International audience

As cities worldwide promote active transport and reduce car dependency, effective planning of bike-sharing systems has become increasingly important for sustainable urban development. Planning effective locations for bike-sharing stations remains a major challenge for sustainable urban mobility, requiring the integration of conflicting criteria and diverse spatial data. While various approaches have been proposed for planning bike-sharing systems, the integration of multi-criteria reasoning with up-to-date, openly available spatial data remains a challenge that is not yet fully addressed. This paper introduces a novel decision-making framework that systematically evaluates virtual bike-sharing station locations by combining the Stable Preference Ordering Towards Ideal Solution (SPOTIS) and RANking COMparison (RANCOM) methods with OpenStreetMap data. The proposed framework is highly flexible and can be adapted to the specific characteristics and needs of a given city or district, allowing decision-makers to select and adjust evaluation criteria according to local priorities and planning contexts. At the same time, it ensures preference stability, enabling consistent and robust rankings even when the set of evaluated locations is modified. In this paper, the framework is demonstrated using five criteria: accessibility to public transport, university area density, cycling infrastructure, points of interest, and residential density, with input from three experts. To demonstrate the applicability of the proposed framework, an evaluation of ten potential locations was conducted in Szczecin. The analysis took into account the preferences of the decision makers and allowed the identification of locations that scored highly in different weighting scenarios. This approach supports the transformation of complex spatial decision-making into systematic, data-informed recommendations, providing urban planners with a tool that can be adapted to various geographical and planning contexts. Thanks to its flexibility and methodological structure, the framework offers practical support for evidence-based planning of bike-sharing infrastructure, helping to connect spatial analysis with real-world urban decision-making.

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.

Evaluating cities based on cost of living and quality of life can be a complex task without the support of a structured decision-making framework. To address this challenge, various methods from the Multi-Criteria Decision Analysis (MCDA) domain can be utilized to assist in solving such decision problems. In this paper, we propose a novel RANCOM-RAM (RANking COMparison - Root Assessment Method) framework, enhanced with a fuzzy ranking approach, to provide a robust tool for city evaluation in terms of living costs. To demonstrate the framework, we present a case study evaluating selected European Union capitals using criteria such as average prices of everyday goods, rent, and income levels. The results show that, under the identified weights, Luxembourg, Brussels, and Berlin occupy the top three positions in the ranking. However, the fuzzy ranking approach reveals that cities like Sofa or Budapest can achieve high rankings under alternative weighting scenarios, highlighting the flexibility of the proposed method.

Multi-Criteria Decision Analysis (MCDA) methods are widely applied in complex decision-making scenarios involving multiple, often conflicting criteria. While considerable research has focused on their performance under varying numbers of alternatives, the effect of changes in the number and importance of criteria, particularly in rank reversal-resistant methods, remains underexplored. This study investigates the stability of three rank reversal-resistant MCDA methods, namely the Reference Ideal Method (RIM), Stable Preference Ordering Towards Ideal Solution (SPOTIS), and the Characteristic Objects Method (COMET), under systematic changes in criteria inclusion. Through a series of 1,000 simulations with varying numbers of alternatives and criteria, the methods were tested for ranking consistency as criteria were removed in both ascending and descending order of importance. Results show that COMET was the most stable in high-dimensional problems, RIM performed best in mid-sized scenarios, and SPOTIS was more robust when more important criteria were removed. The findings provide valuable insights for selecting appropriate MCDA methods in dynamic decision environments and fulfill the study’s goal of assessing method sensitivity to criteria variation.

This paper investigates the impact of different relationship functions on the performance of the Ranking Comparison (RANCOM) method, a ranking-based approach to calculate criteria weights based on subjective preferences of decision-makers in the Multi-Criteria Decision Analysis (MCDA) field. Three relationship functions (three-value, ratio-based, and logarithmic ratio-based) were evaluated across varying decision contexts using simulated weights generated from Dirichlet distributions with different concentration parameters. The results indicate that no single function performs best in all scenarios. Instead, each excels under specific conditions of weight similarity. The ratio-based function performs best with diverse weights, the logarithmic ratio-based function is superior when weights are similar, and the three-value function remains robust under general conditions. These findings provide valuable guidelines for selecting relationship functions based on expected weight distributions. The study highlights the importance of aligning modeling techniques with decision context and opens avenues for further research into alternative relationship functions and their application in practical decision-making problems.

This paper is about the Compromise Ranking Problem (CRP), a well-known problem in the social choice theory. According to the famous Arrow’s theorem there is no voting method which is entirely satisfying and fairness if one accepts Arrow’s axioms. In this paper we formalize the problem as a minimisation problem in a discrete finite search space. We attempt to solve it based on the Least Squares (LS) approach thanks to some appealing metrics to get the optimal CRP solution. Surprisingly, we show that the optimal consensus (compromise) ranking solution disagrees with the commonsense solutions in four simple interesting examples. The search for an optimal solution in agreement with the commonsense appears to be an open very challenging question and our paper warns the users about the impossibility of the main current methods to provide acceptable solutions even for the rather simple examples considered in this work.

Decision-making involves various aspects of daily life, encompassing routine tasks, complex business strategies, and resource management. With problems often including multiple criteria, detailed analysis becomes imperative to ensure a reliable assessment. Consequently, diverse tools have emerged to aid decision-makers in making well-informed choices, with Multi-Criteria Decision Analysis (MCDA) methods standing out for structured evaluations and adaptable processes. MCDA methods accommodate both crisp and fuzzy data, handling uncertainties inherent in decision problems. However, the abundance of methods poses a challenge in selecting the most suitable approach. Comparative analyses underscore the variability in outcomes, emphasizing the importance of employing multiple techniques for robust decision-making. Within resource management, MCDA methods offer versatile solutions, integrating expert knowledge and fuzzy logic to enhance reliability. As technological advancements introduce novel tools, empirical validation becomes crucial. Fuzzy ranking emerges as a promising approach, offering comprehensive insights into decision stability and robustness. This study applies selected MCDA methods within an Intuitionistic Fuzzy (IF) environment to personnel selection, analyzing multiple criteria with the participation of decision-makers. Through Monte Carlo simulation and fuzzy ranking, the study validates the efficacy of fuzzy ranking in practical multi-criteria decision-making scenarios, offering insights into decision robustness and stability. The proposed research provides a comprehensive approach to personnel selection, producing reliable and robust recommendations under changing input conditions.

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.

As civilization advances, decision-making problems become increasingly complex, posing significant challenges in deriving optimal solutions. The field of multi-criteria decision analysis (MCDA) has evolved to address these complexities; however, single-solution approaches are often inadequate in multi-decision-maker scenarios, necessitating group decision-making methods. This study proposes an alternative compromise solution for evaluating electric car performance, examining various compromise approaches, including voting methods like Borda and Copeland counts, and more advanced techniques such as Iterative Compromise Ranking Analysis (ICRA), Improved Borda, HQ compromise, Dominance-directed graph, and Rank position methods. These methods were assessed using two ranking correlation coefficients to evaluate their effectiveness in multi-decision-maker scenarios. Results indicate that while minor differences in rankings were observed based on the chosen compromise method, these differences could influence decision-making outcomes. The analysis highlights the importance of selecting methods that align with specific needs and expectations. Future research should focus on simulation-based studies to better understand the characteristics of each method and apply these methods to real-world case studies to determine their suitability in various contexts.

Decision-making is a high-risk area and, simultaneously, a process that cannot be ignored. Risks often arise under challenging situations where ranking the options under consideration is a complex problem. For such purposes, multi-criteria decision-making methods are resorted to, which, despite continuous improvement, still have some weaknesses, often due to their characteristics. One such method that has seen many modifications in the literature is the Characteristic Objects METhod (COMET). It allows a simplified comparison of alternatives considered in a given problem, compared to the Analytic Hierarchy Process (AHP), but has a relatively high computational complexity. In this study, we present a modification of the COMET method that simplifies the procedure for assigning preference values to characteristic objects. Furthermore, a comparison with the classical approach is given regarding the obtained preference values, rankings, and criteria weights, which occur implicitly in this method. The study showed that the proposed modification has grounds for broader use and offers similar results to classical COMET while reducing the number of required operations to obtain preference values.

In the area of decision-making, where complexities often exceed experts’ analytical capabilities, this study addresses a critical gap by introducing a novel methodology for sensitivity analysis within the Multi-Criteria Decision Analysis (MCDA) problems. Current analytical frameworks struggle to comprehensively consider potential modifications to values within decision matrices, making it challenging to understand their impact in detail. To address this challenge, decision-makers need enhanced tools, and MCDA methods combined with systematic changes in selected elements of the decision matrix stand out as a promising approach. However, existing studies primarily focus on different criteria-weight scenarios, leaving an unexplored gap in the simultaneous modification of multiple values within the decision matrix. This paper integrates sensitivity analysis within the MCDA methods, extending its role in the comprehensive assessment of decision problems. Sensitivity analysis becomes crucial in offering decision-makers a broader perspective, aiding them in navigating the complexities of decision-making in dynamic environments. Recognizing the unexplored potential in sensitivity analysis, the study proposed a novel approach of simultaneous modification of multiple values in a decision matrix, offering an extension of conventional one-at-a-time modifications. As a Proof of Concept (PoC) research work, the study investigated whether the determined approach provides divergent preference scores compared to the traditional single-modification method across ten different MCDA techniques. The results showed that modifying multiple values simultaneously produced different preference scores of alternatives than in the case of a conventional single change, showing that additional insight knowledge could be extracted from this type of sensitivity analysis.

This paper provides a comparative analysis of three selected Multi-Criteria Decision Analysis (MCDA) methods in a practical decision problem to examine the robustness of the obtained results. The Complex Proportional Assessment (COPRAS), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMEHTEE) II, and Evaluation based on Distance from Average Solution (EDAS) methods were utilized for the evaluation purposes, while the Ranking Comparison (RANCOM) method was applied to determine criteria weights. The application was directed toward sustainable development management in selected European countries considering different energy sources in the consumption share per capita. The obtained results showed, that depending on the used MCDA method, the ranking of countries differed, thus showing that the evaluation process is sensitive to changing assessment methods.

This paper presents a comparative analysis of selected distance metrics within the intuitionistic fuzzy environment in Multi-Criteria Decision Analysis (MCDA). The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, combined with intuitionistic fuzzy sets (IFS), was employed to investigate how different distance metrics influence decision-making outcomes. The study examines the sensitivity of the obtained recommendations to the variation in the calculation formula used to determine the distance between two IFS. The practical problem of selecting a site for a large-scale rooftop photovoltaic project served as the case study for this comparison. The results indicated that using different distance metrics within the IF-TOPSIS method led to varying preference score ranges and differing rankings, particularly in the middle portions of the rankings. The findings underscore the sensitivity of MCDA methods to the choice of distance metrics used in the calculation process.

In today's world, addressing multi-criteria decision problems presents a considerable challenge due to their inherent complexity. Decision-makers face diverse and often conflicting factors that exceed their analytical capabilities and complicate the identification of the best solutions. Within this intricate field, Multi-Criteria Decision Analysis (MCDA) serves as an important decision support tool. The complex nature of these problems introduces numerous variables that influence the final choice. This complexity requires sensitivity analysis as an indispensable part of the assessment process. It allows for an examination of how changes in input data can affect the outcomes, providing valuable insights into the stability and reliability of the decision-making process. Based on the state-of-the-art, this study identifies a gap in sensitivity analysis, particularly in assessing the impact of modifications to the decision matrix. Although one-at-a-time (OAT) modification is efficient, simultaneously changing multiple elements provides nuanced insights into decision-matrix interdependencies. Therefore, this research introduces a novel sensitivity analysis method, the COMprehensive Sensitivity Analysis Method (COMSAM), which systematically modifies multiple values within the decision matrix. The COMSAM allows for a detailed problem space exploration, providing insights into alterations across criteria values. Furthermore, the method represents the preferences obtained from the evaluations as interval numbers, offering decision makers additional knowledge about the uncertainty of the analyzed problem. This study advances the field of sensitivity analysis, providing a new perspective on changes in multiple values' simultaneous influence on the robustness of the MCDA result.

In this paper, we propose a novel approach to distance measurement for rankings, introducing a new metric that exhibits exceptional properties.Our proposed distance metric is defined within the interval of 0 to 1, ensuring a compact and standardized representation.Importantly, we demonstrate that this distance metric satisfies all the essential criteria to be classified as a true metric.By adhering to properties such as non-negativity, identity of indiscernibles, symmetry, and the crucial triangle inequality, our proposed distance metric provides a robust and reliable approach for comparing rankings in a rigorous and mathematically sound manner.Finally, we compare our new metric with distances such as Hamming distance, Canberra distance, Bray-Curtis distance, Euclidean distance, Manhattan distance, and Chebyshev distance.By conducting simple experiments, we assess the performance and advantages of our proposed metric in comparison to these established distance measures.Through these comparisons, we demonstrate the superior properties and capabilities of our new drastic weighted similarity distance for accurately capturing the dissimilarities and similarities between rankings in the decision-making domain.

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.

Dealing with uncertainty is a significant direction of contemporary research on decision support systems. Despite many sophisticated methods of dealing with uncertain data, such as fuzzy sets and their numerous generalizations, methods based on interval numbers are still very popular. This is because interval data are much less complex and easier to process, and they are also easier to determine. However, there is a research gap regarding whether such a simple approach can adequately model uncertainty. This paper proposes a new approach to handling interval data in the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on the cartesian product of boundaries. This approach is compared with the most popular extension proposed by Jahanshahloo. In this work, two study cases taken from the literature were analyzed. Finally, the results of both approaches were compared, and conclusions were drawn.

As a new conception of IFS (Intuitionistic Fuzzy Sets), Pythagorean Fuzzy Sets can manage conflicted details more fexibly in decision-making. This extension has been used repeatedly for decision making. This is an important trend in decision making that is worth further study in combination with the study of entropy properties. This paper proposes a new entropy for the Pythagorean Fuzzy Sets and an application measure using the TOPSIS method. The new entropy approach was used to estimate the objective weights in the decision-making procedure. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique is presumed that the optimum alternative has the least distance from the positive and farthest from the negative ideal solution. This approach has been widely adopted to solve MCDM issues in multiple fields. We present a simple study case where we use TOPSIS and new entropy to solve the group decision-making problem.

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.

Chess is a very demanding sport as it requires advanced planning and strategic thinking skills. The degree of difficulty of the game also depends on the time allotted for a game, which can range from a few minutes to several tens of minutes. For this reason, the games are divided into several categories: standard, blitz, and bullet. However, as many chess players specialize in only some of the categories, it is difficult to determine the best chess player. It is very important to keep a proper ranking of the players. One way to recognize their achievements is the FIDE (Fédération Internationale des Échecs) titles awarded to the best players. However, there is still the problem of how to determine the best among the Grandmasters. There are many very talented players competing in chess. Creating a single ranking for all types of chess, regardless of the time allotted for the game, is a difficult challenge, as many undeniably outstanding chess players do not specialize in all types. Creating a ranking for only one type would not accurately describe the level of players. Therefore, a ranking was created based on all of them using the COMET method, which belongs to the multi-criteria decision-making methods (MCDA). It is based on fuzzy logic and uses characteristic objects for the assessment of alternatives, which guarantees immunity to the paradox of reversal rankings. Expert opinion was used for correct evaluation. This article presents the ranking of chess players regardless of the type of game they specialize in, to prove that it should be possible to identify the single best chess player.

The alternative sources of energy have significantly improved quality and are more prevalent in recent years. Solar panels as one of these energy sources continue to be chosen for commercial and private usage. One of the more complex decisions is to choose components to installed in the solar system. All of the public available solar panels have eight important characteristics related to electricity efficiency and dimensions. However, there are many possible alternatives, so it is hard to say which one will be the most proper choice. MCDA methods can make this complex decision problem. This work presents an assessment of solar panels obtained by two popular MCDA methods, i.e., the COMET and TOPSIS techniques. Both methods are distance-based MCDA methods which are using characteristic points idea. However, the COMET method is the rank reversal phenomenon free method and return more accurate results. Next, we present a comparison to find the most rational solar panel from the defined set of alternatives considered their criteria. For this purpose, the weights vector is determined by using three methods. Therefore, results are determined based not only on the used MCDA method but also on the applied weights of the criteria.

Multi-criteria decision-making methods aim to solve complex problems under many factors. These methods each day are more frequently applied and improved. Many researchers try to find a reliable solution for a specific problem or group of problems. More and more often, new methods are created that certain target requirements. That is why solving the specific problem can be executed on multiple methods. In this paper, we provide three new solutions for the problem presented in the referenced paper. For this purpose, the Characteristic Object’s Method (COMET), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and the Stable Preference Ordering Towards Ideal Solution (SPOTIS) method were used. The results were then compared using selected similarity coefficients to emphasize the correlation of the resulting rankings. The research showed that choosing a method will have an impact on results and should be considered with caution.

Multi-criteria decision-methods (MCDA) has a broad field of applications in various areas, such as engineering, logistic, health management, and others. Their main objective is to rank the decision variants, from the best to the worst. It is always essential to get a reliable solution to a decision problem. However, rankings obtained with different MCDA methods are often different, and that is why an interesting research gap is related to measuring the accuracy and reliability of MCDA methods. In this paper, we examine how much different can be rankings obtained using TOPSIS and VIKOR methods. For this purpose, we apply classical versions of TOPSIS and VIKOR methods to randomly generated decision matrices with different criteria and alternatives. Then, we compare the obtained ranking using three ranking similarity coefficients. The comparison results of conducted simulations are represented as boxplots, which contain the primary distribution of similarity of results calculated using the tested methods. The work is finished with a discussion and conclusions on the results in different classes of problems.

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.

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.

In multi-criteria decision support methods a significant participation of the decision maker is required. The obtained assessment is subjective, and the decision-making process is usually difficult to automate. Limiting interaction with the decision-maker, while maintaining the characteristics of multi-criteria analysis seems to be a desirable direction of methodological research. One of the solutions going in this direction is the original multi-criteria PVM method. The applicability of the method has been verified in the context of research on the assessment of EU countries in terms of ensuring healthy living conditions.

Multi-criteria decision-analysis (MCDA) is a popular field of study with implementations in various areas, such as supply chain management, logistics, engineering, health, sustainable development, to name a few. The normalization of the data considered in such challenges (decision matrix) is an essential step of many MCDA methods. Different normalization techniques can influence the results obtained, and hence, generate different final rankings. In this paper, the investigation of normalization effects is conducted in the PROMETHEE II method. Five different normalization methods were used and additionally, the case without normalization was examined. In order to compare the influence of various normalization methods, an empirical study on the selection of the location of the airport was analyzed, with ten alternatives and seven criteria. The outcomes of investigation of normalization effects in regard to the PROMETHEE II method prove that they do have an impact on the final rankings.

Multi-criteria decision-analysis (MCDA) methods have been widely applied by many researchers in various fields of study. One of the numerous MCDA methods, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under fuzzy environment, namely fuzzy TOPSIS, has been successfully applied in many practical, real-world challenges. This paper provides a short review of fuzzy TOPSIS applications. The research is based on 25 studies conducted in the years 2009 - 2018. Most relevant and most cited papers concerned with fuzzy TOPSIS technique were analyzed and categorized into application areas, such as supply chain, environment, energy sources, business, healthcare. Fuzzy TOPSIS implementations are examined and compared by approaches used, such as fuzzy sets, hesitant fuzzy sets or intuitionistic fuzzy sets, by other methods combined with fuzzy TOPSIS, such as fuzzy Analytic Hierarchic Process (AHP) or enhancements for group decision-making and by a number of alternatives and criteria used. Finally, insights into ongoing trends, most popular approaches, and directions of study concerning the fuzzy TOPSIS method are presented.

Many people, organizations and managers try to assess the performance of the football teams and seek new improved approaches. Rankings are an unavoidable part of the football world, where high competitiveness and efficiency is a natural factor. One of the techniques to deal with such problems is the use of multi-criteria decision-analysis methods (MCDA), offering an effective approach to not only ranking but also assessing, selecting and sorting given alternatives. In this paper, the Characteristic Objects METhod (COMET) is used in order to create a performance ranking of the top-tier football clubs. The assessment model is identified and compared with the existing benchmark ranking by the Spearman’s rank correlation coefficient.

This paper investigates the problem of sustainable city transport. The significance of this problem is increasing, considering the inevitable increase of air pollution levels in cities. Currently, cars with diesel engines are not admitted to many city centers, and this trend can be expected to be maintained. Moreover, this restriction will be expanded to all combustion cars in the foreseeable future. Therefore, combustion vehicles will eventually be replaced by electric ones. In this paper, the authors use the Characteristic Objects METhod (COMET) to identify a decision-making model for the selection of the best model of car for sustainable city transport. The COMET method is a new approach that is free of the rank reversal phenomenon. The identified model provides information about the significance level of each of the criteria. The presented approach can easily be expanded by using a greater number of criteria and alternatives, depending on the specification of the considered problem. Moreover, the interval numbers concept is proposed as the basic approach to dealing with input data uncertainty by using the COMET method. The presented technique is an efficient and highly accurate approach to make decisions based on experts’ experience.

This paper investigates the problem of the sustainable ammonium nitrate transport.The significance of this problem is increasing, considered the occurrence of the worldwide agricultural production boost.The existing international regulations for the transport of the dangerous chemical substances are not sufficient to obtain a satisfactory solution for the sustainable transport.The main reason for that is the fact that the safety criteria can easily become dominated by the economic factors.In this paper, the authors use the COMET method to identify a decision making model for the selection of the best scenario of sustainable transport.The COMET method is a new multicriteria decision-making technique that is free of the rank reversal phenomenon.The identified model provides information about the global and local significance level of each of the criteria.The proposed approach can be easily expanded by using a greater number of criteria, depending on the particular problem analyzed.The proposed methodology is an efficient and highly accurate solution to make decisions based on experts' knowledge.

The Analytic Network Process (ANP) is a subject of interest of the authors, which is included into methods of multi-criteria decision-making approach. The article covers the issue of supporting decisions involving experts. A network model structure was proposed, where the assessment of the decision variants on the set of criteria depends not only on the experts’ opinion, but also on the level of their competence. The possibility of taking into account the preferences for the criteria set by: a decision maker, experts or beneficiaries representing the decision-making options was also considered. The model’s performance was verified on the example of employee assessment. The results of the experiment have confirmed that the decision model developed can be formally represented in the computer decision support systems and delivered to the users in the form of a ready-made proposal (pattern).

There are billions of images available on the Internet. With such a rich collection available and such a great diversity of users and their various needs, content-based image retrieval becomes a problem. Luckily, this is partially solved by publicly available image search engines; however, a question what their linguistic quality is arises. An approach to evaluate linguistic query based quality of image search engines is proposed in this paper and a sample test set and queries are presented.

The design of web applications includes the use of components that attempt to increase influence on target audience to achieve expected results in the form of interactions. In this area, conflicts between user experience and business goals are observed, especially when primary tasks within a website are negatively affected by intrusive marketing messages. The proposed in this paper approach based on perceptual experiments is related to modeling the structure of interactive objects and building an inference model, with the main purpose being to determine the response from web users using different levels of influence. The obtained results, compared with online responses from an actual campaign, provide the possibility to adopt compromise solutions through designing Internet applications with an expected outcome and minimizing negative responses without interfering with user experience.