The primary objective of this study is to examine whether expert-based Multi-Criteria Decision Analysis (MCDA) methods might be used to support physiotherapists in yoga-based treatment of non-specific Lower Back Pain (LBP). LBP is one of the most common pain syndromes globally, particularly in industrialized and aging populations, and its prevalence continues to increase. This growing challenge highlights the need to develop AI-based systems that can support not only conventional medicine but also physiotherapeutic approaches to treatment. To our knowledge, this is the first study to explore the application of MCDA methods in yoga-based therapy. To fill this gap, we tested four expert-based MCDA methods — Analytic Hierarchy Process (AHP), Best - Worst Method (BWM), Ranking Comparison Method (RANCOM), and Base Criterion Method (BCM) with the support of two highly experienced physiotherapy experts. Despite minor differences, all methods delivered consistent and highly promising results, demonstrating the potential and usefulness of MCDA approaches in yoga-based treatment of LBP. The results lay the groundwork for future development of AI-supported decision-making tools tailored to physiotherapeutic and integrative treatment strategies. This research contributes to the growing body of evidence on the applicability of MCDA in healthcare and highlights its relevance in non-conventional therapeutic contexts.

This study proposes a novel hybrid Multi-Criteria Decision Analysis (MCDA) framework integrating Stochastic Fuzzy Normalization (STFN) with Stochastic Identification of Weights (SITW) to enhance the modeling and replication of sustainability rankings. The methodology is applied to a dataset of 21 European countries, utilizing six key indicators related to material consumption, waste generation, circular economy, environmental value added, and energy productivity. The proposed STFN–SITW–TOPSIS model successfully reconstructs the structure of the Sustainable Development Report 2022 ranking, achieving high fidelity through reidentification of both fuzzy normalizations and criteria weights. Comparative analyses demonstrate that SITW-based weights outperform conventional objective weighting methods such as Criteria Importance Through Intercriteria Correlation (CRITIC), Entropy, and Equal weighting in terms of rank alignment and structural consistency. A comprehensive sensitivity analysis confirms the model’s robustness and identifies the most influential criteria. The findings highlight the importance of integrating external reference information into MCDA frameworks, offering a data-driven, flexible, and explainable approach to sustainability assessment and broader decision support systems.

This paper introduces WEIghts COMpromise (WEICOM), a novel aggregation method designed to derive a consensus weight vector in Multi-Criteria Decision Analysis (MCDA) involving multiple expert inputs. WEICOM aims to maximize the average similarity, measured by the Weights Similarity Coefficient 2 (WSC2), between the aggregated vector and a set of initial weight vectors, ensuring a balanced representation of all expert preferences. The method is applied to a real-world case study, assessing energy-related sustainability indicators in agri-food systems. The study evaluates the energy performance of countries based on six key criteria, including CO 2 emissions, on-farm energy use, and waste disposal. In addition, a simulation study is conducted to assess the robustness of WEICOM under varying numbers of criteria and experts, comparing its performance with tradition aggregation methods such as mean, median, and maximin. Results from both empirical and simulation-based analyses demonstra that WEICOM consistently outperforms conventional aggregation techniques, offering a more stable and transparent solution for integrating expert judgments in complex decision-making contexts. This approach provides valuable insights for applications in sustainability assessments, participatory decision-making, and environmental policy evaluations.

This study presents a sustainable framework for vehicle assessment using FN-TOPSIS integrated with five weighting methods: RANCOM, AHP, LBWA, FUCOM, and BWM. By utilizing fuzzy normalization, the FN-TOPSIS model achieves greater stability in rankings and better alignment of criteria with goal-oriented objectives. The methodology was applied to a comprehensive dataset containing 7,385 vehicle alternatives, evaluated based on six key criteria, including environmental and technical factors such as CO 2 emissions and fuel efficiency. A comparative analysis of rankings using cosine similarity, WSC 2 , and the weighted Spearman rank correlation coefficient (r w ) revealed significant alignment and variability among the methods, highlighting the influence of weight determination on prioritization outcomes. The results demonstrate the effectiveness of FN-TOPSIS in integrating multidimensional criteria for sustainability, providing consistent and reliable rankings. Methods like AHP, FUCOM, and BWM prioritize dominant criteria, while LBWA adopts a more balanced approach, and RANCOM shows moderate variability. These findings emphasize the importance of selecting appropriate weighting methods tailored to the decision-making context. This framework offers a versatile tool for assessing vehicle sustainability, with potential applications in other fields, such as energy policy and urban planning. Future research could explore dynamic criteria, broader datasets, and stakeholder perspectives to further enhance the model’s adaptability and applicability in sustainability-focused decision-making.

In the context of increasing global energy demand and the urgent need for sustainable energy policies, evaluating national energy efficiency has become a critical analytical endeavor. This study presents a comparative assessment of 33 European countries using three prominent Multi-Criteria Decision Analysis (MCDA) methods: Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Visekriterijumsko Kompromisno Rangiranje (VIKOR), and Weighted Aggregated Sum Product Assessment (WASPAS). Energy-related indicators for the year 2022 were sourced from Eurostat, and criterion weights were derived using the entropy weighting method to ensure objectivity. The analysis focuses on ranking consistency, inter-method correlation, and the phenomenon of rank reversal, which poses a significant threat to the robustness of decision-making frameworks. Results reveal a strong correlation between TOPSIS and VIKOR, while WASPAS exhibits greater resilience to rank instability. The findings underscore the importance of methodological sensitivity when interpreting MCDA-based rankings, especially in international comparative contexts. The study contributes to the methodological discourse by highlighting trade-offs between consistency, robustness, and interpretability in energy efficiency evaluations. It offers practical implications for policymakers seeking to adopt transparent and reliable decision-support tools for energy planning within the European context and beyond.

This paper proposes a novel hybrid Multi-Criteria Decision Analysis (MCDA) framework aimed at assessing Quality of Life (QoL) among students. The framework integrates four objective weighting methods (Gini, Equal, CRITIC, and Angle) with the Expected Solution Point Stable Preference Ordering Towards Ideal Solution (ESP-SPOTIS) method, which allows for personalization of the decision-making process through the specification of an ESP. This hybridization enables the construction of rankings that reflect both objective criteria importance and subjective expectations regarding an ideal QoL profile. To compare the consistency between the different hybrid models, Weighted Spearman and Kendall Tau correlation coefficients were calculated. To further support decision stability and reconcile differences across models, a Compromise COMET (C-COMET) model was developed. It aggregates evaluations over characteristic objects using a voting-based Matrix of Expert Judgment (MEJ). The results demonstrate that the proposed approach provides a stable, interpretable, and flexible method for analyzing Quality of Life, particularly within educational environments, where individual needs and holistic well-being are critical. This methodology can be adapted to broader QoL applications in social, urban, and policy contexts.

This study presents a comparative analysis of Multi-Criteria Decision Analysis (MCDA) methods applied to the supplier evaluation problem, with a focus on ranking stability and weight adjustment. Four prominent MCDA methods—FN-TOPSIS, FN-VIKOR, FN-MABAC, and SPOTIS—are used to generate individual supplier rankings based on a decision matrix comprising 34 alternatives and 12 evaluation criteria. To establish a unified reference point, a compromise ranking is constructed using the Copeland method. The main objective of the study is to determine how each method would need to adjust its internal weighting scheme to align with the compromise ranking. For this purpose, the Stochastic Identification of Weights (SITW) approach is employed. Fuzzy normalization using triangular fuzzy numbers is applied to stabilize the input data prior to analysis. Although fuzzy logic is used for normalization, all underlying data and evaluations are crisp. Ranking similarity is assessed using both the Weighted Spearman correlation coefficient and the Ranking Similarity Coefficient. Additionally, the Weights Similarity Coefficient 2 (WSC2) is used to evaluate the convergence of optimized weights. Results show strong alignment between FN-TOPSIS, FN-VIKOR, and FN-MABAC, both in rankings and re-identified weight vectors, while SPOTIS demonstrates a distinct internal logic and lower similarity with other methods. The proposed methodology offers a robust framework for understanding methodological behavior, enhancing decision transparency, and improving weight interpretability in complex supplier selection scenarios.

The determination of weights is a critical step in Multi-Criteria Decision-Making (MCDM) processes, and subjective methods play a significant role in this area. This paper provides a comprehensive review of subjective weight determination methodologies, focusing primarily on sources from the Scopus database. Through a systematic literature review, this study analyzes 33,434 publications related to subjective weight determination, providing insights into the trends, advancements, and applications of these methodologies. The review underscores the importance of expert knowledge in contemporary decision-making and suggests potential areas for future research, including the integration of subjective methods with emerging technologies and the development of more robust and consistent weighting frameworks. This paper aims to serve as a valuable resource for researchers and practitioners in the field of MCDM, offering a detailed overview of the current state of subjective weight determination methods and their practical implications.

Complex networks attract attention in various scientific fields due to their ability to model real world phenomena and potential for problem-solving.It is essential to evaluate these networks to simulate and solve various issues.Evaluating social networks is challenging due to the unequal status of nodes and their unknown impact on everall characteristics.Existing measures of centrality often need to consider the global structure of the network, which requires the involvement of experts and creates space for multi-criteria decision-making methods usage.Unfortunately, more access to established decision-making models is often needed for various reasons.In this article, we propose an innovative approach called Dynamic Stochastic IdenTifiCation Of Models (Dynamic SITCOM), which considers the preferences of characteristic objects and the characteristic values of criteria, enabling the re-identification of multi-criteria decision models.The approach evaluates nodes in Facebook's complex social network, focusing on prediction accuracy using similarity measures and Mean Absolute Error.The study shows that a stable decision model can be created and applied to evaluate nodes in complex networks.

In recent years, complex networks have gained significant attention for their practical potential in data analysis and decision-making.However, assessing node relevance in complex networks poses challenges, including subjectivity and difficulty reproducing criteria relationships.To address these issues, we propose MLP-COMET.This novel approach combines the Multi-Layer Perceptron (MLP) with the Characteristic Objects Method (COMET) in Multi-Criteria Decision Analysis (MCDA).MLP-COMET aims to re-identify decision models using MLP to evaluate characteristic objects.We evaluate the approach to assessing the complex network and demonstrate its effectiveness in evaluating without heavy reliance on domain experts.The MLP-COMET performance is evaluated through ranking comparisons, showing a strong correlation with reference expert rankings.We also analyze the impact of training sample size and number of characteristic objects on ranking similarity, observing high stability and similarity using the rw metric.MLP-COMET offers an effective and reliable tool for evaluating complex networks and facilitating decision-making processes.

Data normalization is essential in many fields, such as speech recognition, deep learning, machine learning, and optimization. Many researchers focus on developing various normalization techniques, such as min-max, score-based, maximum, vector, or sum, on processing data efficiently. However, classical normalization methods may need to be improved in Multi-Criteria Decision-Analysis (MCDA), where criteria are often non-monotonic. This paper proposes a new approach for identifying and normalizing nonmonotonic criteria in MCDA. The method combines a stochastic optimization technique - a genetic algorithm - with normalization based on triangular fuzzy numbers. This paper compares the proposed approach with classical normalization techniques, such as min-max, maximum, vector, or non-linear, using the classical MCDA method - the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS). The study results showed that the proposed approach to normalizing non-monotonic criteria yields a better match between decision preferences and the actual reference model than traditional normalization methods. Optimization using the genetic algorithm helps identify the means of triangular fuzzy numbers, which generate more accurate results. Analysis of the R2 coefficient confirms a very good fit of the proposed model. Therefore, the new non-linear normalization approach in MCDA presented in this work has great potential in adjusting decision preferences to non-monotonic criteria. Future research should continue on the accuracy of this approach in identifying non-linear decision models and apply it to other MCDA methods and real-world decision problems.

This study introduces an extension of the Characteristic Objects Method (COMET) by incorporating the Expected Solution Point (ESP) approach to enhance individual decision-making. By integrating the ESP methodology, we aim to simplify decision-making, particularly when facing a vast decision matrix concerning multiple criteria. We demonstrate the efficacy of the proposed ESP-COMET method by applying it with one or two ESPs, thereby examining its impact on ranking alternatives according to personal customer preferences. In order to provide a practical example, we use a dataset of used cars to show how the proposed method facilitates the selection of the most suitable vehicle based on individual needs. The main goal of this paper is to show how the ESP-COMET method can help to adapt decisions to individual needs and expectations. We also introduce a novel and efficient approach for determining characteristic values in personalized decision-making, which plays a crucial role in ensuring the higher accuracy of the ESP-COMET method. Additionally, we compare the proposed approach with such methods as Stable Preference Ordering Toward the Ideal Solution (SPOTIS) and Reference Ideal Method (RIM). This study addresses the research gap by introducing an easy way to define customer preferences within COMET, enabling responsible decision-making.

We encounter uncertainty in many areas.In decision-making, it is an aspect that allows for better modeling of real-world problems.However, many methods rely on crisp numbers in their calculations.It makes it necessary to use techniques that perform this conversion.In this paper, we address the problem of score functions assessment regarding their effectiveness and usefulness in the decision-making field.The selected methods were used to convert the intuitionistic fuzzy set matrix into crisp data, then used in the multi-criteria assessment.Managing the theoretical problem showed that the used techniques provide high similarity values.Moreover, they proved to be helpful when dealing with intuitionistic fuzzy sets in the decision-making area.

In many cases involving multi-criteria decisionmaking, we need compromise solutions.This is a crucial aspect due to the specific characteristics of decision problems.However, the proposed compromise approaches are often complex to verify to what extent they are reliable.Therefore, this paper proposes a new iterative approach based on decision option evaluations from selected multi-criteria decision-making methods, i.e., TOPSIS, VIKOR, and SPOTIS.The obtained results have high similarity among each other, which was measured by Spearman's weighted correlation coefficient and WS ranking similarity coefficient.Furthermore, the proposed approach showed high efficiency and adaptability of the generated results.

Most Multi-Criteria Decision Analysis (MCDA) methods identify only discrete, defined sets of alternatives. Such identification does not provide an opportunity to consider a more significant number of samples acquired over long periods, which is not a desirable effect. An additional aspect of identification is the frequent application of expert knowledge, whereby a continuous model would have to be defined to consider further alternatives that would be suitable for further use. This paper considers a new approach for identifying continuous decision models named SITCOM. The purpose of this paper was to conduct a study on the accuracy of the proposed approach. The accuracy of Stochastic IdenTifiCation Of Models (SITCOM) was tested using sample evaluations obtained from fuzzy reference models (FRM). Spearman's weighted correlation coefficient was used to measure the accuracy of the created models. The experiments showed the dependence of accuracy on the number of characteristic objects. Furthermore, the obtained results prove the reliability of the obtained results, where the trends of accuracy decrease based on the number of characteristic values.

Objective evaluation in problems considering many, often conflicting criteria is challenging for the decision-maker.This paper presents an approach based on MCDA methods to objectify evaluations in the camera selection problem.The proposed approach includes three MCDA methods, TOPSIS, VIKOR, COMET, and two criterion weighting techniques.Two ranking similarity coefficients were used to compare the resulting rankings of the alternatives: W S and rw.The performed research confirmed the importance of the appropriate selection of multicriteria decision-making methods for the solved problem and the relevance of comparative analysis in method selection and construction of objective rankings of alternatives.

Defining input values in the decision-making process can be done with appropriate methods or based on expert knowledge.It is essential to ensure that the values are adequate for the problem to be solved in both cases.There may be situations where values are overestimated, and it should be checked whether this affects the final results.In this paper, the Characteristic Objects Method (COMET) was used to investigate the overestimation effect on the final rankings.The decision matrixes with a different number of alternatives and criteria were assessed The obtained results were compared using the WS similarity coefficient and Spearman's weighted correlation coefficient.The study showed that overestimation has a significant effect on the rankings.A larger number of criteria has a positive effect on the correlation strength of the compared rankings.In contrast, a large overestimation of characteristic values has a negative effect on the similarity of the results.

This paper presents a new approach to evaluate sustainable energy consumption in selected European countries for the industrial sector and to identify which country is leading in this respect. Energy consumption has been on a continuously rising trend due to industrial development, migration of people from rural to urban areas and growing energy demand in the service sector. Therefore, it is essential to increase the share of renewable sources in energy consumption because it allows reducing the share of non-renewable sources, which significantly contribute to carbon dioxide emissions to the atmosphere. The assessment of the effectiveness of actions taken to improve the sustainability of energy consumption requires the consideration of many different criteria. The practical usefulness of multi-criteria decision analysis (MCDA) methods in this task has been proven in the literature. The structure of the model evaluated was based on criteria made available by the European Statistical Office (EUROSTAT). The study was conducted using four MCDA methods (TOPSIS, VIKOR, COMET and PROMETHEE II). The results obtained proved the usefulness and adequacy of the selected MCDA methods in assessing the problem in question. It was possible to clearly identify the country with the most sustainable energy consumption in terms of the established principles.

Decision-making problems in an uncertain environment can be solved using fuzzy multi-criteria decision-making methods. One such problem is the problem of housing selection, where it is challenging to determine data explicitly. Many people struggle with the difficulty of determining what conditions they would like to live in. It is hard for them to determine their housing preferences based only on data from the offers. Numerical values do not clearly define many parameters. Both apartment buyers and realtors use values such as much or little, precisely represented by fuzzy numbers. This paper proposes a solution to the housing selection problem using fuzzy MCDA techniques such as Fuzzy MMOORA, Fuzzy WASPAS, Fuzzy TOPSIS and Fuzzy VIKOR. Then the obtained ranking of alternatives was compared using ranking similarity coefficients such as weighted Spearman coefficient and WS ranking similarity coefficient. The proposed approaches produce similar results, indicating that the decision models reflect preferences well. At the end of the paper, conclusions and further research directions are presented.

The problem of choosing the optimal urban transport modes is a broad and challenging subject to study and will be addressed in this document. The current urban transport problem affects the environment the most. Many modes of urban transport use energy from exhaust gas, which is a significant air pollutant. The fuels that powering those modes of transportation are also a non-renewable source of energy, which means that they will run out at some point and no longer be able to available. The solution to this problem could be to replace urban transport combustion vehicles with green-energy vehicles. One such means is electric scooters, whose energy source can be renewable electricity. Currently, there are many types of electric scooters on offer, which differ from each other by some important parameters. Choosing the optimum electric scooter is a significant issue. Investing in electric scooters for the city can bring big economic and environmental profits. These issues would be the primary motivation and justification to undertake this research. In this paper, the Characteristic Objects METhod (COMET) is used to identify the assessment model electric scooters for the sustainable development of cities. This approach has an extremely rare feature in the Multi-Criteria Decision Analysis (MCDA) methods, i.e., it is resistant to the paradox of reversal of rankings and is easy to use for an expert. This technique is crucial as an uncomplicated and effective way of solving decision-making problems. For the problem of selecting electric scooters, an expert was chosen, who identified the model by making pairwise comparisons of characteristic objects. Then, the model was used to evaluate the alternatives, and so a ranking of considered electric scooters was created. Once identified, the model can evaluate the next set of alternatives, which is important in such a dynamic market of new technologies.

Basketball is a constantly developing sport due to the dynamics of changes in the significant characteristics of a basketball player. The acquisition of new basketball players’ skills strongly influences the development of new technologies, as well as the game itself. In addition, it plays an important role in the personal development of young people seeking new achievements. However, it is difficult to determine which basketball player is the best. Creating a proper ranking of the best basketball players in the NBA league is extremely important. There are many rankings of players in the NBA league, but it is difficult to determine if they are correct due to lack of relevant data in the assessment process. The NBA has many talented basketball players in different positions. Their different predispositions make the correct ranking very difficult. Using only one attribute of a given basketball player may prove to be wrong when assessing basketball players playing on different positions. In addition, some basketball players may be missing data, because it was not collected during the player’s playing time. Therefore, the survey was carried out in the absence of some statistics in several basketball players. The aim of such a survey is to prove that creating the correct ranking is possible for incomplete data. For the evaluation of selected basketball players from the NBA league a technique from the family of multi-criteria decision making methods (MCDA) called COMET was used. The COMET method works on the basis of fuzzy logic, and its distinguishing feature compared to other methods is its resistance to the paradox of reversal of rankings. Resistance to the paradox of reversal of rankings is ensured by the fact that the assessment of alternatives does not take place on their own, but on characteristic objects. An expert has been involved in their proper evaluation. This article presents research against NBA basketball players, which shows that correct ranking of basketball players even with partial lack of data for them is possible.

This paper describes preliminary research on the relationship between the selection of characteristic values in the COMET method and the accuracy of the final ranking. Simulation studies on three test functions are presented. These functions are references to the conducted simulations and have been taken from the literature. Thanks to their application, human error can be excluded and focus only on methodical error. The presented experiment compares two approaches in determining characteristic values. For each function, 10000 randomly selected sets are generated and based on which the similarity of the ranking calculated by the COMET method with a reference ranking is calculated. The results are interpreted by means of box diagrams. The research showed the high effectiveness of both approaches and was used to determine the next directions for future works.

In this paper, we identify the mini-model in the space of the state of the problem of evaluating alternative hydropower systems on the Drina River. The COMET method was used for this purpose. This method focuses on identifying decisional hyper-surface in the presence of conflicting criteria. For this purpose, the expert makes comparisons in pairs of the indicated characteristic objects. Based on hyper-surface, we can rank alternatives and choose the best one. The alternatives were taken from previous work using the VIKOR method. Unlike the VIKOR method, the COMET method does not require weighting and is resistant to the rank reversal phenomenon. Finally, the results obtained using both ways were compared by using similarity coefficients.