The increasing complexity of modern decision-making driven by technological advancement and the multifaceted nature of socio-economic and environmental challenges demands robust, transparent, and interpretable multi-criteria decision-making (MCDM) methods. This study evaluates the recently proposed Exhaustive Objective Ranking Solution (EORS), an objective MCDM technique that explores the entire solution space and utilizes kernel density estimation (KDE) to identify the most representative alternative based on preference value distributions. A real-world case study on the optimal site selection for floating photovoltaic (FPV) systems is employed to validate the method’s effectiveness and compare its performance with established objective weighting approaches (standard deviation, entropy, and CRITIC). The analysis focuses on ranking consistency, stability, and correlation across methods. Additionally, a two-stage sensitivity analysis examines the robustness of EORS to variations in the KDE bandwidth parameter and perturbations in the decision matrix. Results show that EORS provides distinct and stable rankings, with enhanced interpretability through confidence measures, making it suitable for high-stakes decision contexts. Future research directions include extending EORS to uncertain environments and hybridizing it with expert-based weighting to improve adaptability and generalizability.

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.

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.

The ever-growing interest in multi-criteria decision-making methods requires research that would compare them with each other as the decision-makers would need help choosing the appropriate method. It is important to highlight the differences between them, because using different methods may result in discrepancies in the obtained rankings. Additionally, it is worthwhile for the research to take into account the problems whose solution is extremely valuable. To this end, we present a comparison of five multi-criteria decision-making methods: TOPSIS, VIKOR, COMET, SPOTIS, and MARCOS in the supplier selection problem. The conducted research showed significant differences in the evaluation of the preferences of the alternatives and some discrepancies in the obtained rankings.

Everyday situations each day more often require us to solve sometimes even complex decisional problems. Such conditions may include the problem of choice when purchasing services, products or devices. The ever-increasing assortment of products and services makes it more and more time-consuming for customers to choose the most suitable product for them. Following their intuition or considering only some selection criteria may result in an unsatisfactory choice. Decision making in multi-criteria selection problems needs the consideration of many factors, which are often conflicting. For this purpose, more people reach for multi-criteria decision methods (MCDM). These methods provide an easy way of solving a given problem under certain circumstances. With the increasing popularity of those methods, more are developed each day. This paper presents an approach based on four MCDM methods applied to solve the problem of choosing the most favourable CPU model available on the market regarding price and performance, among other criteria. For this purpose, COMET, TOPSIS, VIKOR and PROMETHEE II methods were performed. Later, a comparative analysis of the rankings obtained using the applied methods was conducted. Rankings provided by used methods were compared using correlation coefficients. The research showed that choosing a specific method is relevant for the results and that real-life problems can be successfully solved using multi-criteria decision methods. The obtained results confirm the suitability of MCDM methods as a decision support tool for consumers’ product selection. Applications based on MCDM allowing customers to perform multi-criteria analysis of a set of products could contribute to increased consumer satisfaction and growth of popularity and competitiveness of online stores providing them.

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.

The right choice of information system for a company plays a vital role, therefore research to make it easier to choose the right IT system is very important. In the paper, we present research on the identification of a decision model supporting the decision on choosing the right IT system for the company. The study case is based on a literature example, which takes into account five basic criteria that influence the choice of an IT system by an entrepreneur. Then, using the COMET Object Characteristics Method, a model based on 243 characteristic objects is identified. In the following, the values from the decision matrix and the results are compared with each other. An additional model can be used to assess the sensitivity of the received solution. Finally, the most important conclusions and recommendations for further work were formulated.

Multi-Criteria Decision Analysis (MCDA) methods are becoming more popular as the complexity of decisions is rising. As more people are trying to apply such a method, the mistakes are often made and duplicated through numerous use cases. In this paper, the discussion on a given topic is made. We highlight mistakes made on real-life examples and show how to make sure none of them is made in the future. Moreover, the Characteristic Object’s Method (COMET) is shown as an alternative to so popular Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The results were then compared using selected similarity coefficients to emphasize the correlation of the resulting rankings. The research showed that every single step of the decision-making method is needed to be correctly made to make sure that received results are reasonable.