Recent advances in multi-criteria decision analysis: A comprehensive review of applications and trends

2.1MCDA applications in healthcare

Healthcare is at the forefront of societal priorities, and the continued development of technology in this field is of paramount importance. Advances in healthcare technology not only improve patient care and outcomes but also streamline processes, improve diagnostic accuracy, and contribute to the overall efficiency of healthcare systems. From precision medicine to telehealth solutions, technology is crucial in revolutionizing healthcare delivery and access. Integrating cutting-edge technologies such as artificial intelligence, remote monitoring devices, and electronic medical records enables healthcare professionals to make more informed decisions, tailor treatment to individual needs, and provide personalized care. The continuous evolution of healthcare technology is raising the standard of patient care and fostering a proactive and preventative approach to well-being, marking an era of transformation at the interface of healthcare and technology.

Multi-Criteria Decision Analysis has emerged as a powerful tool in healthcare, facilitating complex decision-making processes by considering multiple criteria and variables. In healthcare, decisions often involve many factors, including patient outcomes, cost-effectiveness, and ethical considerations. MCDA methods provide a structured framework to evaluate and prioritize these diverse criteria systematically, aiding healthcare professionals and policymakers in making well-informed choices. Multi-Criteria Decision Analysis contributes to evidence-based decision-making, fostering a more comprehensive and transparent approach, whether in resource allocation, treatment selection, or healthcare policy formulation. Its application in healthcare enhances decision quality and aligns with the increasing emphasis on patient-centered care and the optimization of limited healthcare resources. As healthcare evolves, MCDA is a valuable tool for navigating the complexities inherent in healthcare decision scenarios, ultimately contributing to improved patient care and healthcare system efficiency.

One of the popular topics around the healthcare area at the latest time was COVID-19. In the literature, it could be seen that numerous research papers were directed to this problem. Alemdar et al. focused on the accessibility of vaccination centers in COVID-19 outbreak control [14]. The Analytical Hierarchy Process (AHP) method was used to determine criteria weights based on the experience of the advisory committee. Sarwar et al. employed the MCDA to measure vaccination willingness in response to COVID-19 with the Analytical Hierarchy Process method [15]. De Nardo et al. covered the problem of prioritizing hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage [16]. The Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method was applied for this purpose. Sarwar and Imran directed their research to prioritize infection prevention and control activities for SARS-CoV-2 with the AHP method [17]. With the outcome of the evaluation, the authors indicated that unnecessary travel, 3Cs (spaces that are closed, crowded, and involve close contact), and touching own body parts should be avoided. Moreover, wearing a mask and proper hand washing were also crucial to reduce the spread of coronavirus. Choudhury et al. examined the preparedness of Indian states against COVID-19 pandemic risk based on the Fuzzy Analytical Hierarchy Process approach [18]. Ruggeri et al. used the MCDA methodology with Monte Carlo simulation for assessing the health innovations applied to the COVID-19 emergency [19]. Yang et al. applied the Analytical Hierarchy Process method with an Intuitionistic Linguistic multi-criteria group decision model to evaluate the health technologies [20]. The results accuracy was validated with the Keeney-Raiffa MCDA (KRM) method. Ortiz-Barrios et al. assessed hospitals’ disaster preparedness with the Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) techniques [21]. The authors indicated that Personnel is the most important factor when evaluating hospital preparedness, while Flexibility has the greatest prominence.

It could be seen that occurring uncertainties in healthcare problems lead to employing fuzzy logic to multi-criteria decision models. Chen et al. focused on the choice of makeshift hospitals with a multi-criteria approach [22]. For this purpose, the authors used the Best-Worst Method (BWM) and Data Envelopment Analysis (DEA) in the Trapezoidal Interval Type-2 Fuzzy (TrIT2F) environment. Yazdani et al. aimed to select the healthcare waste disposal locations [23]. To solve the determined problem, the authors proposed a new BWM with Interval Rough Numbers (IRN) for healthcare waste disposal location decisions and a new IRN Dombi-Bonferroni (IRNDBM) means the operator to process the rough data because of the unavailability of precise information. Ozsahin et al. evaluated the schizophrenia treatment techniques with the Fuzzy Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) method [24]. The obtained results showed that pharmacotherapy is the most preferred technique in assessed cases, while other techniques have different places in the final ranking based on the varied weights. Mishra and Rani addressed the problem of healthcare waste disposal location selection with the Fermatean Fuzzy Weighted Aggregates Sum Product Assessment (WASPAS) method, combining it with score function and entropy measure to indicate criteria weights[25]. Salimian and Mousavi evaluated the technologies for healthcare waste treatment with a group decision-making approach in an Intuitionistic Fuzzy environment [26]. The authors also performed a sensitivity analysis based on changing criteria weights values to indicate the influence of changes in the final rankings. Bharsakade et al. examined lean healthcare management with the Fuzzy Analytical Hierarchy Process method [27].

Another visible direction of developing multi-criteria models involved using different weighting methods and varied MCDA methods in the evaluation process. Németh et al. analyzed the impact of different weighting methods in MCDA models in healthcare with a focus on low and middle-income countries [28]. The results showed that more complex multi-criteria techniques are less eager to bias. Moreover, the authors indicated that the Simple Multi-Attribute Rating Technique (SMART), combined with the SWING-weighting technique and the Analytic Hierarchy Process methods, could be the most feasible approach. Devarakonda et al. performed research toward identifying drivers and triggers of infectious disease outbreaks using ensemble learning [29]. To identify the criteria relevance, the authors used Shannon’s entropy technique to objectively define criteria weights based on the information measures from the decision matrix. Almahdi et al. used MCDA for developing mobile-based patient monitoring systems with the Best-Worst Method to establish criteria weights based on expert knowledge and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to assess the considered systems [30]. Schey et al. focused on assessing the importance of criteria in treatments for rare diseases [31]. Based on the results from the Analytical Hierarchy Process method, the authors indicated that the treatment efficacy was the most important aspect. Beheshtinia et al. addressed the problem of selecting healthcare waste disposal center locations [32]. The authors presented the hybrid MCDA model PROMSIS, a combination of TOPSIS and PROMETHEE methods. Moreover, the AHP method was used to establish criteria weights. Adalı and Tuş examined the potential hospital sites with distance-based MCDA methods [33]. Thus, the authors selected Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Evaluation based on Distance from Average Solution (EDAS), and COmbinative Distance-based ASsessment (CODAS) methods to assess alternatives. The CRiteria Importance Through Intercriteria Correlation (CRITIC) method was applied to determine criteria weights in the problem. Mustapha et al. examined the problem of breast cancer screening using supervised learning and MCDA [34]. For this purpose, the authors used the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) method to evaluate the accuracy of the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression, and Random Forest Classifier, showing that the first one was the best approach. Gardas directed the research to indicate the organizational challenges that influence the adoption of Healthcare 4.0 technologies and model the mutual relationship between them using the Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology [35]. The results from the study show three causal factors, namely “Lack of vision and commitment from the top management”, “Hierarchical structure”, and “Lack of skilled workforce”.

The trends in application decision models in healthcare problems showed that various assessment approaches are utilized to solve the considered healthcare problems. Xiao proposed a novel method for assessing healthcare waste treatment technologies based on D numbers [36]. The results showed that the proposed method can effectively handle the considered selection problem under complex and uncertain environments. Camilo et al. covered the problem of selecting the most appropriate triage system for the emergency care units in natal [37]. For this purpose, the FITradeoff method was used. Montibeller et al. focused on examining the prioritization of health threats with the Multi-Attribute Value analysis [38]. Lasorsa et al. directed their research into evaluating non-clinical hospital services [39]. The Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method was used. Gardas et al. covered the problem of healthcare surgical management [40]. The Total Interpretive Structural Modelling (TISM) methodology was used to identify the relationships among the factors and develop a hierarchical structure. Also, the Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) approach was employed to identify the dominant factors. From the research papers directed to healthcare problems, the five most popular works were highlighted and presented in Table 1.

2.2MCDA applications in energy development

Multi-Criteria Decision Analysis stands out as a crucial tool in energy development due to its ability to handle the complexity inherent in decision-making processes. In renewable energy development, decisions often involve many criteria, objectives, and constraints that require careful consideration. MCDA provides a systematic framework for evaluating and comparing various alternatives based on multiple criteria simultaneously. It is particularly beneficial in the energy field, where decisions impact diverse aspects such as economic feasibility, environmental sustainability, and social acceptance. The MCDA methods allow decision-makers to weigh these decision factors comprehensively, ensuring a more informed and balanced assessment of energy development strategies.

Moreover, the adaptability of MCDA methods makes them particularly useful in addressing the evolving challenges of energy technologies and policy problems. The renewable energy sector is dynamic, with advancements and innovations occurring rapidly. MCDA allows decision-makers to incorporate new criteria, adjust weights, and adapt decision models to changing circumstances. This flexibility is vital to staying updated with technological developments, political changes, and emerging energy trends. By providing a structured and adaptable approach, MCDA empowers decision-makers to navigate the complexities of energy development, make informed choices aligned with sustainability goals, and contribute to a cleaner and more resilient energy future.

The performed review showed that varied approaches to solving determined multi-criteria problems were applied within the analyzed research works. Albawab et al. focused on evaluating the development of sustainability indicators for ranking energy storage technologies [41]. The multi-criteria analysis was based on extended Stepwise Weight Assessment Ratio Analysis (SWARA), and Additive Ratio ASsessment (ARAS) methods. Mazzeo et al. proposed a novel energy-economic-environmental MCDA approach in optimizing a hybrid renewable system [42]. The results from the study emphasized that, for specific loads, PV, wind, and battery powers, the development of specific incentives for wind systems can help to make hybrid systems more economically competitive. Rehman et al. faced the problem of selecting wind energy power plant locations using the PROMETHEE method, which allowed for the identification of the optimal wind energy power plant location selected among the five alternatives [43]. Khorsand and Ramezanpour focused on proposing an energy-efficient task-scheduling algorithm based on an MCDA approach in cloud computing [44]. Using the BWM and TOPSIS methods provided results that indicated that the proposed approach, in comparison with its counterparts, can effectively reduce the makespan and energy consumption. Diemuodeke et al. evaluated the problem of the optimal mapping of hybrid renewable energy systems for locations using the MCDA approach [45]. The HOMER software was used, and the TOPSIS method was applied considering technical, economic, environmental, and sociocultural criteria. Hong, Kim, and Jeong assessed the sustainability of the green hydrogen economy in Korea [46]. Hydrogen, renewables, uranium, coal, and gas were compared and evaluated in six selected dimensions including energy return on energy investment, greenhouse gas emissions, levelized cost of electricity, import dependency, long-term energy storage, and other applications.

Roy compared the performance of novel biomass-based hybrid energy systems such as downdraft type biomass gasifier, solid oxide fuel cell (SOFC), externally fired gas turbine (EFGT), and Stirling engine (SE) [47]. To assess the considered decision variants, the entropy method was used to determine criteria weights, and the VIKOR method was used to calculate the final ranking. Singh et al. addressed the problem of selecting optimal distributed energy resources mix in distribution networks [48]. The authors proposed a novel mix with the TOPSIS method, which was used compared to the proposed and existing methods on the standard 33-bus distribution system. McKenna et al. directed their research toward combining local preferences with the MCDA approach and linear optimization to develop feasible energy concepts in small communities in Germany [49]. The authors used the Multi-Attribute Value Theory (MAVT) method with sensitivity analysis of modeling interval weights to determine the final rankings. Murrant and Radcliffe addressed the problem of assessing energy storage technology options using a framework based on a multi-criteria approach [50]. The Multi-Attribute Value Theory method was applied for this purpose, and the authors emphasized that it can lead to important insights for the development of energy systems.

Many applications utilize fuzzy logic to handle uncertainties occurring in multi-criteria decision problems in energy management. Chatterjee and Kar focused on an MCDA evaluation of renewable energy selection in an uncertain environment with both subjective and objective criteria weights. The authors proposed COPRAS-Z methodology, where Z-number model fuzzy numbers helped with reliability degree representing the imprecise judgment of decision-makers in evaluating the weights of criteria and assessment of renewable energy alternatives [51]. Ke et al. developed a framework for a comprehensive evaluation of plan selection of urban integrated energy systems based on a BWM-CRITIC-VIKOR framework under an intuitionistic fuzzy environment [52]. The authors indicated that the initial investment, the utilization rate of renewable energy, and the comprehensive utilization rate have the most significant influence. Zhou et al. utilized a hybrid fuzzy MCDA approach for performance analysis and evaluation of park-level integrated energy systems [53]. For this purpose, the objective weighting method of entropy was used to calculate criteria weights, and the schemes are evaluated based on the extended TODIM (an acronym in Portuguese for interactive and multi-criteria decision-making).

Ren constructed the MCDA model to prioritize energy systems under uncertainties after life cycle sustainability assessment [54]. The criteria weights were identified with a fuzzy two-stage logarithmic goal programming, and the Interval Grey Relational Analysis (GRA) was used to rank the analyzed alternatives, while the results were validated with the Interval TOPSIS method. Mrówczyńska et al. covered the problem of examining scenarios as a tool supporting decisions in urban energy policy using fuzzy logic, multi-criteria analysis and GIS tools [55]. Different criteria weights scenarios have identified factors affecting the implementation of renewable energy sources evaluations. Zhong et al. analyzed the investment strategies for renewable energies with Interval Type-2 (IT2) Fuzzy Sets, hesitant IT2 Fuzzy DEMATEL, TOPSIS, and VIKOR methods [56]. The authors recommend performing a detailed analysis to identify the risk in renewable energy investment projects that could be done with sensitivity analysis approaches. Noorollahi et al. proposed a framework for GIS-based site selection of photovoltaic solar farms [57]. For this purpose, the Fuzzy-Boolean logic and Analytical Hierarchy Process method were applied, showing that 25.78% of the examined province’s area is best suited for the solar farm. Wang et al. directed their research to renewable energy plants location selection in Vietnam under a fuzzy environment [58]. The uncertainties were modeled with Fuzzy AHP and TOPSIS methods.

On the other hand, it could be seen that applying the Analytical Hierarchy Process method is a popular direction, despite the visible shortcomings of this method. Shaaban et al. directed their research to the sustainability assessment of electricity generation technologies in Egypt with AHP and WSM methods [59]. The authors indicated that the performed study emphasized the importance of a multi-dimensional evaluation of the technologies while considering the stakeholders’ preferences to achieve a reliable and sustainable future energy supply. Mahdy and Bahaj focused on using MCDA to evaluate offshore wind energy potential in Egypt [60]. The authors used the Analytical Hierarchy Process and Pairwise Comparison methods to link the obtained criteria weights to site spatial assessment in a Geographical Information System. Okokpujie et al. covered the problem of selecting suitable material for developing horizontal wind turbine blades [61]. The AHP and TOPSIS methods were used to evaluate considered decision variants. Ruiz et al. focused on the MCDA assessment for selecting the optimal location of solar energy plants in Indonesia [62]. To identify the relevance of considered decision factors, the AHP method was applied. Vinhoza and Schaeffer assessed Brazil’s offshore wind energy potential using a Spatial MCDA approach in which the AHP method was used to identify criteria weights considered in the determined problem [63]. Nzotcha, Kenfack, and Manjia focused on selecting a site for a pumped hydro-energy storage plant using the AHP method for weights identification and the ELimination and Choice Expressing REality (ELECTRE) II method used for the evaluation purposes [64].

Selected applications contained complex decision models determined with many multi-criteria decision methods. Ullah et al. used the MCDA approach for optimal planning of on/off grid hybrid solar, wind, hydro, and biomass clean electricity supply [65]. Fuzzy AHP, TOPSIS, Evaluation based on Distance from Average Solution (EDAS), and Multi-Objective Optimization Method by Ratio Analysis (MOORA) methods were used to determine the decision model. Criteria such as cost, reliability, emission, social, and topographical were considered simultaneously. Saraswat and Digalwar evaluated energy alternatives of the energy sector in India by integrating Shannon’s entropy and Fuzzy AHP with six Fuzzy MCDA methods, namely TOPSIS, VIKOR, PROMETHEE II, WSM, WPM, and WASPAS [66]. Ehteram, Karami, and Farzin faced the challenge of reservoir optimization for energy production with a new evolutionary algorithm based on the MCDA model [67]. All examined evolutionary algorithms were evaluated by Complex Proportional Assessment (COPRAS), TOPSIS, modified TOPSIS, Weighted Aggregated Sum Product Assessment (WASPAS), and BORDA. The presented research works indicated that a variety of problems connected to energy management and development are currently addressed in the literature. Moreover, Table 2 presents the most popular works from this field, showing the number of citations, used methods, solved problems, and the reference to the paper.

2.3MCDA applications in supplier selection

Multi-Criteria Decision Analysis serves as a valuable and robust methodology in the supplier selection area. The process of choosing suppliers involves considering a multitude of criteria, ranging from cost and quality to reliability and environmental sustainability. MCDA allows for simultaneously evaluating and comparing suppliers based on these diverse criteria. This comprehensive approach ensures that decision-makers can make well-informed choices that align with the organization’s strategic goals. In supplier selection, where decisions have far-reaching implications on a business’s efficiency and competitiveness, MCDA helps prioritize criteria based on their relative importance, thereby facilitating a more rational and objective decision-making process.

The flexibility of MCDA methods is particularly advantageous in supplier selection, where market conditions, supplier capabilities, and organizational priorities can change over time. The adaptability of MCDA allows decision-makers to adjust decision models to reflect the evolving dynamics of the business environment. It is crucial for maintaining the relevance and effectiveness of supplier selection processes in the face of changing market conditions and organizational requirements. By providing a structured and dynamic approach, MCDA empowers organizations to navigate the complexities of supplier selection, optimize decision outcomes, and enhance overall supply chain performance.

Since supplier selection problems could be considered in many industries, research works directed to this field addressed varied approaches for selecting the most rational decision variant among the considered alternatives. Xue et al. used a multi-stage MCDA approach for evaluating the supplier performance of high-speed trains [68]. The authors included six criteria in the problem, namely physical quality, delivery performance, which belongs to quantitative criteria, and service, price, quality management system, and environmental safety, which belong to qualitative criteria. Mishra et al. addressed the problem of medical equipment supplier selection with dual probabilistic linguistic Full Consistency ARAS model [69]. Chakraborty et al. combined D numbers with Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) method to select the best-performing supplier in a leading Indian iron and steel-making industry based on seven selective evaluation criteria and opinions of three decision-makers [70]. Liu, Deng, and Chan used the ANP and entropy method to determine criteria weights, and then the DEMATEL method combined with Game Theory was applied for evidential supplier selection [71]. Kaya and Yet determined Bayesian networks based on the DEMATEL method for solving the problem of supplier selection, and knowledge elicited from multiple experts was used to build the decision model [72]. Chen proposed a hybrid MCDA model approach based on ANP-Entropy TOPSIS for building materials supplier selection [73]. Zakeri et al. directed their research toward supplier selection and proposed a new weighting method called the Win, Loss, Draw (WLD) method [74]. As the practical problem, a real case study of domestic cheese brands was considered, and the proposed methodology was used to select the best cheese supplier for an Iranian hypermarket.

Many works included handling uncertainties in the defined problems by introducing fuzzy logic and combining it with multi-criteria calculations. Güneri and Deveci covered the problem of assessment of supplier selection in the defense industry using q-rung orthopair fuzzy set based EDAS approach [75]. alić et al. proposed a novel integrated Fuzzy PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Interval Rough Simple Additive Weighting (SAW) methods to assess green supplier [76]. The authors considered seven criteria and four alternatives in the problem space. Wu, Lin, and Barnes focused on sustainable supplier selection in the chemical industry with an integrated decision-making approach using Fuzzy Grey Relational Analysis (FGRA), Failure Mode and Effects Analysis (FMEA), and cloud computing-entropy weight method (EWM) to analyze the economic, social, and environmental dimensions [77]. Moreover, the authors evaluated considered alternatives using the Decision-making Trial and Evaluation Laboratory. Hendiani et al. applied Interval Type-2 Fuzzy preference relations to propose a novel MCDA model for sustainable supplier selection problems in which the weights of criteria and performance ratings are expressed as Interval Type-2 Trapezoidal Fuzzy Sets [78]. Gupta, Soni, and Kumar focused on the problem of supplier selection in the automotive industry with an MCDA approach under a fuzzy environment and used Fuzzy AHP for criteria weight calculation and three popular multi-criteria techniques, namely TOPSIS, MABAC, and WASPAS [79].

Furthermore, Memari et al. used the Intuitionistic Fuzzy TOPSIS method to evaluate the automotive spare parts manufacturer problem that concerns nine criteria and thirty sub-criteria [80]. Liu et al. directed their research toward an extended multi-criteria group decision-making method applied in emergency medical supplier selection [81]. The authors used an extended IT-2FSs assessment method and a novel ISM-BWM-Cosine Similarity-Max Deviation Method (IBCSMDM) to determine criteria weights and the TODIM method to evaluate the decision variants. Hendiani, Mahmoudi, and Liao adopted the Fuzzy Best-Worst method to obtain corresponding weights, while the supplier selection problem was directed at finding the most sustainable supplier for procuring Tubes for Shell & Tube Heat Exchanger equipment in the south of Iran [82]. Wei and Zhou proposed an MCDA framework for electric vehicle supplier selection of government agencies and public bodies in China, where BWM and fuzzy VIKOR method were applied [83]. Yildizbasi and Arioz integrated big data analytics and a hybrid fuzzy MCDA with fuzzy AHP-TOPSIS methods to evaluate green supplier selection considering selected aspects of sustainable development [84]. Ecer used the Interval Type-2 Fuzzy AHP method to examine home appliance manufacturers and applied sensitivity analysis to verify the consistency and stability of the proposed model [85].

Some approaches considered employing multiple MCDA methods to solve supplier selection problems, examining the robustness of the obtained results. Stojić et al. propose a novel rough WASPAS approach for supplier selection in a company manufacturing PVC carpentry products [86]. For this purpose, the AHP, Simple Additive Weighting (SAW), rough EDAS, rough MultiAttributive Border Approximation area Comparison (MABAC), rough VIKOR, rough Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA), and rough Multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA), while the obtained results were compared with Spearman correlation coefficient. Badi and Pamucar addressed the problem of supplier selection for steelmaking companies by using combined Grey-MARCOS methods and compared the obtained results with CODAS, TOPSIS, and VIKOR methods [87]. Petrović et al. used three Fuzzy MCDA methods to evaluate suppliers of procurement of THK Linear motion guide components in the “Lagerton” company in Serbia, namely Fuzzy TOPSIS, WASPAS, and ARAS [88]. Moreover, criteria weights were determined by the Fuzzy SWARA method. Stević et al. developed a novel MCDA model with the FUCOM method and the Interval Rough SAW method to solve the supplier selection problem regarding the sustainable assumptions [89]. Moreover, the authors performed a sensitivity analysis with changing criteria weights and modeling the dynamic decision matrices. Leong, Wong, and Wong directed their research toward determining the robustness of the supplier selection evaluation with GRA-BWM-TOPSIS methods under seven criteria, namely quality, lead time, cost, flexibility, visibility, responsiveness, and financial stability, and five domain experts were engaged in the evaluation process [90]. Phochanikorn and Tan developed a new extension to determine the MCDA model for sustainable supplier selection under an Intuitionistic Fuzzy Environment [91]. The authors used ANP, DEMATEL, and VIKOR methods to assess the area of the palm oil industry. Fei, Deng, and Hu covered the problem of supplier selection problem by proposing a combination of Dempster-Shafer evidence theory with the VIKOR method [92]. Within the evaluation, domain experts were engaged to determine the relevance of the given criteria: product quality, difficulty in establishing cooperation, service performance, risk factor, and price/cost. The presented research approaches showed that many uncertainties could occur in evaluating suppliers, and the fuzzy sets could be effectively used to model the decision process more reliably. Moreover, applying multiple MCDA techniques allowed for verifying results robustness. The most popular research works directed toward supplier selection field included in the review were presented in Table 3.

2.4MCDA applications in transport

The Multi-Criteria Decision Analysis methods have extensive application in the transport field due to the apparent complexity and many factors involved in decision-making processes. Transportation systems are multifaceted, involving cost, environmental impact, safety, and efficiency. MCDA provides a structured and systematic approach to evaluating and prioritizing transportation alternatives based on diverse criteria. In the transport field, where decisions influence urban planning, environmental sustainability, and public prosperity, MCDA enables decision-makers to assess the trade-offs and make informed choices comprehensively.

The dynamic nature of the transport industry, with evolving technologies, changing societal needs, and environmental concerns, necessitates a flexible decision-making framework. MCDA allows for incorporating evolving criteria and changing priorities, ensuring that decision models remain relevant over time. Whether selecting the optimal public transportation system, evaluating infrastructure projects, or making policy decisions, Multi-Criteria Decision Analysis methods assist in considering the multifaceted impacts of each alternative. By accounting for diverse criteria simultaneously, MCDA facilitates a holistic understanding of the consequences of transport decisions, contributing to the development of sustainable and effective transportation systems.

In the performed review, the authors utilized various MCDA techniques to evaluate multi-criteria problems within the transport area. Kalifa et al. applied MCDA analysis to prioritize the public transport system in Sub-Saharan Africa [93]. For this purpose, the ANP and ELECTRE III methods were used, combined with the sensitivity analysis of modifying criteria weights. The results from the research showed that the obtained rankings are robust to modeled scenarios. Ki̇raci and Bakir applied the TOPSIS method to indicate the most suitable decision variant among the four aircraft types, which are the most demanded by airline companies [94]. The selection was made regarding different flight networks and different flight destinations. Ammenberg and Dahlgren compared different bus technologies as part of the sustainable assessment of public transport with 12 criteria grouped into four main areas of evaluation [95]. Aspen and Sparrevik combined Stochastic Multicriteria Acceptability Analysis (SMAA) with the TOPSIS method to evaluate usage of biofuels, natural gas, and electricity on Norwegian ferry crossings [96]. The obtained results showed that all-electric propulsion is preferable, and plug-in hybrid solutions with natural gas also gave a robust performance. Manzolli, Trovão, and Antunes directed their research toward evaluating rapid transit systems implementation in an urban context with PROMETHEE method [97]. The authors modeled four assessment scenarios, namely baseline, environmental, economic, and economic-environmental. Ali et al. directed their research toward direct reductions in carbon emission levels in the Lahore Metropolitan Area (LMA) of Pakistan with the MCDA approach combined with the Bilan Carbone model [98]. Three assessment scenarios were included, namely the current situation and two variants of the future (usual and low carbon scenarios). Hasan, Chapman, and Frame focused on indicating the acceptability of transport emissions reduction policies in New Zealand by 2030 [99]. The analysis was performed with the Simple Additive Weighting method, and the obtained results were then verified with a sensitivity analysis approach concerning generated weights scenarios.

Furthermore, many developed multi-criteria decision models were determined based on applying the AHP method. Singh, Singh, and Sandhu addressed the modern problem of green scooter selection with the MCDA approach, for which the authors combined the AHP and TOPSIS methods [100]. Güner combined the AHP and TOPSIS methods to measure the service quality and rank the bus transit routes, showing the opportunity to monitor and improve the quality of bus transit service [101]. Liaqat et al. directed their research toward evaluating portable energy storage technologies for electric vehicles with the AHP method [102]. Lee focused on prioritizing advanced public transport modes considering urban types in Korea, for which the AHP method was used [103]. Jahanshahi et al. covered the problem of evaluation and relocating bicycle sharing stations in Mashhad city [104]. For this purpose, the authors identified seven decision criteria, which were evaluated by the AHP method, and the obtained weights were used to rank alternatives with the VIKOR method. Petrović and Kankaraš determined the decision model based on the DEMATEL-AHP methods whose purpose was to evaluate criteria for selecting an aircraft for the protection of air traffic [105]. The study included gathering assessments from 45 respondents. Hamurcu and Eren assessed electric buses regarding green transportation conditions with the AHP-TOPSIS method [106]. Six potential electric buses were evaluated under seven criteria, and the criteria weights were modeled with sensitivity analysis to examine the results stability. Nalmapantis et al. faced the challenge of evaluating innovative ideas for public transport proposed by citizens [107]. The AHP method was used to evaluate the importance of criteria weights, with 97 completed questionnaires.

On the other hand, while uncertainties were found in the defined problem, researchers applied various extensions of fuzzy logic to handle the decision-making calculations. Deveci et al. developed a Fuzzy Einstein-based Decision Support System for public transportation management during the COVID-19 pandemic [108]. The dimension of the considered problem was defined as four public transportation management alternatives and 13 criteria, grouped into four main aspects. The logarithmic additive function was used to determine criteria weights, and the Combined Compromise Solution (CoCoSo) method combined with the Einstein T-norm and T-conorm was used to evaluate alternatives. Stević, Subotić, and Softić used Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis to determine criteria weights and EDAS method to evaluate the alternatives within the problem of safety road sections [109]. Sałabun, Palczewski, and Wątróbski focused on assessing the transport evaluation with MCDA approach under incomplete knowledge [110]. The authors evaluated electric bikes with the Characteristic Objects Method (COMET), which uses Triangular Fuzzy Numbers (TFN) within the calculations. Tian et al. proposed a Decision Support System (DSS) for electric vehicle selection [111]. The DSS was developed based on indicating criteria weights by transforming the sentiment analysis results of online auto reviews into Hesitant Intuitionistic Fuzzy Elements, while the evaluation was done by the Organısation, rangement et Synthésse de données relarionnelles (ORESTE) method with Hesitant Intuitionistic Fuzzy Chebyshev distance. Ziemba proposed an approach that combines different types of uncertainty in decision-making by developing a New Easy Approach To Fuzzy PROMETHEE (NEAT-PROMETHEE) method, combined with the Monte Carlo method and elements of the Stochastic Multicriteria Acceptability Analysis method to assess electric vehicles [112]. Alkharabsheh et al. integrated the MCDA approach with Grey Theory to evaluate urban public transportation systems with the Grey-AHP method in the real-world problem using the public transport system’s supply quality in Amman, Jordan [113]. Feng et al. proposed a novel MCDA method for selecting the site of electric vehicle charging stations under sustainable perspectives. [114]. The model was built based on the Linguistic Entropy Weight (LEW) method and Fuzzy Axiomatic Design (FAD). Ali et al. developed a new hybrid MCDA method for car selection based on the Full Consistency Fuzzy TOPSIS (FCF-TOPSIS) approach, with the obtained results indicating a more accurate outcome than in the case of the AHP-TOPSIS application [115].

Moreover, selected evaluation approaches considered using multiple MCDA methods within the same problem to compare the obtained results and indicate their robustness. Sarraf and McGuire focused on the problem of safe route planner [116]. For this purpose, the AHP, Fuzzy AHP, TOPSIS, Fuzzy TOPSIS, and PROMETHEE methods were applied. Broniewicz and Ogrodnik applied the AHP, Fuzzy AHP, TOPSIS, and PROMETHEE methods to solve the problem of evaluating transport infrastructure projects [117]. These four techniques were used to select the variant of the expressway section in north-eastern Poland. Erdogan et al. focused on applying the MCDA approach to optimal planning of workplace charging stations by adapting the WASPAS model with Dombi Bonferroni functions for flexibility [118]. Moreover, the obtained results were compared with MABAC, MAIRCA, and CoCoSo methods to verify the robustness of the rankings. The presented applications and trends showed that despite visible shortcomings, the AHP method is highly popular in developing decision models dedicated to transport areas. Moreover, the concept of fuzzy logic and its varied extensions is popular in modeling the occurring uncertainties. Based on the presented approaches directed to solve the problems in transportation, Table 4 presents the most popular research works.

2.5MCDA applications in sustainable development

MCDA is also effective in sustainable development because it can handle complex decision problems involving multiple, often conflicting, objectives. Sustainable development requires balancing economic, social, and environmental considerations, making it multi-dimensional. In sustainable development, where decisions have long-term repercussions on ecological systems, social equity, and economic prosperity, MCDA enables a comprehensive assessment of alternatives. It assists in integrating diverse stakeholder perspectives, aligning with the participatory nature of sustainable development. By concurrently considering environmental impact, social integration, economic viability, and other relevant criteria, MCDA ensures a more precise understanding of the consequences of various decisions. Additionally, the flexibility of multi-criteria techniques allows for adapting to evolving sustainability goals and shifting societal priorities, making it well-suited for the dynamic and evolving nature of the field.

Moreover, the transparency offered by MCDA aids in building consensus among stakeholders by providing a clear rationale for decision outcomes. It is crucial in sustainable development, where decisions often involve complex trade-offs, and stakeholder engagement is vital for successful implementation. MCDA significantly advances sustainable development goals by facilitating informed, inclusive, and balanced decision-making processes.

Since sustainable development covers various areas of operation, researchers addressed their research works in various directions and proposed many approaches to improve making decisions under different circumstances. Aksoy and San combined GIS and MCDA approaches to select sustainable landfill sites considering dynamic data source [119]. The AHP method was used to identify criteria importance. Gharizadeh et al. integrated the MCDA approach to assessing insurance companies regarding sustainability performance [120]. The AHP method was combined with Principal Component Analysis (PCA) and Data Envelopment Analysis to provide a combined subjective and objective evaluation approach. Erdogan, Šaparauskas, and Turskis determined the MCDA model to indicate the most rational solution for sustainable construction management [121]. Seven criteria (technical experience, record of performance, financial stability, the qualifications of the employees and the management, capacity, safety record, and equipment and operation) were evaluated with the AHP method. Das and Mukhopadhyay used the MCDA approach to assess the groundwater potential zones focusing on Birbhum district (West Bengal) in India [122]. By combining the AHP and GIS techniques, the authors indicated that 212.27 km2 has a very high potential. Omran et al. focused on the sustainability assessment of wastewater treatment techniques in urban areas of Iraq, employing 52 specialists to gather expert knowledge [123]. The Weighted Sum Model (WSM) was used to validate the results, while 31 factors grouped into four areas (environmental, social, economical, and technical) were considered. Manupati, Ramkumar, and Samanta faced the challenge of evaluating the urban renewal process in Southern India [124]. The determined framework consisted of seven criteria, and the decision variants were assessed by the Decision Making Trial and Evaluation Laboratory based Analytic Network Process (DANP) method.

Furthermore, Almeida focused on developing a tool to build indicators and localize Sustainable Development Goal (SDG) 11 in Brazilian municipalities [125]. To this end, the Multi-Actor MultiCriteria Analysis (MAMCA) approach was used with the AHP method to determine criteria relevance. Pagone, Salonitis, and Jolly proposed an approach for automatically weighted high-resolution mapping for sustainable manufacturing systems [126]. For this purpose, the entropy weighting method was used to determine criteria relevance objectively, and the TOPSIS method was used to evaluate decision variants. The authors indicated that the proposed methodology can be applied to sustainable manufacturing in general. Roszkowska and Filipowicz-Chomko measured sustainable development in the education area with the extended TOPSIS method [127]. The authors considered eight indicators from SDG4 to evaluate the 28 European countries. Ma et al. used the Life Cycle Assessment (LCA) to select sustainable material in the automotive industry [128]. The authors used the entropy weighting method combined with the TOPSIS technique to evaluate 16 alternatives. Talukder, Hipel, and vanLoon directed their research toward assessing the sustainability of agricultural systems regarding selected indicators (productivity, stability, efficiency, durability, compatibility, and equity) [129]. Chen and Zhang assessed the sustainability of cities in Liaoning province in China and considered 21 decision factors grouped into three areas, namely economic, social, and environmental [130]. The obtained results were then verified with selected criteria weighting approaches, namely entropy weights, equal weights, and Coefficient-Variation Method (CVM). Invidiata, Lavagna, and Ghisi addressed the problem of selecting design strategies to improve the sustainability of buildings in Milan [131]. The authors indicated that there will be an average increase of 53% in the cooling energy demand, while the heating energy demand in 2080 will decrease by 49%, compared to the consumption in 2017.

To handle incomplete knowledge in the challenged problems regarding sustainable development, many fuzzy extensions were applied to handle the decision process. Boral et al. proposed a novel hybrid group MCDA methodology for failure mode and effect analysis for sustainable manufacturing [132]. For this purpose, the Interval Type-2 Fuzzy DEMATEL and Modified Fuzzy MAIRCA methods were used to evaluate the process plant gearbox. Ezbakhe and Pérez-Foguet handled the problem of renewable energy planning under uncertain conditions [133]. For this purpose, the authors used a modified ELECTRE III model and represented the uncertainty in the performance scores as lower/upper bounds added to the model鈥檚 discrimination thresholds. The evaluation of renewable energy resources for Turkey (hydro, wind, geothermal, solar, and biomass) was performed under five main criteria (technological, technical, economic, environmental, and socio-politic). Nguyen, Lin, and Dang applied the Fuzzy MCDA approach for evaluating online food delivery in Vietnam [134]. For this purpose, the Fuzzy AHP method was integrated with the WASPAS technique to assess considered food companies. Raj, Kumar, and Verma used big data analytics to facilitate the challenges of a sustainable manufacturing supply chain with the AHP method combined with Grey Relational Analysis [135]. Eleven criteria grouped into five fields (financial, quality, operation, technical, and logistics) were considered in the decision process. Wang and Peng proposed a Fuzzy MCDA framework for assessing urban sustainable development using the Fuzzy Delphi method and adopting the Extent Analysis Method on Fuzzy AHP (EAFAHP) method to aggregate experts comments on empirical evaluation [136]. Yıldızbaşı et al. evaluated the sustainable supply chain indicator with the Fuzzy MCDA approach [137]. The Fuzzy AHP and Fuzzy TOPSIS methods were used to obtain results, which indicated the situation regarding the social sustainability of the automotive industry companies in Turkey.

On the other hand, selected approaches were directed toward assessing the robustness of the obtained rankings by comparing the performance of different MCDA methods. Ture, Dogan, and Kocak evaluated the EURO 2020 strategy with the VIKOR and TOPSIS methods [138]. Taking into account 22 indicators and assessing 27 European Union (EU) countries, the authors indicated that new EU member countries such as Slovenia and Romania have attained higher scores than many of the 15 EU countries. Mijajlović et al. addressed the problem of determining the competitiveness of Spa centers to achieve assumed sustainable goals [139]. Due to uncertainty in the problem, FUCOM was used to determine criteria weights, and the Fuzzy MARCOS method was used to assess decision variants. The results were verified with the Fuzzy SAW, WASPAS, MABAC, ARAS, and TOPSIS methods, while the sensitivity analysis of criteria weights scenarios was used to verify the impact of different criteria relevance. Bączkiewicz and Kizielewicz addressed the problem of sustainable energy consumption in Europe for the industrial sector [140]. Based on the European Statistical Office (EUROSTAT) data, the TOPSIS, VIKOR, COMET, and PROMETHEE II methods were used to evaluate European countries. Moghtadernejad, Chouinard, and Mirza provided a methodology for the preliminary design of sustainable facades, which has a crucial impact on building energy performance [141]. The authors used three selected MCDA methods, namely AHP, Choquet integrals, and TOPSIS while considering eight decision criteria: aesthetics, weight, fire resistance, acoustics, environmental impacts, ease of construction, durability, and initial costs. Suganthi focused on evaluating sectoral investments for sustainable development with integrated Fuzzy AHP, VIKOR/DEA methodology [142]. Yang et al. addressed the problem of sustainable sports tourism with a proposition of framework determined based on a hybrid MCDA model [143]. The aim was to explore potential sports tourism attractions in Taiwan. For this purpose, the Bayesian Best Worst Method (Bayesian BWM) was used to integrate multiple experts’ judgments to generate the group optimal criteria weights, and the VIKOR method was used to assess decision variants. The sensitivity analysis of criteria weights was performed, and the results were verified with the SAW, TOPSIS, PROMETHEE, and WASPAS methods. From the provided description, it could be seen that the authors addressed various problems, important to increase the quality of decision-making in sustainable development areas. Problems of fulfilling Sustainable Development Goals is a common challenge. Moreover, new approaches to handling uncertainties allowed to evaluate decision variants more effectively. Based on the presented research works, the highlight of the five most popular papers included in the review from the field on sustainable development were presented in Table 5.

2.6MCDA applications in other fields

Multi-criteria methods find applications across diverse fields beyond healthcare, energy management, transport, supplier selection, and sustainable development. In areas such as finance, MCDA can aid in investment decisions by simultaneously considering multiple factors like risk, return, and market conditions. In marketing, it can assist in product launch decisions by evaluating various criteria like market demand, competition, and cost-effectiveness. The versatility of MCDA extends to human resources, helping in recruitment processes by weighing qualifications, experience, and cultural fit. Urban planning can be utilized to assess different development proposals, considering factors like environmental impact, public opinion, and infrastructure needs. In each of the mentioned fields, MCDA’s strength lies in its ability to handle complexity, accommodate diverse criteria, and provide a structured framework for decision-making, promoting more informed and well-explained choices.

Since the MCDA approach could be adjusted to any practical application and field in which multiple opposite criteria are considered, researchers aim to develop new methodologies for solving the given problem with multi-criteria techniques. Askarifar, Motaffef, and Aazaami proposed an investment development framework in Iran’s seashores using TOPSIS and Best-Worst Method [144]. Rezaei et al. covered the problem of the product-package design in a food supply chain [145]. The authors used the BWM method to evaluate three selected products of the Kraft Heinz Company and examined the results’ stability by applying sensitivity analysis of weights scenarios. Bampa et al. covered the problem of harvesting European knowledge on soil functions and land management with the Decision EXpert (DEX) methodology to evaluate knowledge and needs [146]. Rani et al. proposed a multi-criteria food waste treatment method selection using a Single-Valued Neutrosophic (SVN) CRITIC Multi-Objective Optimization based on Ratio Analysis with the full multiplicative form (MULTIMOORA) framework [147]. The CRITIC method was used to determine criteria weights, while the MULTIMOORA allowed for alternatives’ evaluation. Grover, Chopra, and Krejci directed their research towards the public distribution system of food grains in Indian Punjab to indicate the approaches for decentralized food policies in developing countries [148]. The MAVT method was used to assess analyzed decision variants. Vavrek analyzed the effectiveness of MCDA methods in athlete evaluation, directing the assessment to National Hockey League (NHL) attackers [149]. The TOPSIS method was used, and the results were verified by comparing different criteria weighting methods. Iacovidou and Voulvoulis proposed a framework for development and application in comparing two food waste management options using a UK region [150]. The DEFINITE decision support software was employed to assess considered decision variants, allowing the evaluation of the performance of criteria/sub-criteria using a range of analytical methods. Moreover, the authors conducted uncertainty and sensitivity analyses related to the criteria/sub-criteria weighting variations. Kondratenko, Kondratenko, and Sidenko applied the MCDA approach to select a rational Internet of Things (IoT) platform based on the Linear Convolution Method (LCM), which allows to bring the multi-criteria problem to one-criterion [151].

In the reviewed papers, it could be seen that the Analytical Hierarchy Process method is still highly popular, despite the development of newer techniques aiming to improve the process of determining criteria weights. Everest combined the GIS and MCDA approaches to select a suitable site for pistachio (Pistacia vera) in Turkey [152]. The AHP method was used to determine criteria relevance. Haroun et al. challenged the problem of evaluating the adaptive reuse of heritage buildings using the case of Aziza Fahmy Palace (Alexandria) in Egypt [153]. The authors used the AHP method to determine the decision model. Maleki et al. focused on the social sustainability assessment of high-rise residential buildings [154]. The authors developed an MCDA tool to assess sustainability using the Integrated Value Model for Sustainability Assessment (MIVES) built on the AHP method. Mantogiannis and Katsigiannis used the Analytical Hierarchy Process model to evaluate four real estate investment alternatives from the UK private rented market. The authors validated the volatility of the financial performance indicators through Monte Carlo simulation runs [155]. März evaluated the fuel poverty vulnerability of urban neighborhoods for the German city of Oberhausen [156]. For this purpose, the GIS-MCDA approach was used with the AHP methods applied to identify criteria weights. Pham et al. integrated deep learning and the MCDA approach to perform a flood risk assessment [157]. The authors used one of the popular Deep Neural Networks (DNN) algorithms to generate flood susceptibility maps while the Analytic Hierarchy Process was used to generate the hazard, exposure, and vulnerability maps.

The authors handled selected practical applications to MCDA problems by introducing extensions of fuzzy logic to the considered problem space to model the uncertainties effectively. Deveci et al. focused on prioritizing zero-emission zone logistics [158]. For this purpose, the authors applied the Defining Interrelationships Between Ranked criteria (DIBR) method to gather the experts’ opinions and compute the weights, while a novel approach that integrates Combined Compromise Solution (CoCoSo) with the context of type-2 neutrosophic numbers was used to evaluate alternatives. Sahin et al. applied the Fuzzy TOPSIS method with vertex distance to select the dry bulk carrier [159]. Gireesha et al. developed an improved Interval-Valued Intuitionistic Fuzzy Sets Weighted Aggregate Sum and Product Assessment (IIVIFS-WASPAS) to select cloud service providers for the identification of trustworthy solutions [160]. The sensitivity analysis of the impact of the change in coefficient characteristic for the proposed methodology was verified, and the feasibility of the Rank Reversal phenomenon was also evaluated. Kizielewicz and Dobryakova applied the COMET method to model the uncertain knowledge in the problem of basketball players’ assessment [161]. Sharma, Gupta, and Acharya focused on prioritizing the critical factors of cloud computing adoption, engaging experts from 13 organizations in the assessment process [162]. The AHP and Fuzzy AHP methods were applied for this purpose. Simon et al. proposed a methodology for the mass customization model in the food industry using industry 4.0 standard [163]. The authors used the Fuzzy AHP method to determine criteria weights. Urbaniak, Wątróbski, and Sałabun focused on evaluating the performance of e-sports players in the Counter-Strike: Global Offensive (CS:GO) with the COMET method [164]. Vazifehdan and Darestani addressed the problem of green logistics outsourcing by employing MCDA and Quality Function Deployment (QFD) in the petrochemical industry [165]. The authors used Fuzzy ANP, Fuzzy DEMATEL, and Superiority and Inferiority Ranking (SIR) methods.

Researchers also addressed the problem of examining the robustness of the MCDA methods since different techniques could produce unequivocal results. Pamucar et al. developed a new Logarithm Methodology of Additive Weights (LMAW) method to solve multi-criteria problems and verified it in the logistics area to evaluate service providers in India [166]. The results were verified with selected MCDA techniques, namely TOPSIS, VIKOR, Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI), COPRAS, and MABAC. Rutten-van Mölken et al. directed their research toward strengthening the evidence base of integrated care for people with multi-morbidity in Europe [167]. The authors applied the Discrete Choice Experiment (DCE), Swing Weighting, and Simple Multi-Attribute Rating Techniques Exploiting Ranks (SMARTER) to evaluate criteria relevance and performed a sensitivity analysis by comparing selected methods and excluding certain criteria in the problem. Moreover, the proposed approach modeled the parameter uncertainty in the performance scores and the criteria weights simultaneously in a probabilistic sensitivity analysis using Monte Carlo simulation. Guarini, Battisti, and Chiovitti determined a methodology for site selection in real estate and land management processes [168]. For this purpose, the performance of selected MCDA methods was compared, namely Measuring Attractiveness by a Categorical Based Evaluation (MACBETH), TOPSIS, ANP, AHP, Multi-Attribute Utility Theory (MAUT), PROMETHEE, and ELECTRE. Vakilipour et al. evaluated the quality of life at different spatial levels [169]. The authors compared the performance of selected MCDA methods, namely TOPSIS, VIKOR, SAW, and ELECTRE, and assessed alternatives within three spatial levels: socioeconomic, environmental, and accessibility. Pamucar, Chatterjee, and Zavadskas assessed third-party logistics providers [170]. The authors proposed a novel BWM-WASPAS-MABAC model based on interval rough numbers. The sensitivity analysis of different weighting scenarios was applied to verify the results’ stability. Puška, Beganović, and Šadić developed an investment model based on the MCDA approach by applying the SAW, TOPSIS, and VIKOR methods [171]. Tsakalerou, Efthymiadis, and Abilez proposed an intelligent methodology for offshore construction evaluation [172]. To assess decision variants, the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method was applied. Moreover, the authors used the COMET method to handle uncertainties in the problem. Widianta et al. applied AHP, TOPSIS, SAW, and PROMETHEE methods to evaluate employee placement [173]. The results showed that each method’s ranking and accuracy levels differed depending on the different scaling and weighting processes in the examined methods. Based on the presented works included in the review, it could be seen that varied practical problems were addressed in the literature recently. Besides five mainly distinguished fields (healthcare, energy management, transport, supplier selection, and sustainable development), the authors covered the areas of sport management, logistics, and cloud computing, among others. Table 6 presents the most popular research works dedicated to fields other than five main fields identified in the review.