healthcare sector: A multiple case study analysis from the Indian context. Int. J. Qual. Reliab. Manag. 2019, 37, 90–111. [Google Scholar] [CrossRef]
  54. Yontar, E. Assessment of the logistics activities with a structural model on the basis of improvement of sustainability performance. Environ. Sci. Pollut. Res. 2022, 29, 68904–68922. [Google Scholar] [CrossRef]
  55. Fritz, M.M.; Rauter, R.; Baumgartner, R.J.; Dentchev, N. A supply chain perspective of stakeholder identification as a tool for responsible policy and decision-making. Environ. Sci. Policy 2018, 81, 63–76. [Google Scholar] [CrossRef]
  56. Reed, M.; Vella, S.; Challies, E.; De Vente, J.; Frewer, L.; Hohenwallner-Ries, D.; Huber, T.B.; Neumann, R.K.; Oughton, E.; Del Ceno, J.S.; et al. A theory of participation: What makes stakeholder and public engagement in environmental management work? Restor. Ecol. 2018, 26, S7–S17. [Google Scholar] [CrossRef]
  57. Sharpe, L.M.; Harwell, M.C.; Jackson, C.A. Integrated stakeholder prioritization criteria for environmental management. J. Environ. Manag. 2021, 282, 111719. [Google Scholar] [CrossRef]
  58. Chai, N.; Zhou, W. A novel hybrid MCDM approach for selecting sustainable alternative aviation fuels in supply chain management. Fuel 2022, 327, 125180. [Google Scholar] [CrossRef]
  59. Chung, H.; Chang, K. A Novel General Data Envelopment Analysis Based Approach for MCDM Issues of Hydrogen Energy under a Fuzzy Environment. Systems 2022, 10, 176. [Google Scholar] [CrossRef]
  60. Le, M.D.; Nhieu, N. An Offshore Wind–Wave Energy Station Location Analysis by a Novel Behavioral Dual-Side Spherical Fuzzy Approach: The Case Study of Vietnam. Appl. Sci. 2022, 12, 5201. [Google Scholar] [CrossRef]
  61. Wang, C.; Dang, T.; Nguyen, N.; Chou, C.; Hsu, H.; Dang, L. Evaluating Global Container Shipping Companies: A Novel Approach to Investigating Both Qualitative and Quantitative Criteria for Sustainable Development. Axioms 2022, 11, 610. [Google Scholar] [CrossRef]
  62. Wang, X.; Zhang, C.; Deng, J.; Su, C.; Gao, Z. Analysis of Factors Influencing Miners’ Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model. Int. J. Environ. Res. Public Health 2022, 19, 7368. [Google Scholar] [CrossRef]
  63. Štilić, A.; Njeguš, A. Primena metoda višekriterijumske analize u odabiru kandidata za rad u turističkoj privredi. In Sinteza 2019—International Scientific Conference on Information Technology and Data Related Research; Singidunum University: Belgrad, Serbia, 2019. [Google Scholar] [CrossRef]
  64. Tuljak-Suban, D.; Bajec, P. Integration of AHP and GTMA to Make a Reliable Decision in Complex Decision-Making Problems: Application of the Logistics Provider Selection Problem as a Case Study. Symmetry 2020, 12, 766. [Google Scholar] [CrossRef]
  65. Hwang, C.; Yoon, K. Multiple Attribute Decision Making: Methods and Applications a State-of-the-Art Survey; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012. [Google Scholar]
  66. Garg, H. Linguistic Pythagorean fuzzy sets and its applications in multiattribute decision-making process. Int. J. Intell. Syst. 2018, 33, 1234–1263. [Google Scholar] [CrossRef]
  67. Tavana, M.; Hajipour, V. A practical review and taxonomy of fuzzy expert systems: Methods and applications. Benchmarking Int. J. 2019, 27, 81–136. [Google Scholar] [CrossRef]
  68. Saaty, T.L. Decision Making with Dependence and Feedback: The Analytic Network Process: The Organization and Prioritization of Complexity; Rws Publications: Pittsburgh, PA, USA, 1996. [Google Scholar]
  69. Jayawardena, T.S.S.; Jayawardena, C.S. Analytical Network Process in Decision Making. In Advances in Knowledge Acquisition, Transfer and Management Book Series; Springer Nature: Berlin/Heidelberg, Germany, 2023; pp. 180–196. [Google Scholar] [CrossRef]
  70. Gabus, A.; Fontela, E. World Problems an Invitation to Further Thought within the Framework of DEMATEL; Battelle Geneva Research Centre: Geneva, Switzerland, 1972. [Google Scholar]
  71. Sang, X.; Yu, X.; Chang, C.; Liu, X. Electric bus charging station site selection based on the combined DEMATEL and PROMETHEE-PT framework. Comput. Ind. Eng. 2022, 168, 108116. [Google Scholar] [CrossRef]
  72. Rezaei, J. Best-worst multi-criteria decision-making method. Omega 2015, 53, 49–57. [Google Scholar] [CrossRef]
  73. Mohammadi, M.; Rezaei, J. Bayesian best-worst method: A probabilistic group decision making model. Omega 2020, 96, 102075. [Google Scholar] [CrossRef]
  74. Duckstein, L.; Opricovic, S. Multiobjective optimization in river basin development. Water Resour. Res. 1980, 16, 14–20. [Google Scholar] [CrossRef]
  75. Deng, J. Introduction to Grey system theory. J. Grey Syst. 1989, 1, 1–24. [Google Scholar]
  76. Brito-Parada, P.R. A multiple criteria decision making method to weight the sustainability criteria of renewable energy technologies under uncertainty. Renew. Sustain. Energy Rev. 2020, 127, 109891. [Google Scholar] [CrossRef]
  77. Ghaleb, A.M.; Kaid, H.; Al-Samhan, A.M.; Mian, S.H.; Hidri, L. Assessment and Comparison of Various MCDM Approaches in the Selection of Manufacturing Process. Adv. Mater. Sci. Eng. 2020, 2020, 4039253. [Google Scholar] [CrossRef]
  78. Shaikh, S.A.; Memon, M.A.; Prokop, M.; Kim, K. An AHP/TOPSIS-Based Approach for an Optimal Site Selection of a Commercial Opening Utilizing GeoSpatial Data. In Proceedings of the International Conference on Big Data and Smart Computing, Busan, Republic of Korea, 19–22 February 2020. [Google Scholar] [CrossRef]
  79. Gül, S.; Aydoğdu, A. Novel distance and entropy definitions for linear Diophantine fuzzy sets and an extension of TOPSIS (LDF-TOPSIS). Expert Syst. 2022, 40, e13104. [Google Scholar] [CrossRef]
  80. Kokaraki, N.; Hopfe, C.J.; Robinson, E.P.; Nikolaidou, E. Testing the reliability of deterministic multi-criteria decision-making methods using building performance simulation. Renew. Sustain. Energy Rev. 2019, 112, 991–1007. [Google Scholar] [CrossRef]
  81. Salim, F.S.; Bakar, Z.A.; Noor, N.M.M.; Mohemad, R.; Sabri, I.A.A. Aesthetic user interfaces ranking using fuzzy analytic hierarchy process (FAHP) approach. In AIP Conference Proceedings; American Institute of Physics: College Park, MD, USA, 2023. [Google Scholar] [CrossRef]
  82. Asakereh, A.; Soleymani, M.; Ardebili, S.M.S. Multi-criteria evaluation of renewable energy technologies for electricity generation: A case study in Khuzestan province, Iran. Sustain. Energy Technol. Assess. 2022, 52, 102220. [Google Scholar] [CrossRef]
  83. Bilgili, F.; Zarali, F.; Ilgün, M.F.; Dumrul, C.; Dumrul, Y. The evaluation of renewable energy alternatives for sustainable development in Turkey using intuitionistic fuzzy-TOPSIS method. Renew. Energy 2022, 189, 1443–1458. [Google Scholar] [CrossRef]
  84. Yazdani, H.; Baneshi, M.; Yaghoubi, M. Techno-economic and environmental design of hybrid energy systems using multi-objective optimization and multi-criteria decision making methods. Energy Convers. Manag. 2023, 282, 116873. [Google Scholar] [CrossRef]
  85. Alhakami, W. Computational Study of Security Risk Evaluation in Energy Management and Control Systems Based on a Fuzzy MCDM Method. Processes 2023, 11, 1366. [Google Scholar] [CrossRef]
  86. Favi, C.; Marconi, M.; Mandolini, M.; Germani, M. Sustainable life cycle and energy management of discrete manufacturing plants in the industry 4.0 framework. Appl. Energy 2022, 312, 118671. [Google Scholar] [CrossRef]
  87. Ghosh, S.; Mandal, M.C.; Ray, A. Strategic sourcing model for green supply chain management: An insight into automobile manufacturing units in India. Benchmarking Int. J. 2021, 29, 3097–3132. [Google Scholar] [CrossRef]
  88. Saeidi, P.; Mardani, A.; Mishra, A.R.; Cajas, V.E.C.; Carvajal, M.G. Evaluate sustainable human resource management in the manufacturing companies using an extended Pythagorean fuzzy SWARA-TOPSIS method. J. Clean. Prod. 2022, 370, 133380. [Google Scholar] [CrossRef]
  89. Batwara, A.; Sharma, V.; Makkar, M.; Giallanza, A. An Empirical Investigation of Green Product Design and Development Strategies for Eco Industries Using Kano Model and Fuzzy AHP. Sustainability 2022, 14, 8735. [Google Scholar] [CrossRef]
  90. Feng, C.; Huang, Y.; Chen, X. Sustainable Design for Transforming Sustainability Requirements to Design Parameters Based on Multi-criteria Decision-Making Methodology. In Mechanisms and Machine Science; Springer Nature: Dordrecht, The Netherlands, 2022; pp. 933–959. [Google Scholar] [CrossRef]
  91. Hameed, A.; Sultan, M.T.H.; Raj, S.A.; Baghdadi, M.A.; Shahzad, M. Sustainable Product Development Using FMEA ECQFD TRIZ and Fuzzy TOPSIS. Sustainability 2022, 14, 14345. [Google Scholar] [CrossRef]
  92. Keshavarz-Ghorabaee, M. Sustainable Supplier Selection and Order Allocation Using an Integrated ROG-Based Type-2 Fuzzy Decision-Making Approach. Mathematics 2023, 11, 2014. [Google Scholar] [CrossRef]
  93. Oladunni, O.J.; Mpofu, K.; Olanrewaju, O.A. Greenhouse gas emissions and its driving forces in the transport sector of South Africa. Energy Rep. 2022, 8, 2052–2061. [Google Scholar] [CrossRef]
  94. Kokkinos, K.; Nathanail, E.; Gerogiannis, V.C.; Moustakas, K.; Karayannis, V. Hydrogen storage station location selection in sustainable freight transportation via intuitionistic hesitant decision support system. Energy 2022, 260, 125008. [Google Scholar] [CrossRef]
  95. Saraji, M.K.; Streimikiene, D.; Čiegis, R. A novel Pythagorean fuzzy-SWARA-TOPSIS framework for evaluating the EU progress towards sustainable energy development. Environ. Monit. Assess. 2021, 194, 42. [Google Scholar] [CrossRef]
  96. Wei, Q.; Zhou, C. A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China. Environ. Sci. Pollut. Res. 2022, 30, 10540–10559. [Google Scholar] [CrossRef]
  97. Peng, X.; Huang, H.; Luo, Z. Fuzzy dynamic MCDM method based on PRSRV for financial risk evaluation of new energy vehicle industry. Appl. Soft Comput. 2023, 136, 110115. [Google Scholar] [CrossRef]
  98. Goyal, S.; Agarwal, S.; Singh, N.; Mathur, T.; Mathur, N. Analysis of Hybrid MCDM Methods for the Performance Assessment and Ranking Public Transport Sector: A Case Study. Sustainability 2022, 14, 15110. [Google Scholar] [CrossRef]
  99. Liu, Z.; Zhang, Y. Comprehensive Sustainable Assessment and Prioritization of Different Railway Projects Based on a Hybrid MCDM Model. Sustainability 2022, 14, 12065. [Google Scholar] [CrossRef]
  100. Markatos, D.N.; Pantelakis, S.G. Implementation of a Holistic MCDM-Based Approach to Assess and Compare Aircraft, under the Prism of Sustainable Aviation. Aerospace 2023, 10, 240. [Google Scholar] [CrossRef]
  101. Chen, F.; Li, Y.; Feng, Q.; Dong, Z.; Qian, Y.; Yan, Y.; Ho, M.S.; Ma, Q.; Zhang, D.; Jin, Y. Road safety performance rating through PSI-PRIDIT: A planning tool for designing policies and identifying best practices for EAS countries. Socio-Econ. Plan. Sci. 2022, 85, 101438. [Google Scholar] [CrossRef]
  102. Khan, S.; Kabir, G.; Billah, M.; Dutta, S. An integrated framework for bridge infrastructure resilience analysis against seismic hazard. Sustain. Resilient Infrastruct. 2022, 8 (Suppl. 1), 5–25. [Google Scholar] [CrossRef]
  103. Badi, I.; Alosta, A.; Elmansouri, O.; Abdulshahed, A.M.; Elsharief, S. An application of a novel grey-CODAS method to the selection of hub airport in North Africa. Decis. Mak. 2023, 6, 18–33. [Google Scholar] [CrossRef]
  104. Dang, T.; Nguyen, N.; Nguyen, V.T.; Dang, L. A Two-Stage Multi-Criteria Supplier Selection Model for Sustainable Automotive Supply Chain under Uncertainty. Axioms 2022, 11, 228. [Google Scholar] [CrossRef]
  105. Awadh, M.A. Assessing the Quality of Sustainable Airline Services Utilizing the Multicriteria Decision-Making Approach. Sustainability 2023, 15, 7044. [Google Scholar] [CrossRef]
  106. Chaisar, M.; Garg, S.K. Selection of Sewage Treatment Technology using Analytic Hierarchy Process. Mater. Today Proc. 2021, 56, 3433–3440. [Google Scholar] [CrossRef]
  107. Demircan, B.G.; Yetilmezsoy, K. A Hybrid Fuzzy AHP-TOPSIS Approach for Implementation of Smart Sustainable Waste Management Strategies. Sustainability 2023, 15, 6526. [Google Scholar] [CrossRef]
  108. Dewalkar, S.V.; Shastri, S.S. Integrated Life Cycle Assessment and Life Cycle Cost Assessment based fuzzy multi-criteria decision-making approach for selection of appropriate wastewater treatment system. J. Water Process Eng. 2022, 45, 102476. [Google Scholar] [CrossRef]
  109. Garcia-Garcia, G. Using Multi-Criteria Decision-Making to optimise solid waste management. Curr. Opin. Green Sustain. Chem. 2022, 37, 100650. [Google Scholar] [CrossRef]
  110. Kabirifar, K.; Ashour, M.; Yazdani, M.; Mahdiyar, A.; Malekjafarian, M. Cybernetic-parsimonious MCDM modeling with application to the adoption of Circular Economy in waste management. Appl. Soft Comput. 2023, 139, 110186. [Google Scholar] [CrossRef]
  111. Van Thanh, N. Optimal Waste-to-Energy Strategy Assisted by Fuzzy MCDM Model for Sustainable Solid Waste Management. Sustainability 2022, 14, 6565. [Google Scholar] [CrossRef]
  112. Yang, J.; Qiao, L.; Li, C. Fuzzy Comprehensive Evaluation Method for Geological Environment Quality of Typical Heavy Metal Mines. Pol. J. Environ. Stud. 2023, 32, 1877–1886. [Google Scholar] [CrossRef]
  113. Kum, G.; Sönmez, M.; Kargın, A. An Alternative Process for Determining Erosion Risk: The Fuzzy Method. Coğrafya Derg. 2022, 44, 219–229. [Google Scholar] [CrossRef]
  114. Baumann, M.; Weil, M.; Peters, J.F.; Chibeles-Martins, N.; Moniz, A.B. A review of multi-criteria decision making approaches for evaluating energy storage systems for grid applications. Renew. Sustain. Energy Rev. 2019, 107, 516–534. [Google Scholar] [CrossRef]
  115. Hariri, R.H.; Fredericks, E.M.; Bowers, K.M. Uncertainty in big data analytics: Survey, opportunities, and challenges. J. Big Data 2019, 6, 44. [Google Scholar] [CrossRef]
  116. Willard, J.; Jia, X.; Xu, S.; Steinbach, M.; Kumar, V. Integrating scientific knowledge with machine learning for engineering and environmental systems. ACM Comput. Surv. 2022, 55, 1–37. [Google Scholar] [CrossRef]
  117. Freudenreich, B.; Lüdeke-Freund, F.; Schaltegger, S. A stakeholder theory perspective on business models: Value creation for sustainability. J. Bus. Ethics 2020, 166, 3–18. [Google Scholar] [CrossRef]
  118. Bag, S.; Gupta, S.; Kumar, A.; Sivarajah, U. An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Ind. Mark. Manag. 2021, 92, 178–189. [Google Scholar] [CrossRef]
Eng 04 00088 g001 550
Figure 1. Publishers of articles pertaining to the use of MCDM methods in sustainable engineering.
Figure 1. Publishers of articles pertaining to the use of MCDM methods in sustainable engineering.
Eng 04 00088 g001
Eng 04 00088 g002 550
Figure 2. Publications of articles pertaining to the the use of MCDM methods in sustainable engineering.
Figure 2. Publications of articles pertaining to the the use of MCDM methods in sustainable engineering.
Eng 04 00088 g002
Eng 04 00088 g003 550
Figure 3. Subjects of articles pertaining to the use of MCDM methods in sustainable engineering.
Figure 3. Subjects of articles pertaining to the use of MCDM methods in sustainable engineering.
Eng 04 00088 g003
Eng 04 00088 g004 550
Figure 4. The most commonly used methods in the sustainable engineering articles.
Figure 4. The most commonly used methods in the sustainable engineering articles.
Eng 04 00088 g004
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Štilić, A.; Puška, A. Integrating Multi-Criteria Decision-Making Methods with Sustainable Engineering: A Comprehensive Review of Current Practices. Eng 2023, 4, 1536-1549. https://doi.org/10.3390/eng4020088

AMA Style

Štilić A, Puška A. Integrating Multi-Criteria Decision-Making Methods with Sustainable Engineering: A Comprehensive Review of Current Practices. Eng. 2023; 4(2):1536-1549. https://doi.org/10.3390/eng4020088

Chicago/Turabian Style

Štilić, Anđelka, and Adis Puška. 2023. "Integrating Multi-Criteria Decision-Making Methods with Sustainable Engineering: A Comprehensive Review of Current Practices" Eng 4, no. 2: 1536-1549. https://doi.org/10.3390/eng4020088

APA Style

Štilić, A., & Puška, A. (2023). Integrating Multi-Criteria Decision-Making Methods with Sustainable Engineering: A Comprehensive Review of Current Practices. Eng, 4(2), 1536-1549. https://doi.org/10.3390/eng4020088

Article Metrics

Back to TopTop