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The methods of multi-criteria decision making are more and more willingly used as assistance in finding an optimal solution to the problem. As the family of these methods grows, choosing the appropriate technique and whether the use of multiple methods for one problem will provide a similar final result may be a problem. In this paper, extensions of the classic TOPSIS method have been used to show one decision problem results. We used the Interval TOPSIS, Bag-Based TOPSIS, and Fuzzy TOPSIS methods to examine the influence of used methods to final rankings. They solved the theoretical problem of selecting a high-protein supplement for persons in power training. The results were then compared using selected similarity coefficients to emphasize the correlation between the resulting rankings. The research showed that the method used has an impact on the final ranking, which was confirmed by the indicators obtained through the similarity coefficients. [ABSTRACT FROM AUTHOR]

Many decisions made in different areas of life require a certain number of criteria to be taken into account to help find the optimal solution. The problem may be to determine whether the way in which we represent the input has an impact on the final result. It means whether or not the normalization affects the results achieved by using multi-criteria decision analysis (MCDA) methods. In this paper, we examine the influence of normalization on the results obtained by the VIKOR method. This issue will be addressed based on the training management problem. First, we prepare the raw decision matrix and its forms after normalizations. According to the VIKOR method, we use a calculating procedure for all cases and compare the obtained rankings using selected similarity coefficients, which showed to what extent these rankings are similar. The results obtained as a result of the conducted research show that the presentation of the input data has an impact on the final obtained rankings. [ABSTRACT FROM AUTHOR]

Abstract Background Insufficient anatomical training can put patients' safety at risk. The aim of this study was to assess the proficiency of medical students and physicians in identifying labeled anatomical structures. The second aim of the study was to evaluate factors that can affect this recognition. Methods An internet-based survey where participants had to correctly identify labeled anatomical structures on cadaveric specimens was designed. Results The study group included 1186 participants (58.7% females): 931 medical students and 255 medical graduates from all twelve Polish medical schools. The mean total survey score for the entire study group was 65.6%. Students gained significantly higher results than graduates (total: 67.3% vs. 59.5%, P < 0.001); 331 (27.9%) participants did not pass the test (<60). There was a correlation observed between points gained in this survey and grade obtained in the gross anatomy course (P < 0.001). Multivariable logistic regression found that participation in cadaver laboratory classes most strongly increases anatomical competencies (OR = 5.30, 95%CI = 1.20–23.40, P = 0.03). Other significant factors boosting anatomical proficiency were membership in students' scientific clubs, being male, and having a high grade (≥80%) in initial gross anatomy course. The time since anatomy course completion was negatively correlated with the total survey score (OR = 0.86, 95%CI = 0.81–0.92, P < 0.001). Conclusions Anatomical knowledge of Polish medical students is moderate (<70%) and it significantly decreases with time. Anatomical structure recognition can be up to 25% lower in highly trained physicians when compared to pre-clinical medical students. This trend may be reversed by replacing subject-based anatomy courses with system-based (integrated) curricula at the undergraduate level or introducing short refresher anatomical courses during postgraduate training. [ABSTRACT FROM AUTHOR]

Effective evaluation of cities' and communities' sustainability is important for sustainable development. From a methodological point of view, Multi-Criteria Decision Analysis (MCDA) methods proved their usability in the sustainability evaluation domain. Nevertheless, the process of building a model in the classical MCDA paradigm is based on a single set of input data. Therefore, it may lead to oversimplification, especially in the domain of sustainability. Moreover, in addition to the current assessment, it is also important to know the dynamics of sustainability change over time. Therefore, this paper proposes an innovative sustainability assessment method that integrates the MCDA approach with the variability of the alternatives' performance measurement called Data vARIability Assessment Technique for Order of Preference by Similarity to Ideal Solution (the DARIA-TOPSIS method). This method was used to assess sustainable cities and communities in 26 European countries. Time-based analyses conducted using the DARIA-TOPSIS method for aggregated data (countries), individual sustainability dimensions, and alternatives proved our new approach's usefulness, suitability, and effectiveness in sustainable cities and society domain. [Display omitted] • A new MCDA method named the DARIA-TOPSIS. • A DARIA-TOPSIS based framework for sustainable cities and communities assessment. • The practical problem of sustainable cities and communities assessment. [ABSTRACT FROM AUTHOR]

The correlation between two different variables possesses an important place in the field of statistics. Correlation is used to estimate the change in one variable associated with another variable. The concept of correlation is well known but it is not used within intuitionistic 2-tuple fuzzy linguistic (I2TFL) information. To investigate the best alternative among different alternatives in multi-criteria group decision-making (MCGDM) problems, our aim is to propose a formula for the correlation coefficient based on I2TFL information. A new type of information energy for I2TFL element is defined first and then the correlation coefficient between I2TFL information are discussed based on this information energy. In addition, considering that different I2TFL elements may have different criteria weights, the weighted correlation coefficients are further investigated. Some of the properties of the proposed correlation coefficient are also mentioned and verified. Furthermore, best-worst method (BWM) is expanded to uncertain situations like I2TFL BWM, where the data is expressed in I2TFL form. Utilizing the idea of BWM, linearly constrained optimization problems are framed to acquire weights of criteria in the context of I2TFL environment. Two numerical illustrations are described to confirm the effectiveness of the proposed work in the cases where the criteria weights are either known or unknown. Finally, comparative discussions between the proposed approach and existing practices are taken to reveal the importance of our work. • Multi-criteria group decision-making based on the correlation coefficient. • Intuitionistic 2-tuple fuzzy linguistic environment (I2TFL). • Correlation coefficient between I2TFL. • Best-Worst method. [ABSTRACT FROM AUTHOR]

• Priority vector construction ways in BWM method under I2TL information are proposed. • Hamacher aggregation operations for I2TL are introduced. • MCGDM framework under I2TL information is proposed. Sometimes in the sense of intuitionist 2-tuple linguistic (I2TL) sets, experts may not be able to decide the most suitable criterion weight vector for multicriteria decision making (MCDM). To avoid this situation, the decision-makers (DMs) can use the Best-Worst method (BWM), in which DMs choose the best (most significant) criterion and the worst (least significant) criterion and then provide two preference vectors by comparing criteria best to other (BO) and other to worst (OW). In the Nonlinear Best-Worst Method (NBWM) it is more complicated to find the unique solution of the model. Therefore, the main goal of this study is to propose two approaches to BWM, namely, Linear Best-Worst Method (LBWM) and Euclidean Best-Worst method (EBWM) to achieve the best criteria priority vector for Multi-Criteria Group Decision Making (MCGDM) problems in the context of I2TL information. In the computational process of MCDM problems, we have to aggregate I2TL elements into a global one. Consequently, under certain critical properties, we are creating some operational laws for I2TL elements based on Hamacher operations. Also, the intuitionistic 2-tuple linguistic Hamacher weighted average (I2TLHWA), and the intuitionistic 2-tuple linguistic Hamacher weighted geometric (I2TLHWG) operators are introduced with the assistance of Hamacher operations and I2TL elements. Subsequently, we analyze some of the I2TLHWA operator's related properties and we propose MCGDM framework under I2TL information. Finally, we demonstrate the validity and efficiency of our method and operations. [ABSTRACT FROM AUTHOR]

This paper introduces a new V-COMET method for assessing the sustainable management of renewable energy sources (RES) in European countries. The proposed method belongs to the multi-criteria methods and is free of significant shortcomings occurring in most Multi-Criteria Decision Analysis (MCDA) methods. The paper shows that the V-COMET method is characterized by high accuracy and has very limited computational complexity. The practical contribution of the paper covers the use of the developed V-COMET method in RES assessment. The practical research domain covers the evaluation of 30 European countries in respect of criteria provided by Eurostat (the statistical office of the European Union). Three evaluation models with high criteria details for comparison were proposed: model with KTOE, percentage, and TOE per capita data. Due to the practical potential of the model with data in TOE per capita based on the V-COMET method, full robustness and sensitivity tests of the country rankings were carried out, showing the wide practical possibilities of the proposed approach. Additionally, a methodological evaluation of the proposed V-COMET method was performed. For this purpose, the uncertainty was investigated, and the developed V-COMET method was benchmarked with the set reference MCDA methods. The results allow concluding that the proposed method shows very high accuracy. [Display omitted] • Multi-criteria methods application in renewable energy sources (RES) evaluation domain is investigated. • A new multi-criteria method called V-COMET is proposed. • Based on the method developed a practical framework for sustainable RES evaluation is proposed. • The practical problem of sustainable RES assessment in European Union countries is investigated. [ABSTRACT FROM AUTHOR]

The identification of the criteria relevance from the expert in the multi-criteria problem could be a challenging task. It is important to provide simple and intuitive techniques that reflect the expert knowledge effectively. When the problem includes many criteria, it is possible to occur some probability of error in determining the criteria relevance. However, most subjective weighting methods perform correctly when the experts do not hesitate, are repeatable, and are very precise. Consequently, there is interest in improving the currently used approaches to handle expert hesitation more effectively. To fill this gap, we propose the RANking COMparison (RANCOM) method, which can be used to determine criteria weights based on expert knowledge. Its performance is based on defining the criteria ranking order to obtain the weights vector. The illustrative example and a comparative analysis of the selected subjective weighting methods show that the proposed approach provides reliable results, highly correlated with the existing approaches. However, it offers a simple manner of use, adjusted for less and more experienced users. The comparison of the RANCOM and AHP methods shows that when a slight probability of error in expert judgments occurs, the RANCOM method proves to be a more suitable solution to handle the expert inaccuracies for the problems with 5 or more criteria. Performed experiments showed that the RANCOM method is a repeatable and more consistent method regarding possible judgment errors. The obtained results showed that the RANCOM method guarantees more repeatable results than the AHP method, especially for a greater number of criteria. [ABSTRACT FROM AUTHOR]

Sustainability research and practice decisions are the fields that address one of the most important challenges of today's world, which is frugal resource management. More people are adopting ethical, less polluting lifestyles and, thus, are seeking out companies that share their values. Multi-Criteria Decision Analysis (MCDA) is a suitable tool for comprehensively analyzing the available decision variants. However, the currently used frameworks are frequently based on crisp values, so the resulting ranking and ratings can only be regarded as initial recommendations. On the other hand, tools for solutions based on uncertain data are limited in practical usability, scalability, and flexibility for solutions based on uncertain data. In this paper, we propose a new comprehensive framework for sensitivity analysis based on interval arithmetic and the MCDA approach. The primary assessment gives initial recommendations, but only after thorough analysis can the ranking of alternatives be reliably treated. It means that thresholds for each criterion should be obtained, which informs us about the robustness of obtained alternative's order. It is very important information that allows accepting or rejecting the ranking received. The framework's performance is verified in sustainable development, where we evaluate the group of electric cars. The TOPSIS method for crisp and interval numbers is involved as the selected MCDA method. The results show that the proposed framework can be effectively used as a recommendation system for multi-criteria problems where input data can fluctuate. [ABSTRACT FROM AUTHOR]

[Display omitted] • We investigate Multi-Criteria Decision Analysis (MCDA) methods application in the renewable energy sources (RES) evaluation domain. • We propose a new MCDA method called CODAS-COMET. • We assess Green Electricity Generation in Poland using the method developed. The purpose of this paper is to present the new hybrid multi-criteria decision analysis (MCDA) method CODAS-COMET for evaluating sustainable electricity generation considering renewable energy sources (RES). The introduced approach combines the advantages of two known MCDA methods: Characteristic Object METhod (COMET) and COmbinative Distance-based ASsessment (CODAS). Due to the advantages of the COMET method, the proposed approach enables the identification of the whole domain model, is resistant to the rank reversal phenomenon, is easy to apply, and allows to obtain an objective and reliable recommendation based on the gathered data. Meanwhile, combining the COMET method with CODAS eliminates the limitations of COMET by automatizing and accelerating the characteristic object comparison procedure. The presented method's practical application includes evaluating Poland's sixteen voivodeships, considering the sustainable use of RES in the electricity generation domain. An additional contribution of this research is to investigate the impact on results of distance metrics other than the default distance metric used by the method that is the focus of this paper and demonstrate the design and practical application of a research framework for selecting the most appropriate metrics. A comparative analysis of the CODAS-COMET method with the TOPSIS-COMET reference approach was performed to confirm the results' reliability. The results showed a high correlation among all final rankings and confirmed the robustness of the CODAS-COMET approach to changing metrics in the algorithm. Furthermore, the results proved that our approach is a suitable and valuable tool for sustainability assessment in renewable energy domains and other complex issues requiring consideration of multiple criteria. [ABSTRACT FROM AUTHOR]