Multi-criteria Decision-making Problem Research Articles

Group decision-making with hesitant fuzzy linguistic preference relations in view of worst and average indexes

To address the situation where Multi-criteria group decision-making (MCGDM) problems with hesitant fuzzy linguistic preference relations (HFLPRs), this study introduces a group decision-making (GDM) method in view of the worst additive consistency index (WACI) and average additive consistency index (AACI) simultaneously. First, several optimization models are constructed for deriving the WACI and AACI. The main characteristic of the constructed optimization models is that it takes into accounted the personalized individual semantics (PISs). And then the concept of acceptable additive consistent HFLPRs is developed based on the proposed WACI and AACI. Second, several optimization models are constructed for improving the consistency of HFLPRs. Two predefined thresholds for the WACI and AACI are considered in the proposed optimization models. It requires the consistency levels of all the linguistic preference relations (LPRs) associated with HFLPR meet the threshold of WACI, and the average consistency level of all LPRs reaches the threshold of AACI. Third, an algorithm is designed for deriving priority weights from acceptable consistent HFLPRs. Finally, the presented models are validated using a numerical example and extensive comparative analyses.

Health Insurance Provider Selection Through Novel Correlation Measure of Neutrosophic Sets Using TOPSIS

Risk monitoring aims to recognize and control potential threats to our assets or health activities. Insurance is vital, as it compensates for any unexpected loss of property or life. Clients selecting the best health insurance provider must consider various factors and their relative significance to their circumstances. Multi-criteria decision-making (MCDM) helps people analyze and compare policy alternatives to find the best option. This research aims to assist potential health policy purchasers by addressing the selection of health insurance providers as an MCDM problem. The correlation coefficient is useful for identifying the importance of several conflicting criteria. The idea of correlation coefficients is extended in a neutrosophic context to capture the indeterminacy and incomplete information in the relationship among the criteria. The technique for order preference by similarity to an ideal solution (TOPSIS) approach is a useful and straightforward approach to solving MCDM problems. However, it often became ambiguous to researchers due to its involvement in the distance measure technique. The proposed neutrosophic correlation measure may also replace the ambiguity of using a suitable distance measure in the TOPSIS approach. This study extends the TOPSIS method by using the proposed neutrosophic correlation coefficient on single-valued neutrosophic sets (SVNSs). The criteria preferences are computed using a method based on the removal effects of the criteria (MEREC) approach. Some valuable concepts, like the weighted closeness measure of type I and type II and the weighted index parameter, are introduced with their properties to establish the proposed neutrosophic TOPSIS approach. An MCDM approach for health insurance providers has been constructed to illustrate the proposed approach numerically. The proposed method suggest that the health insurance provider ϒ2 is the most beneficial alternative, whereas ϒ1 is the least suitable. The client considers the terms and conditions for non-coverage and the facilities provided for pediatric and maternity care while buying health insurance. The comparative analysis of the suggested technique demonstrates the merit of the research in terms of consistency. The sensitivity analysis demonstrates the flexibility and robustness of the obtained results.

Optimisation problem of China’s supply chain transportation issues in European logistics

The paper highlights the importance and validity of the research problem: the major consequence for logistics arising from China’s logistics market due to its effective short-term and long-term strategies and developing transportation wholesale. The presented viewpoint helps to clearly understand the international perspective of the vastly enlarging China’s supply chain market due to its strong links with logistics centres. In recent years, much scientific research and studies have been conducted in China and Europe regarding China’s transport evolution era, from the production stage to the physical distribution stage, involving multiple steps until loads are in customers’ hands. The article considers the optimisation problem of a supply chain with multiple periods and diverse means of transportation. The considered problem can be formulated as a dynamic multi-criteria decision-making problem, in which the criteria are minimising the total cost, minimising the carbon footprint, and minimising the average transporting time.

A new virtual water qualimeter with a changeable broken linear pointer based on a quadrant circular constellation graph

ABSTRACT As water is a very important natural resource for humans and its quality is closely related to human health, water quality evaluation (WQE) is very important for the protection of human health, livelihoods, environment, and management of water. Although various WQE methods have been developed and used, a common drawback is that the methods lack visualization of the WQE results and require additional complicated graphical techniques. The Pointer meter is an analogue measuring device with a pointer indicating the measured value with its location. A constellation graph (CG) enables to map multi-dimensional data on a two-dimensional plane. By introducing the pointer meter and CG for WQE, this paper proposes a new visualized pointer meter-type virtual water qualimeter (VWQ-meter) based on a quadrant circular constellation graph (QCCG). It has a changeable broken linear pointer with variable lengths and shapes, while the traditional pointer meters have straight-line pointers with a fixed length and a straight-line shape. It evaluates the water quality not only quantitatively but also intuitively as in the pointer meters. To demonstrate its effectiveness, it is applied to one example of WQE. It could be widely applied to not only WQE but also various multi-criteria decision-making (MCDM) problems arising in water pollution evaluation, water resource management, water supply, and wastewater treatment.

Site Selection Decision-Making for Offshore Wind-to-Hydrogen Production Bases Based on the Two-Dimensional Linguistic Cloud Model

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics.

Similarity Measures of Probabilistic Interval Preference Ordering Sets and Their Applications in Decision-Making

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics.

Development of the rough Defining Interrelationships Between Ranked criteria II method and its application in the MCDM model

Although numerous methods of multi-criteria decision-making have been developed so far, there are still those that have not been improved by different theories that deal well with uncertainties and inaccuracies that are a normal occurrence in everyday life. The goal of this research is the development of the Rough Defining Interrelationships Between Ranked Criteria II (Rough DIBR II) method and the presentation of its application in the multi-criteria decision-making (MCDM) problem, specifically in human resource management (HRM). The paper presents the subject-developed method and its practical application in the MCDM model with the Rough Additive Ratio Assessment (Rough ARAS) method. In order to check the consistency and validity of the proposed methodology, a sensitivity analysis and a comparative analysis were performed. The conclusions of this research indicate a stable and valid proposed methodology, as well as the possible application of the Rough DIBR II method for defining weighting coefficients of criteria in real-life decision-making problems.

Empowering decentralized identity systems for Web 3.0 in complex spherical fuzzy knowledge

The Web 3.0 network system, the next generation of the world wide web, incorporates new technologies and algorithms to enhance accessibility, decentralization, and security, mimicking human comprehension and enabling more personalized user interactions. The key component of this environment is decentralized identity management (DIM), embracing an identity and access management strategy that empowers computing devices and individuals to manage their digital personas. Aggregation operators (AOs) are valuable techniques that facilitate combining and summarizing a finite set of imprecise data. It is imperative to employ such operators to effectively address multicriteria decision-making (MCDM) issues. Yager operators have a significant extent of adaptability in managing operational environments and exhibit excellent effectiveness in addressing decision-making (DM) uncertainties. The complex spherical fuzzy (CSF) model is more effective in capturing and reflecting the known unpredictability in a DM application. This research endeavors to enhance the DM scenario of the Web 3.0 environment using Yager aggregation operators within the CSF environment. We present two innovative aggregation operators, namely complex spherical fuzzy Yager-ordered weighted averaging (CSFYOWA) and complex spherical fuzzy Yager-ordered weighted geometric (CSFYOWG) operators. We elucidate some structural characteristics of these operators and come up with an updated score function to rectify the drawbacks of the existing score function in the CSF framework. By utilizing newly proposed operators under CSF knowledge, we develop an algorithm for MCDM problems. In addition, we adeptly employ these strategies to handle the MCDM scenario, aiming to identify the optimal approach for ensuring the privacy of digital identity or data in the evolving landscape of the Web 3.0 era. Moreover, we undertake a comparative study to highlight the veracity and proficiency of the proposed techniques compared to the previously designed approaches.

Auditing road safety achievement using MEREC–ARAS–QBKM model: an empirical study for APEC member economies

Progress monitoring and action recalibrations are advocated as promising methods for improving road safety, which significantly relates to economic stability and social development. To achieve this, an auditing framework that can evaluate road safety and aid in policymaking is urgently required. To this end, this study developed a systemic decision model that integrates the method based on the removal effects of criteria (MEREC), additive ratio assessment (ARAS), and quantile-based k-means clustering (QBKM), termed MEREC–ARAS–QBKM, with the aim of auditing road safety achievements and providing corresponding policy suggestions with substantial reliability. In particular, the performance of the traditional k-means clustering model was improved by implanting quantiles to determine the initial clustering, which overcomes the uncertainty of k-means clustering owing to the variety of initial cluster centers. Multiple comparisons of empirical results based on a case study of the Asia-Pacific Economic Cooperation (APEC) member economies verified the robustness of the proposed model, demonstrating its applicability, practicability, and reliability in handling real-world multi-criteria decision-making problems in the field of road safety. The empirical findings show that road safety developments among the APEC countries are of class differentiation, suggesting an urgent regional benchmarking. Overall, the proposed methodology empowers decision-makers and policymakers in APEC to swiftly formulate effective action plans, countermeasures, and investment schemes, ultimately contributing to the enhancement of road safety performance and socio-economic benefit across APEC members.

Generalized picture fuzzy Frank aggregation operators and their applications

Picture fuzzy sets with four-dimensional features are widely used in decision-making as a mathematical tool because they can capture the uncertainty of data. However, the methods and techniques based on matrix theory are difficult to solve the decision problem involving high-dimensional data in a picture fuzzy setting. Therefore, operators that can identify high-dimensional data in a picture fuzzy environment are proposed to address this challenge. In this paper, firstly, by integrating the Frank operators into the picture fuzzy tensor, the generalized picture fuzzy Frank weighted arithmetic (GPFFWA) and generalized picture fuzzy Frank weighted geometric (GPFFWG) operators are defined. Their specific expressions are discussed, and the idempotency, order-preservation, boundedness, and commutativity of the proposed operators are also given. Then, combining the GPFFWA and GPFFWG operators, an algorithm is designed to solve the multi-criteria decision-making problem with high-dimensional data features in the picture fuzzy environment. Finally, a numerical example and related analysis demonstrate the effectiveness, superiority, and flexibility of the suggested technique. This work provides new theoretical and methodological support for developing and practicing the decision-making discipline.

Argument-based Cooperative Decision-Making for Autonomous Vehicles

In this paper, we focus on multi-agent multi-criteria decision problems in dynamic environments where autonomous vehicles need to cooperate to make decisions. When a group of agents faces these problems, each agent may have a different preference about which criteria are more relevant. Hence, agents’ preference rules will be used to generate arguments that represent all the agents’ points of view. Our approach performs an argumentation-based analysis to entail the best alternatives, and explains how it arrives at its conclusions. We provide various scenarios to show how our proposal behaves in different situations. We formally show some results about how our current proposal behaves in dynamic environments regarding the computation of the solution of the problem.

Selection of Military Armored Vehicle Using Fuzzy EDAS method

Military armored vehicles are specially designed and equipped for defense and security activities. During military operations, the right vehicle selection is of vital importance for the success of the operation. This problem examined 5 different alternative military armored vehicles for operational and logistic success using the criteria Engine Power, Dimensions, Fire Power Range, Gradeability, and Armor Thickness. In military decision problems, some alternatives probably need to be evaluated regarding more than one conflicting criterion. The decision among these alternatives will usually involve other strategic-level decisions. Multi-criteria decision-making (MCDM) refers to making decisions in such a situation. Are there any methods and techniques available to solve MCDM problems? The evaluation according to distance from average solution (EDAS) method is a multi-criteria decision-making method whose effectiveness has been tested in other problem areas in the literature. In this study, we use the EDAS method to address the problem of military armored vehicle selection in a fuzzy environment. While a Type-1 fuzzy set is used in the fuzzy EDAS method, trapezoidal fuzzy numbers are preferred for military expert evaluation. The results of this study show that the fuzzy EDAS method is effective.

New covering techniques and applications utilizing multigranulation fuzzy rough sets

In order to conduct an in-depth study of Zhan’s methodology pertaining to the covering of multigranulation fuzzy rough sets (CMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {C}_{{MG}}$$\\end{document}FRSs), we build two families: the family of fuzzy β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-minimum descriptions and the family of β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-maximum descriptions. Subsequently, utilizing these notions, we proceed to develop two variations of covering via optimistic (pessimistic) multigranuation rough set samples (CO(P)MG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO(P)}_{{MG}}$$\\end{document}FRS). The axiomatic properties are examined. In this study, we examine four models of covering using variable precision multigranulation fuzzy rough sets (CVPMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CVP}_{{MG}}$$\\end{document}FRSs). We proceed with analyzing the features of these models. Interconnections between these planned plans are also elucidated. This study explores algorithms that aim to identify innovative strategies for addressing multiattribute group decision-making problems (MAGDM) and multicriteria group decision-making problems (MCGDM). The test examples have been elucidated to provide an inclusive grasp of the efficacy of the offered samples. Ultimately, the distinctions between our methodologies and the preexisting research have been demonstrated.

Ranking and Challenges of Supply Chain Companies Using MCDM Methodology

Background: Supply chain companies have merits and demerits regarding operational and economic transactional policies. The effectiveness of supply chain companies corresponds to a cumulative score on a multi-criteria and perspectives-based evaluation. In this paper, we analyse the performances and challenges of several celebrated e-commerce companies to perceive their overall impression of supply chain management. Method: A mathematical model is framed as a multi-criteria decision-making (MCDM) problem with challenges as criteria and companies as alternatives. The criteria importance through inter-criteria correlation (CRITIC) method is used in this paper to adjust weights representing the available data. The ranking of e-commerce companies is evaluated using multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method. Results: This model investigates the most dependent criteria and sub-criteria for the adaptation challenges of supply chain companies (SCCs). Furthermore, the SCCs are prioritized based on various conflicting criteria. Conclusion: Various challenges of SCCs, like logistics constraints, disruptions in supply chains, issues with technology, ethical sourcing and inconsistency between the products’ availability and the pace of consumption, are considered and analysed. We amassed the difficulties as criteria and sub-criteria in a numerical process using the MCDM approach. Additionally, the sensitivity and comparative of several optimal phenomena are analysed based on distinctive combinations of challenges in the ranking arena.

Authentication Methods Selection in Information Security through Hybrid AHP and EGT

The information security leader frequently encounters the challenge of choosing the appropriate defence strategy. Effective multi-criteria decision-making (MCDM) is essential in the field of information security for determining the optimal strategies that involve more than one party. To address this challenge, we propose a hybrid model that combines the strengths of the Analytic Hierarchy Process (AHP) with Evolutionary Game Theory (EGT). The hybrid model helps the information security leader assess the criteria for security controls and make the optimal decisions to protect the organization's data. Initially, the AHP is utilized to assess the criteria of information security control. Subsequently, the priority of the alternatives is established through evaluating these criteria. Furthermore, we will construct a defence-attack circumstance using the EGT framework, which involves formulating strategies and determining payoffs for both the information security leaders and attackers involved. We utilize the replicator dynamic to examine the process of evolution in the game, resulting in the determination of the optimal strategy. A case study is conducted to determine the optimal strategy for information security leaders and attackers. The result indicates that the best defence strategy is password protection, followed by token-based and biometric-based protections. On the other hand, the optimal strategy for attackers is no attack, followed by attack and moderate attack. This study contributes to the multi-criteria decision-making (MCDM) problem’s solving by considering the dynamic aspect between both defender and attacker in the context of information security.

New robust multi-criteria decision-making framework for thermal insulation of buildings under conflicting stakeholder interests

The choice of thermal insulation technology for existing building retrofit can be a complex multi-criteria decision-making (MCDM) problem characterized by multiple stakeholders with conflicting interests. The conflicting interests of the building owners and the policymakers could negatively influence the adoption of low-carbon energy retrofit measures by building possessors, slowing down the progress toward sustainable development. The investigation of this issue is, therefore, crucial to support more informed decisions and offer policymakers useful insights to develop future economic incentives for energy refurbishment of buildings. Most of the existing MCDM studies on the subject do not emphasize the differences among different stakeholders nor consider the outcomes' robustness. To overcome this gap, this paper integrates multi-stakeholder analyses and robustness assessments to provide broad-spectrum and reliable results. An innovative robust MCDM framework for building thermal insulation under conflicting stakeholder interests is proposed and tested on a real case study building (located in Southern Italy) using building dynamic energy simulation. Several alternatives of thermal insulation are evaluated under environmental, energy, and economic key performance indicators (KPIs). The Analytic Hierarchy Process is used to define criteria weights for conflicting decision-makers representing collectivity (policymakers) and private interests. TOPSIS, VIKOR, WASPAS, and MULTIMOORA methods are integrated to perform initial rankings of the alternatives. A rank similarity analysis is performed to evaluate the consensus between MCDM methods, and an ensemble ranking is obtained for each decision-maker using an approach based on the half-quadratic theory. A Confidence Index and a Trust Level are used to evaluate the agreement among the MCDM approaches, verifying the reliability of the final ensemble ranking. The robustness of the framework is attained by complying with well-established literature guidance. The hybrid MCDM analysis showed that porous materials like expanded clay and expanded perlite lead to better results under the KPIs investigated. The worst performances are attained by vacuum insulation panels and aerogel, mainly due to high values of embodied CO2 emissions and long payback periods. Insights upon the performance of different thermal insulating alternatives are also highlighted by the study and a stakeholder comparative analysis demonstrates that global rankings obtained for the different decision-makers show some similarities, but also important deviations, and a full compromise solution is not achieved.

Archimedean [formula omitted]-norm and [formula omitted]-conorm coupled q-rung orthopair fuzzy TOPSIS method for unknown criteria weighting information

To evaluate some real-life multi-criteria decision-making (MCDM) problems, the decision-makers (DMs) need an interactive and flexible MCDM approach that works well for extended decision-making space and has the ability to apply any suitable operational laws to fuse the information under incomplete criteria weighting information. The objective of this study is to propose a multi-criteria group decision-making (MCGDM) approach to evaluate any real-life MCDM problem under incomplete criteria weighting information. To achieve the objective, the paper proposes an integrated MCGDM approach in which the q-rung orthopair fuzzy set (q-ROFS) is used to provide the extended decision-making space to the DMs, a transformative approach is applied to find the DMs’ weights, the maximizing deviation method is implemented to find the criteria weights, Archimedean t-norm and t-conorm (ATU)-based operational laws are adopted to fuse the information, and the TOPSIS method is applied to rank the alternatives. The proposed integrated MCGDM approach has been applied to a real-life MCDM problem associated with the evaluation of different strategies implemented by governments to contain the COVID-19 pandemic. A sensitivity analysis is conducted to examine the impact of changes in the parameters involved in the proposed MCGDM approach on the results. A comparative analysis is also performed with some existing classical and aggregation operator-based MCDM methods to demonstrate the consistency of the proposed approach. Findings suggest that the proposed MCGDM approach is general and flexible enough to evaluate any real-life MCDM problem under unknown criteria weighting conditions.

Analyzing risk factors of tuberculosis using type-2 interval-valued trapezoidal fuzzy numbers with Einstein aggregation operators extended to MCDM

The principal motive of this work is to evolve and initiate an extension from interval-valued fuzzy sets to type-2 interval-valued fuzzy sets (T2IVFS) related to weighted aggregation functions containing the Einstein operator. The chief reason for this extension is that the constancy of the terms can also be taken into data during the aggregation operation. The main goal of this article is to compose the aggregation operators and their characteristics such as the Type-2 interval-valued fuzzy Einstein weighted arithmetic aggregating operator (T2IVFEWA), Type-2 interval-valued fuzzy Einstein weighted geometric aggregating operator (T2IVFEWG), and the characteristics are expressed. At last, to intimate the effectiveness of the suggested approach and explicate the purpose of these operators, a hybrid multi-criteria decision-making problem (MCDM) to select the best risk factor for Tuberculosis (TB) is considered and the result is compared with the outcome of the existing operators and methods. Additionally, a sensitivity analysis was conducted to verify the robustness of the proposed decision-making process.

Application of the VIKOR Method for Selecting the Purpose of Recultivated Terrain after the End of Coal Mining

Surface mining of coal has a negative impact on the environment that needs to be prevented, reduced, controlled, monitored by applying the management of technological processes of technical and biological recultivation after the end of exploitation works. The paper deals with the problem of multi-criteria decision-making in the process of selection of indicators that have an impact on the choice of purpose of recultivated terrain after the end of coal mining. The considered reclamation solutions represent possible alternatives. Degraded areas need to be returned to their original purpose, they should be in the function of environmental protection of the natural environment and settlements near this location. In addition, various criteria and sub-criteria that affect the choice of the most favourable solution were defined and analyzed. The final decision on land use after exploitation was made based on mathematical calculations using the multi-criteria VIKOR method.

Analysis of Digital Skills in European Union Countries with IQRBOW-Based Multi-Moora Approach

This study aims to examine the digitalization levels of European countries from the perspective of individuals' digital skills. It aims to fill a gap in the literature by emphasizing the importance of the human element, which is the main component in digitalization processes. Based on the Digital Skills Indicator data published by Eurostat, components such as ICT users, ICT specialists and ICT training included in the analysis. IQRBOW-based Multi-MOORA approach is applied in the study. This unique methodology has increased the validity of the analysis results by providing more economical and reliable weighting in multi-criteria decision-making problems. Based on 28 sub-criteria, a comprehensive assessment was carried out and 36 countries were compared. The findings of the analysis revealed that Finland ranked highest in terms of digital skills, while Romania ranked last. The results allow for a review of the European Union's digitalization strategies and recommendations for policymakers.