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This paper proposes a rectified complex spherical fuzzy set (rCTSFS) model that enables the phase term of a complex number to function truthfully to its inherent meaning of representing directions, phases, or color hues. In addition, this paper proposes the score and accuracy functions for rectified complex spherical fuzzy numbers (rCTSFn), which allows the three types of membership degrees of an rCTSFn to fulfill human judgment/intuition. The proposed rCTSFS model proves to be a productive extension of the complex spherical fuzzy set (CSFS), complex fuzzy set (CFS), and spherical fuzzy set (SFS) models. On the other hand, Dombi t -norms prove more flexible and comprehensive than some of the other families of triangular norms, such as the algebraic t -norms and the Einstein t -norms, due to the presence of a parameter γ. The parameter γ determines the amount of aggressiveness at estimating the maximum and the minimum of a population based on a sample obtained. Therefore, this paper proposes two Dombi-Choquet integral operators, namely, the rectified complex t -spherical fuzzy arithmetic Dombi-Choquet integral (r C T S F A γ , w λ ) operator and the rectified complex t -spherical fuzzy geometric Dombi-Choquet integral (r C T S F G γ , w λ ) operator. A multi-criteria decision making (abbr. MCDM) algorithm utilizing the two Dombi-Choquet integral operators is innovated. The proposed Dombi-Choquet MCDM algorithm for the rCTSFS model is applied to an MCDM problem related to diabetic retinopathy detection on five real-life fundus images of different severity levels taken from the Messidor2 dataset. Our newly proposed algorithm proves to be the only algorithm that yields the correct diagnostic results that match the hard truth. On the other hand, none of the 50 algorithms observed among recent works in literature can produce the correct diagnostic results, even after lending the fuzzification procedure innovated in this work to them. [ABSTRACT FROM AUTHOR]

• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

• The centroid distance of PFNs is proposed and its properties are discussed. • Propose a score function based on the centroid distance and discuss its properties. • Applications of Pythagorean fuzzy clustering, ranking and MCDM are developed. • Some advantages of the proposed methods are showed through comparative analysis. By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of −4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed. [ABSTRACT FROM AUTHOR]

Given that the majority of existing approaches for multi-criteria group decision making (MCGDM) rely solely on the preferences of decision makers (DMs) and fail to consider the various relationships between alternatives, this paper attempts to model the relevant relational structures using graphs and introduce the concept of graph neural networks (GNNs) in the context of group decision-making. By leveraging the powerful expressive capabilities of GNNs, the aim is to mine additional information pertinent to the decision-making process and screen out alternatives for the final decision. To begin, we provide a mapping of MCGDM to the graph domain and construct a corresponding relation graph among alternatives. Additionally, to deal with uncertain or vague information, we transform the group decision-making problem into a Pythagorean fuzzy environment and define a novel measure of entropy specifically designed for Pythagorean fuzzy sets (PFSs) in the entropy weight model to determine the weights of criteria. Simultaneously, we propose a new distance measure for PFSs, which is then applied to the extended Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to rank alternatives. Furthermore, we develop a GNNs-based Pythagorean fuzzy MCGDM approach that incorporates the aforementioned techniques for group decision-making. Finally, to validate the effectiveness and superiority of this approach, we employ it to address a supplier selection issue. Compared with baseline group decision-making approaches, our approach can indeed capture the relationships among alternatives in complex group decision-making scenarios and outperforms the best-performing baseline by nearly 2.8% in terms of ranking accuracy. • A mapping from multi-criteria group decision making to the graph domain is provided. • A normalized entropy measure is defined in Pythagorean fuzzy sets. • A modified Pythagorean fuzzy distance measure is proposed. • A Pythagorean fuzzy group decision-making approach based on graph neural networks is developed. [ABSTRACT FROM AUTHOR]

The high dimensionality of data hinders the learning ability of machine learning algorithms. Feature selection techniques can be used to reduce dimensionality, which is an important step for processing high-dimensional data. Feature selection solves this problem by removing irrelevant and redundant information, which can improve learning models, reduce calculation time, and improve learning accuracy. In this paper, a novel filter in mixed-attribute datasets for feature selection is proposed. The independent attributes are mixed or heterogeneous in the sense that both numerical and categorical attribute types may appear together in the same dataset. Based on the preordonnances theory, we use a new concept to quantify the relevance and redundancy of features even if there are heterogeneous (mixed-type) data. The technique for order preference by similarity to the ideal solution is one of the well-known multicriteria decision-making methods; it is utilized as a weighting and informative feature selection filter. To assess the effectiveness of the proposed method, several experiments, both simulated and real, are performed, including a comparison to other well-known filter methods. The experimental results show that, in most cases, the method yielded competitive results in comparison to other methods. • Introducing new criterions for relevance and complementarity. • Dealing with mixed-attribute datasets without requiring a preprocessing step. • The multi-criteria decision method, namely TOPSIS, is used to score the explanatory features. • Investigate the performance of the proposed filter in high-dimensional data by using simulated and real data sets. [ABSTRACT FROM AUTHOR]

The evaluation of urban energy internet (UEI) is crucial for the establishment of low-carbon cities. This paper aims to present a hybrid decision-making framework to address UEI evaluation problems from a sustainability perspective. First, the Fermatean fuzzy Aczel-Alsina power aggregation operators are proposed to aggregate evaluation information, and the remarkable properties of these operators are explored. Then, the combined weight of the criteria is determined through the decision-making trial and evaluation laboratory method and the Shannon entropy-based power weighted method within the Fermatean fuzzy circumstance. Moreover, the classic multiobjective optimization by ratio analysis plus the full multiplicative form method is extended using the proposed aggregation operators. Finally, an index system was constructed from a sustainability perspective, and the level of development of the four UEI projects is assessed using the proposed method. The experimental results demonstrate that the proposed method can provide beneficial guidelines for the construction and development of the UEI. Furthermore, the reliability and effectiveness of the proposed decision-making framework are validated through sensitivity analysis and a comparative study with existing MCDM methods. [ABSTRACT FROM AUTHOR]

With the constant changes in the decision-making environment, various limitations have emerged in traditional decision-making methods, such as the inability to simultaneously consider multiple factors such as randomness, hesitation, and ambiguity, thus limiting a comprehensive grasp of the decision-maker's cognitive inputs. To address the above problems, we propose a multi-criteria decision-making (MCDM) model based on a new fuzzy set (FS). Firstly, we define a new Z hesitant fuzzy linguistic term set (ZHFLTS) and propose a visualization metric for mapping ZHFLTS into T-spherical fuzzy (T-SF) space to simplify the computation. The relevant properties of ZHFLTS are further investigated. In order to accurately measure the uncertainty of FSs, we propose the probabilistic-hesitation-fuzzy (PHF) entropy and use it as a basis to derive the potential probability distribution using the fruit fly optimization algorithm (FOA). Subsequently, an MCDM model is established based on the proposed ZHFLTS, visualization metric, and PHF entropy. The model can better solve complex decision-making problems such as TCM through example studies and comparative analyses. The research in this paper aims to cope with today's increasingly complex decision-making environments, and the introduced ZHFLTS framework and the corresponding methodology provide new ways to solve complex decision problems. • ZHFLTS is defined and a mapping method to visualize ZHFLTS in T-SF space is studied. • PHF entropy is proposed based on FOA for obtaining potential probability distribution. • A Novel MCDM model based on ZHFLTS is presented. • It proves the effectiveness of the model through instances and comparative analysis. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making (MCDM) is a popular branch of decision theory in which many methods have been proposed to solve MCDM real-world problems. However, in spite of the multiple methods, it is common that different of them provide the same solutions. Therefore, this paper analyzes the causes of the same solutions from different MCDM methods, studies why different normalization methods can achieve the same solutions in MCDM methods through the theory of MCDM methods, and discusses the relationship between different MCDM methods by category. To achieve such goals, MCDM methods are classified into three categories according to their ranking methods. Some traditional MCDM methods are then extended by considering general normalization methods and distance measures. Finally, the characteristics of various MCDM methods are discussed by category and their evaluation indexes are also compared. The comparison results are represented by tables and flowchart models. The relationship between different categories of MCDM methods is shown through the comparison of the evaluation indexes. The aim of the proposed method is not to replace existing MCDM methods, but to fill some present research gaps about MCDM methods. • General normalization methods and distance measures are considered in MCDM methods. • The causes of the same solutions from different MCDM methods are examined. • The characteristics of various MCDM methods are discussed by category. • The relationship between different MCDM methods is summarized. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) technology provides new possibilities for a variety of applications, but it also comes with challenges influenced by distinct aspects, such as the size of organizations that use such technology. Therefore, it is important to understand which architecture of 5G-radio access networks (RANs) is best for a given purpose; this requires an evaluation platform for assessment. This paper tackles this problem by presenting a novel multi-criteria decision-making (MCDM) solution based on a new integrated fuzzy set. The proposed integrated approach, which is based on a Type-2 neutrosophic fuzzy environment, is developed to address the application challenges of 5G-RANs architecture evaluation, as also to face the MCDM theoretical challenge represented by ambiguities and inconsistencies among decision makers within the decision making context of the presented case study. Many MCDM techniques for weighting and selection were presented from the literature, yet many of them still suffer from inconsistencies and uncertainty. Therefore, the chosen methods in this research are unique in a way that previous issues are addressed, making them suitable for integration with Type-2 neutrosophic fuzzy environment, and therefore creating a more robust decision platform for the presented challenge in this research, as a theoretical contribution. First, a new "Type-2 Neutrosophic Fuzzy-Weighted Zero-Inconsistency" (T2NN-FWZIC) technique is formulated for weighting the evaluation criteria of RAN architectures. Second, another new method, namely, "Type-2 Neutrosophic Fuzzy Decision by Opinion Score Method" (T2NN-FDOSM), was formulated to select the optimal RAN architecture using the obtained weights. The weighting results by T2NN-FWZIC for the (n = 25) evaluation criteria revealed that (C 21 latency and C 22 reliability) as the most important criteria, with 0.06 value for each as opposed to (C 15 Data Processing) as the lowest weighted criteria with 0.0186 value. As for T2NN-FDOSM, a total of four 5G-RAN architectures were evaluated, including "virtualized cloud RAN" coming as the optimal one, followed by "fog RAN," "cloud RAN," and finally "heterogeneous cloud RAN." The results were confirmed by carrying out a sensitivity analysis. The outcome of this study can be used to assist future 5G-RAN developments according to business needs and to establish an assessment platform for 5G technology in different domains and applications. [ABSTRACT FROM AUTHOR]

Best-worst method (BWM) has received extensive attention because of its fewer pairwise comparisons and maintenance of consistency. Extending it to various fuzzy environments has become a hot research spot. This paper initiates a new interval-valued intuitionistic fuzzy (IVIF) BWM with additive consistency. Firstly, an IVIF fuzzy preference relation (IVIFPR) is viewed as a directed graph with IVIF weights. An approach is offered to distinguish the best and worst criteria by the defined score out-degree and accuracy out-degree of criteria. The concept of IVIF reference comparisons is introduced. The additive consistency of IVIF reference comparisons is acquired by that of IVIFPR using its left and right intuitionistic fuzzy preference relations. We derive the relationship between the IVIF reference comparisons and the IVIF priority weight vector. Based on this relationship, a linear goal programming model is constructed to generate the IVIF priority weights. Two consistency indexes are analytically obtained and the consistency ratio is then defined to check the consistency of IVIF reference comparisons. For the unacceptably additive consistency, a new approach is put forward to enhance the consistency. Thereby, the proposed IVIF BWM is summarized step by step. Three examples of applications are furnished to demonstrate its effectiveness and validation. [ABSTRACT FROM AUTHOR]

This paper proposes a new Multiple-criteria decision-making (MCDM) method called MUltiple-TRIangles ScenarioS (MUTRISS) with two scenarios respecting different levels of access to complete information for material selection problems. MUTRISS calculates the areas occupied by alternatives in n-dimensional space, employing analytic geometry and converting each alternative into n-edges forms. The paper applies MUTRISS to three material selection case studies, with Ti-6Al-4V, Material 4, and AISI 4140 Steel- UNS G41400 emerging as the best materials for the three examples with the highest overall scores of 0.036, 4.540 and 0.427 respectively. The results are compared with various MCDM methods through four statistical measures, including relative closeness ratio, robustness analysis, compromise ranking coefficient, and similarity degree. The measures focus on different aspects of MCDM methods in solving problems and their results. The paper concludes that MUTRISS offers a more robust and reliable approach for material selection problems compared to other MCDM methods, with the first scenario of MUTRISS being more reliable than the second scenario. The paper also emphasizes the importance of validating results in material selection problems due to the potential irreversible consequences of selecting the wrong material. [ABSTRACT FROM AUTHOR]

Due to more and more awareness of environmental conservancy and sustainable development, automated container terminals (ACTs) have gained increasing attention and become popular worldwide. However, there is a crucial challenge in implementing ACTs, i.e., how to develop terminal layout planning (including terminal layout and facility selection) to realize performance goals. Focusing on this challenge, the aim of this paper is to resolve how to appropriately select terminal layout and facilities in ACTs with respect to various performance criteria. Several research limitations can be gathered from the existing literature: (i) Most of the existing works mainly develop optimization technologies to tackle layout optimization and facility comparison issues within a few types of terminal layout and facilities, yet few researchers have proposed evaluation methods for making selection decisions on terminal planning. (ii) Only a few types of criteria are considered, and requirements and preferences of different participants are ignored, not to say realizing an optimal compromise by employing comprehensive criteria weights in evaluation processes. (iii) New technological improvement in ACTs has not received much attention, and there still lacks a method to precisely quantify the concerned criteria. Accordingly, this paper introduces a criteria system comprising economic, environment, social, resource and performance groups and develops an agent-based simulation method considering different terminal layout and facilities to determine criteria values. A multi-criteria decision making (MCDM) framework is proposed for making the most reasonable and appropriate selection decisions, which employs a variation coefficient weighted method to calculate comprehensive criteria weights, which integrate subjective and objective weights obtained from AHP (analytic hierarchy process) method and entropy weight method. To overcome rank reversal problems in the TOPSIS (technique for order preference by similarity to an ideal solution) method, a new improved rank reversal robust TOPSIS method, called NR-TOPSIS, is utilized to rank and select alternatives, which is ranking stable in the experiments performed. An illustrative application on a container terminal in China demonstrates the effectiveness of the proposed MCDM method and sensitivity and comparative analysis verifies the robustness and stability of the method. [ABSTRACT FROM AUTHOR]

The real-world complex systems of transportation and insurance constantly produce massive data, and the number of variables used to capture these systems can sometimes be overwhelming. Predictive modelling of such high-dimensional data often comes with the increased complexity of models, leading to low model interpretability and performance. Therefore, balancing the model's interpretability and prediction accuracy is crucial. Keeping a balance between these two aspects significantly improves prediction reliability. This will ensure that complex systems' overall control and management can be statistically optimal. However, measuring variable importance to reduce the dimensionality of given problems may be challenging due to different data sources, methods employed, or both. This paper proposes a novel approach to variable selection based on a comprehensive variable importance measure. The proposed method formulates variable selection as a multi-criteria decision analysis (MCDA) problem and uses TOPSIS to solve it. It offers a systematic approach to selecting variables to construct more interpretable predictive models. Our contribution is a thorough investigation using simulations with known model characteristics and a rigorous test of the method's robustness by analysing different datasets with different noise scenarios. To demonstrate the effectiveness of our approach, we apply it to the variable selection problem for the national collision database. We identify the most significant variables that may significantly influence the prediction of fatal accident rates. [Display omitted] • Variable selection is formulated as a Multi-criteria Decision Making problem. • TOPSIS with different weightings is proposed for ranking variable importance. • A simulation study is conducted to demonstrate the robustness of the method. • The proposed method is applied to national collision data to illustrate usefulness. [ABSTRACT FROM AUTHOR]

The continuous development of advanced manufacturing has triggered a strong demand for technological service platforms. To better support the development of the technological service platform, it is necessary to conduct a performance evaluation for the technological service platform. However, the existing evaluation methods fail to fully consider the ambiguity and uncertainty of decision-makers' judgments. Meanwhile, conventional methods require quite a few pairwise comparisons when determining the criteria weights. This paper proposes a novel approach that integrates rough Z-number, the best worst method (BWM), and TODIM (an acronym in Portuguese of Interactive and Multi-Criteria Decision Making) to address these issues. The proposed method can significantly reduce the number of pairwise comparisons. It also shows a significant advantage in dealing with vague information and handling the reliability of the evaluation. Moreover, the TODIM method helps capture the decision-makers' risk attitude towards uncertainty. Finally, a case study of seven technological service platforms is conducted to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present the formalization and implementation of a decision framework in which a group of agents can express their preferences among a set of alternatives by using several comparison criteria. We define special kind of argument to support agents' preferences, and the set of generated arguments is used both to obtain the set of selected alternatives and to explain why they were selected. We introduce a software application that implements our proposed approach and provides interactive tools for obtaining different kinds of explanations for the computed results. Also, we formally study the proposed argumentation process and we prove that the set of warranted conclusions, which support the obtained results, is consistent. We also show that our framework works properly on choice scenarios with incomplete evidence, and we propose how to optimize the computation process for dynamic environments. [ABSTRACT FROM AUTHOR]

In this paper, a novel multi-criteria three-way decision (TWD) method is devised to solve the problems of classification and sorting by considering requirements and targets based on qualitative and quantitative information. TWD theory has been generally utilized to handle multi-criteria decision-making problems. However, few studies on the TWD theory consider the requirements and targets of decision-makers. To fill this gap in the existing studies on the TWD theory, the conditional probability is computed based on the fuzzy axiomatic design and the target-based aggregation operator. A parameter is designed to reflect the preference degree of the basic requirements over the target values offered by the decision-maker. To meet the description needs of quantitative and qualitative attributes, the real numbers and the interval numbers are employed to describe the quantitative information, the interval-valued double hierarchy linguistic term sets are employed to express the qualitative information. The analytic hierarchy process method is utilized to calculate the weights of the criteria with interval-valued double hierarchy linguistic information. Following the maximum-utility principle, the classification and ranking of the objects are attained. Ultimately, a case study on the selection of algorithm engineers in the recruitment process verifies the practicability and effectiveness of the proposed method, and a comparative analysis with other methods proves the superiority of this method. • Basic requirements and target values are considered in the three-way decision. • Quantitative and qualitative expressions are used to describe the criterion values. • Our proposed method can not only classify the objects but also rank them. [ABSTRACT FROM AUTHOR]

Electric vehicle charging station location (EVCSL) is critical to promoting the electric vehicle industry and how it is evaluated is typically a MCDM issue. In this paper, an applicable multi-criteria group decision support model is developed to evaluate the electric vehicle charging station location. To do this, we used normal wiggly hesitant fuzzy set to describe the fuzzy decision-making information, which can further dig the potential uncertainty. Considering that the experts' preference can lead to significant changes in the results, k-means clustering method is used to group the experts in different fields. Moreover, the improved correlation coefficient and standard deviation (CCSD) method is used to determine the criterion weights, and considering that prospect theory can reflect the mental behavior of experts and the simplicity and efficiency of weighted aggregated sum product assessment (WASPAS) method, a combined ranking model is proposed to evaluate the alternatives comprehensively. The procedure of the model is given in detail, and the example of electric vehicle charging station location evaluation is employed to demonstrate our results. Comparative analyses and discussion show that the proposed model has powerful effectiveness, performs better than other existing methods, and largely extends the applicability of the method with theoretical and practical implications. [ABSTRACT FROM AUTHOR]

• Paper presents the application of HMCDM methods for ranking dental materials. • Ten evaluative criteria are used for the ranking. • Comparison of hybrid MCDM methods was executed. • Rank correlation and the statistical p -value are used to compare HMCDM methods. Ascertaining the appropriate material through detailed functionalities out of numerous existing materials is a complex and time-consuming exercise. Hence, it is essential to follow a methodical and resourceful approach to decide on the best material for any given application. This paper aims to demonstrate the capability of five distinct Hybrid Multi Criteria Decision Making (HMCDM) methods in identifying the best polymer based biomaterial used in dentistry. A total of seven materials were evaluated based on ten criteria. At first, Analytical Hierarchy Process (AHP) was employed to determine the weights of the recognized criterion. Later, AHP-VIKOR, AHP-TOPSIS, AHP-MOORA, AHP-ELECTRE, and AHP-PROMTHEE procedures were used to rank the materials. The methodologies have displayed their effectiveness by providing the equivalent ranking to the materials. Finally, spearman's rank correlation was determined to compare HMCDM methods. The comparative analysis revealed that AHP-VIKOR, AHP-TOPSIS, and AHP-PROMETHEE have a greater correlation compared to AHP-MOORA and AHP-ELECTRE. [ABSTRACT FROM AUTHOR]

• Trust relationships among recommenders are applied in fusion method. • Personalized individual semantics of trustor and recommenders are considered. • A new definition of harmony degree based on heterogeneous opinions is proposed. • The weights of other recommenders for a recommender are determined from four aspects. • The comprehensive weights of criteria consider subjective and objective information. This paper develops a new trust and personalized individual semantics (PISs) based fusion method for heterogeneous multi-criteria group decision making (HMCGDM) and applies to the selection of live streaming commerce plan. Firstly, a consistency-driven optimization model is built to determine the PIS of linguistic terms in linguistic preference relation of criteria given by the trustor and recommenders. Secondly, this paper defines a harmony degree to reflect the degree of difference in the evaluation information of different recommenders in HMCGDM problem. Then, considering the trust relationship between trustor and recommenders and between the recommenders themselves, two multi-objective mathematical programming models are constructed. One purpose is to produce values to positions that are not assigned, and the other purpose is to fuse the evaluation information of recommenders. In addition, combining the preference information of criteria given by trustor and Shannon entropy based on the collective evaluation information, the subjective and objective weights of criteria can be determined respectively. The comprehensive criteria's weights are acquired by an optimization model which aims at minimizing the Jenson-Shannon divergence between the subjective and objective weights. Finally, an example of selection of influencer or celebrity in live streaming commerce for promoting new products sales is offered to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Setting a mathematical-model approach to solving selection of giant machine. • Validation of the problem-solving selection approach for the modernization process. • Methodology selection of bucket wheel excavators were proposed. • This methodology is reliable and suitable tool for the purpose. This paper presents a methodology for evaluating and ranking different bucket wheel excavators (BWE) for determining which machine should enter in the process of revitalization and modernization. Various multicriteria methods are used. Combining them achieves the best effect when choosing BVE for the modernization process. AHP, TOPSIS, VIKOR, and PROMETHEE methods are used. The hybrid models are developed from the multi-criteria decision making (MCDM) methods and they are used to evaluate the basic indicators that characterize the operation of a BWE. The methods will be applied to each group of the parameters, their sub-parameters and alternatives in the selection. The models are defined in two parts. In the first part, the priority vectors of parameters are defined by applying the AHP method. Other methods are applied in the second part where the evaluation of alternatives is made according to the defined parameters. That forms three partial hybrid models AHP-TOPSIS, AHP-VIKOR, AHP-PROMETHEE. The analysis parameters are qualified in four groups Technical, Working, Maintenance, and Economics parameters. Validation and selection of the optimal method will be performed in relation to the expert analysis with prepared questionnaires. The selected method should help in defining which machine will enter in the process of revitalization and modernization. According to this method, there is a 100% match with AHP-PROMETHEE, and the ranking given with AHP-TOPSIS corresponds to 75%, while AHP-VIKOR corresponds to 50%. Model AHP-PROMETHEE for the selecting of the BWE is a reliable and suitable tool for this purpose. In comparison to the classical statistical methods, this approach can achieve great saving. The BWE are very expensive machines and their value is 20–30 million euros. The investment in revitalization and modernization is 25–35% of the purchase price. This explains the importance of forming such an evaluation model. The presented analysis showed the possibility of the application of hybrid combined methods. The results in the paper presented the benefit from two aspects. The first aspect is the formation of a model that can be applied to other similar problems. Another aspect is the choice of the machine itself based on a complex analysis of a large number of parameters. Generally, the model becomes universal and can be used for later similar analyzes. [ABSTRACT FROM AUTHOR]

In this paper each possible decision in a given economic context is assessed according to a weighted aggregation of its ordered costs, what is known in the literature as an Ordered Weighted Averaging, or OWA, measure. We assume these weights are uncertain and belong to a given set through which different decision attitudes toward risk are modeled. A compromise (minmax regret) solution is proposed in order to conciliate these admissible decision scenarios. In particular, the choice of a set of weights with bounded orness , a measure of the aversion to the risk, is analyzed. A Linear Programming problem is proposed in order to find a consensus solution under such a set of bounded orness OWA operators. Later, this model is generalized by adding bounds on the difference of consecutive individual weights for the ordered costs and a Benders decomposition scheme is analyzed in order to solve the corresponding optimization problem. It is shown how this model can support decisions about the location of a new facility under a set of different OWA operators and its relation with the efficiency (Pareto optimality) from a multicriteria decision making viewpoint. • A new mathematical model to manage uncertainty in Ordered Weighted Averaging models. • Uncertainty on weights modeled through an orness measure. • A minmax regret criterion is used in order to achieve a consensus. • An application in a Location problem and extensions are given. • A numerical experiment to show operativeness is carried out. [ABSTRACT FROM AUTHOR]

• A novel consensus method has been developed for multi-agent decision systems. • The method is specially designed to allow large groups of decision-makers. • Social rules and component division are introduced to improve the method response. • The method allows to manage previous influence. • HIVES can be applied beforehand to obtain group decisions for any MCDM method. Aggregating multiple opinions or assessments in a decision has always been a challenging field topic for researchers. Over the last decade, different approaches, mainly based on weighting data sources or decision-makers (DMs), have been proposed to resolve this issue, although social choice theory, focused on frameworks to combine individual opinions, is generally overlooked. To resolve this situation, a novel methodology is developed in this paper based on social choice theory and statistical mathematics. This method innovates by dividing the assessment into components which provides a multiple assessment analysis, assigning weights to each source regarding their position compared to the group for each considered criteria. This multiple-weighting process maximises individual and group satisfaction. Furthermore, the method makes it possible to manage previously assigned influence. An example is given to illustrate the proposed methodology. Additionally, sensitivity analysis is performed and comparisons with other methods are made. Finally, conclusions are presented. [ABSTRACT FROM AUTHOR]

The core objective of this article is to portray a novel multi-criteria group decision-making (MCGDM) approach that combines the salient features of the 2-tuple linguistic m -polar fuzzy sets (2TL m FSs) with the theoretical background and accuracy of the well-known ELimination and Choice Translating REality (ELECTRE) II approach. The resulting 2TL m F-ELECTRE II technique outlines the framework and explains how to construct the ELECTRE II model for problems having 2TL m F uncertainties. Initially, for constructing ELECTRE-II methodology in the 2TL m F context, the basic steps are designed to evaluate the pairwise comparison of alternatives utilizing the fundamental concepts of concordance, indifferent, and discordance sets and matrices, respectively. After that, the 2TL m F strong and weak outranking relationships are investigated by comparing the 2TL m F-concordance and 2TL m F-discordance indices with the threshold values (three concordance and two discordance levels) provided by the decision experts. Finally, a complete ranking set is obtained using an iterative procedure on strong and weak outranking graphs. A comprehensive flowchart is supplied to elaborate the brief and clear vision for the mathematical procedure of the intended work. Moreover, we utilized the proposed methodology for selecting adequate sports utility vehicles (SUVs) to confirm the applicability and feasibility of the novel approach. Ultimately, the final outcomes are briefly compared with the existing 2TL m F ELECTRE-I approach to ensure the authenticity and reliability of the proposed work. • This paper addresses the case of providing 2-tuple linguistic m-polar fuzzy data. • We propose a new version of the ELECTRE II method based on 2TLmF information. • We consider a problem to show the practicality and efficiency of the proposed model. • We provide a comparative analysis of the proposed method with existing techniques. [ABSTRACT FROM AUTHOR]

In recent years, there have been dramatic changes in manufacturing systems in many industries depending on technological developments. Robotics is one of the essential components of these changes. Today, the usage of robotics in manufacturing processes has become widespread in almost all industries. Also, it has become a very strong desire ever-increasing for even small and medium-sized enterprises at present. Almost all the previous studies emphasized that industrial robot selection is a highly complex decision-making problem as there are many conflicting factors and criteria. Besides, different and advanced specifications of these robotics added by robotic manufacturers have caused to increase the complexities much more. Hence, decision-makers encounter more complicated decision-making problems affected by many uncertainties. Because of that, an integrated fuzzy group MCDM framework can help overcome many ambiguities proposed in the current paper. The proposed fuzzy integrated model consists of the fuzzy SWARA (F-SWARA'B) and the fuzzy CoCoSo (F-CoCoSo'B), which are extended with the help of the Bonferroni function. The model selected the appropriate industrial robotics used in the automotive industry by considering 15 criteria and ten alternatives. According to the result of the study, the three most significant criteria have been determined: Working Accuracy, Reaching Distance, and Performance; and the most suitable option is the A8. The obtained results were validated with the help of a comprehensive sensitivity analysis consisting of different 150 scenarios. The results are also compared with some existing techniques. The sensitivity analysis results approve the validity and applicability of the proposed model. [ABSTRACT FROM AUTHOR]

• Estimate the relevance of contextual information for music recommendation. • Model users' context-sensitive preference for music. • Recommendation quality is improved by treating criteria preferences as contexts. • Experimental results show the advantages of the proposed method. • The method outperforms both traditional baselines and context-aware models. Context-aware recommender systems have received considerable attention from industry and academic areas. In this paper, we pay heed to the growing interest in integrating context-awareness and multi-criteria decision making in recommender systems, to deal with the most pressing challenges in music recommender systems, namely the diversity of the recommended playlist, the scalability of the system, and the cold start problem. This paper introduces a new multi-criteria recommendation approach, named MORec, which generates Top-N music recommendations by bootstrapping the system using beforehand collected data. We usher by gauging the relevance of contextual information from the relation between three elements: user, music genre, and the user's context. Then, we apply an aggregation technique to uncover the relationship between the context and the overall rating. Besides, we apply the K-means algorithm to generate a predictive model that comprises clusters of similar contexts defining the association between contextual dimensions and music genres. Carried out experiments emphasize very promising results of our approach in terms of clustering quality, compared to the Partitioning Around Medoids algorithm in terms of connectivity and stability. The comparison versus pioneering recommendation baselines underscored the effectiveness of MORec in terms of recommendation quality and usefulness. [ABSTRACT FROM AUTHOR]

[Display omitted] • Main developments on sorting approaches. • The categorization of multiple-criteria decision-making sorting methods. • Applications of the multiple-criteria decision-making sorting methods. • Trends and future directions of MCDM Sorting. Multi-Criteria Decision Making (MCDM) is a complex process. It aims to support decision makers in making their decisions more effective and consistent. MCDM provides a useful and successful alternative for handling three main types of MCDM problems, namely, choosing, ranking and sorting. The first two are the most common problems studied but the third offers a way to deal with real world MCDM problems that require alternatives to be assigned to ordered categories. The practitioners are currently developing and applying sorting methods to solve problems from different application areas. In spite of its interest and applicability, there is only one previous review on Multiple-Criteria sorting, performed almost 20 years ago. Hence, because of its interest this paper presents a new and systematic review of MCDM sorting methods that includes 30 years of research in the field. This review has systematically analyzed the conventional and non-classical methods of MCDM sorting and then classified the papers published into 16 application areas. The analysis reveals that the methodological development is still in growth phase for MCDM sorting and discovers the applied methods' trends. It also shows the complete spectrum of the areas of the application addressed, the state of knowledge about methods, the type of contribution to the knowledge, and the application area for the four categories of the MCDM approaches. The systematic review scrutinizes each selected article in order to find out which approach from the multi-criteria sorting presents the most development based on its contribution and application of the methods. We also aim to discover which Multiple-Criteria sorting methods are the most studied in MCDM. The relevant finding is the relation between the most applied methods and the application areas together with future directions for further research. [ABSTRACT FROM AUTHOR]

Blockchain technologies, also known as Distributed Ledger Technologies (DLT), are increasingly being explored in many applications, especially in the presence of (potential) dis-/mis-/un-trust among organizations and individuals. Today, there exists a plethora of DLT platforms on the market, which makes it challenging for system designers to decide what platform they should adopt and implement. Although a few DLT comparison frameworks have been proposed in the literature, they often fail in covering all performance and functional aspects, adding that they too rarely build upon standardized criteria and recommendations. Given this state of affairs, the present paper considers a recent and exhaustive set of assessment criteria recommended by the ITU (International Telecommunication Union). Those criteria (about fifty) are nonetheless mostly defined in a textual form, which may pose interpretation problems during the implementation process. To avoid this, a systematic literature review regarding each ITU criterion is conducted with a twofold objective: (i) to understand to what extent a given criterion is considered/evaluated by the literature; (ii) to come up with 'formal' metric definition (i.e., on a mathematical or experimental ground) based, whenever possible, on the current literature. Following this formalization stage, a decision support tool called CREDO-DLT, which stands for "multiCRiteria-basEd ranking Of Distributed Ledger Technology platforms" , is developed using AHP and TOPSIS, which is publicly made available to help decision-maker to select the most suitable DLT platform alternative (i.e., that best suits their needs and requirements). A use case scenario in the context of energy communities is proposed to show the practicality of CREDO-DLT. • Blockchain (DLT) standardization initiatives are reviewed. • To what extent ITU's DLT assessment criteria are covered in literature is studied. • A mathematical formalizations of the ITU recommendations are proposed. • A decision support tool (CREDO-DLT) is designed for DLT platform selection. • An energy community use case is developed to show the practicality of CREDO-DLT. [ABSTRACT FROM AUTHOR]

• Feature engineering based framework for location-sensitive disaster event detection. • Augmentation of context-free and context-based features with place of occurrence. • Evaluation with unsupervised machine learning algorithm with 09 performance metrics. • AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set. • Location-augmented context-based features outperformed traditional textual features. Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust , that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis. [ABSTRACT FROM AUTHOR]

In the last decades, a considerable number of studies have been conducted to find the optimal locations for renewable energy facilities. The reviewed literature demonstrates how the combination of spatial representation computer tools, such as geographic information systems (GIS), with multi-criteria decision making (MCDM) methodologies, has successfully solved the problem of identifying optimal locations. Furthermore, since the appearance of fuzzy logic, the combination approaches have extended to the field of fuzzy sets to consider the imprecision and vagueness that some criteria may involve. In this paper, we propose a comparative analysis among fuzzy versions of MCDM methodologies, including GIS technologies, for the optimal site selection of offshore wind power plants. With this aim, we combined a classical pair-wise comparison method (AHP) with two distance-based approaches (TOPSIS and VIKOR), applying GIS software and comparing the two most extended fuzzy membership functions: triangular and linear. As a case study, this optimal location problem was applied to offshore wind site selection in the Gulf of Maine (USA). Initially, 56 alternatives for potential locations were identified from 22,331 km 2 study area. After applying the AHP methodology, the weights of the criteria were obtained, turning out to be the wind speed and bathymetry the most important criteria. The results demonstrate the robustness of the fuzzy TOPSIS methodology against potential variations in the criteria weights, since the best alternatives (optimal locations) and almost 90% of the 25 top-ranked alternatives were matching. Likewise, the rankings of alternatives illustrate that the use of triangular or linear fuzzy membership functions does not cause significant differences after applying the fuzzy VIKOR methodology and ArcGIS software. In fact, the most appropriate alternative is the same for both cases, and there is only an exchange of positions among the top-ranked alternatives. The proposed solutions can be applied to other locations and both onshore and offshore installations. • A comparative analysis among fuzzy versions of MCDM methodologies. • GIS and MCDM fuzzy methodologies combined for optimal site wind locations. • Assessment of site alternatives through TOPSIS–VIKOR MCDM comparative analysis. • Case study based on the offshore wind site selection in the Gulf of Maine, USA. [ABSTRACT FROM AUTHOR]

• Considering ABSA at three levels simultaneously based on customer reviews. • Integrating ABSA and BWM in designing an end-to-end ranking system. • Ranking the quality of services, facilities and amenities based on real case study. • Supporting customers decision by addressing the issue of "choice overload" • Designing an end-to-end ranking method as a service for web users. Considering customer reviews is one of the challenges of real-world decision models. These reviews can be on different platforms and include a large amount of information and may also include incomprehensible and unrelated phrases. The main advantage of customer reviews is the realistic view that it provides a realistic view of the product or service. Therefore, converting unstructured and incoherent customer-based reviews into machine learning language and ultimately turning it into a decision model is very important. In this paper, we propose an end-to-end ranking method for integrating mechanisms such as text processing, sentiment analysis and the multi-criteria decision-making technique. The proposed ranking method relies on the integration of three methods, namely, the aspect-based sentiment analysis (ABSA) method, the Dawid-Skene algorithm and the Best Worst Method (BWM). In other words, the proposed work encompasses four major steps: i) crawling customer reviews, ii) preprocessing, iii) aspect term extraction, aspect category detection and polarity detection, and iv) designing a decision-making model. The main contribution of this study is to consider ABSA at three levels simultaneously and integrate ABSA and BWM in designing an end-to-end ranking method for ranking the quality of hotel services, facilities and amenities based on customer reviews. The ability of the proposed end-to-end ranking framework is evaluated using a real data set of user reviews of Sydney hotels. [ABSTRACT FROM AUTHOR]

This paper introduces and investigates ranked hesitant fuzzy sets, a novel extension of hesitant fuzzy sets that is less demanding than both probabilistic and proportional hesitant fuzzy sets. This new extension incorporates hierarchical knowledge about the various evaluations submitted for each alternative. These evaluations are ranked (for example by their plausibility, acceptability, or credibility), but their position does not necessarily derive from supplementary numerical information (as in probabilistic and proportional hesitant fuzzy sets). In particular, strictly ranked hesitant fuzzy sets arise when no ties exist, i.e., when for any fixed alternative, each submitted evaluation is either strictly more plausible or strictly less plausible than any other submitted evaluation. A detailed comparison with similar models from the literature is performed. Then in order to produce a natural strategy for multi-criteria multi-agent decisions with ranked hesitant fuzzy sets, canonical representations, scores and aggregation operators are designed in the framework of ranked hesitant fuzzy sets. In order to help implementation of this model, Mathematica code is provided for the computation of both scores and aggregators. The decision-making technique that is prescribed is tested with a comparative analysis with four methodologies based on probabilistic hesitant fuzzy information. A conclusion of this numerical exercise is that this methodology is reliable, applicable and robust. All these evidences show that ranked hesitant fuzzy sets are an intuitive extension of the hesitant fuzzy set model designed by V. Torra, that can be implemented in practice with the aid of computationally assisted algorithms. • We introduce a new model called ranked hesitant fuzzy sets. • We develop scores in this framework. • An algorithm produces information fusion of ranked information under hesitancy. • Mathematica codes facilitate reproducibility of these computations. • We apply these elements to multi-criteria multi-agent decisions. [ABSTRACT FROM AUTHOR]

• A new Multi-Criteria Decision-Making Method, ABAC, is proposed. • ABAC is simple, more scalable and free from rank reversal problem. • ABAC is illustrated and validated using cloud service selection. • Various case studies using ABAC in different domains are provided. Decision-making appears as a complex and challenging task when it requires finding the most suitable alternative among the numerous alternatives in the presence of multiple, usually conflicting criteria. At the same time, stakeholders expect a simple, transparent, and traceable decision-making method. Multi-Criteria Decision-Making (MCDM) methods rank the alternatives considering multiple criteria. The rank reversal problem is an important issue in most existing conventional MCDM methods. This paper proposes a new alternative by alternative comparison-based MCDM Method (ABAC) that addresses the rank reversal problem. We prove that ABAC is free from the rank reversal problem. To illustrate and validate ABAC, we have taken the cloud service selection problem as an application. Further, to show the effectiveness of ABAC, we have provided several case studies covering various domains. We perform several experiments by simulating the ABAC method. We have compared ABAC and existing MCDM methods. The experimental results support that the ABAC method is a rank reversal free MCDM method. We also carry out sensitivity analysis for ABAC. Salient features of ABAC over existing MCDM methods are (i) it is simple; (ii) it is rank reversal free; (iii) it is more scalable. [ABSTRACT FROM AUTHOR]

COVID-19 is a respiratory infection caused by a coronavirus that spreads from person to person. In the present situation, the COVID-19 pandemic is a swiftly rising phase. Now the time is the second wave ending phase of coronavirus and the third wave coming phase of coronavirus in India. The pandemic situation is moving forward all over India. Nowadays, the worldwide COVID-19 pandemic structure is a very hazardous situation. The COVID-19 vaccine can suppress this situation and gain preventive measures against coronavirus. In producing the COVID-19 vaccine, the Indian medical board plays a significant role. The COVID-19 vaccines have exhibited 90%–95% efficacy in preventing symptomatic COVID-19 infections. Against COVID-19, for emergency purposes, the Indian medical board has approved three vaccines: Covishield, Covaxin, and Sputnik V. Generally, the Indian people are embarrassed about the vaccination of COVID-19. All people are thinking about which vaccine is best for them. This labyrinth can be evaluated effectively using the multi-criteria decision-making (MCDM) technique. Therefore, we have proposed a novel MCDM technique for selecting COVID-19 vaccines. The main aim of this paper is to develop an MCDM technique based on a λ -weighted ranking interpreter ( R λ + , R λ − ). The first time, we have defined positive and negative λ -weighted rank interpreter for the ranking of single-valued bipolar neutrosophic (SVbN) number. Additionally, positive and negative λ -weighted values and positive and negative λ -weighted ambiguity of an SVbN-number are formulated here. Some important, valuable theorems and corollary of SVbN-number are formulated. To show the applicability of the proposed MCDM technique, we have considered a real decision-making problem where ratings of the alternatives are with SVbN-numbers. [ABSTRACT FROM AUTHOR]

• Performance measurement complexities during relief operations are key motivations. • Strategies aimed to improve the performance measurement in HSC were proposed. • Proposed strategies were modelled using the grey-DEMATEL and m-TISM approach. • The proposed framework may help managers improve performance measurement in HSC. Performance measurement activities (PMA) in the humanitarian supply chain (HSC) face several issues. Many of these issues are avoidable and inter-related, subsequently producing undesirable cascading effects. HSC Stakeholders are actively looking for means to reduce the complexities associated with PMA and improve its efficiency. In this paper, we identify and model the eleven strategies that can improve both PMA and the performance measurement system (PMS). Strategies were identified using detailed literature reviews and discussions with the experts and were modelled using the grey DEMATEL and m-TISM methodology. The results indicate that the most effective strategies are fundraising for PMA, Digital supply chain adoption, Top management support, Public awareness activities and Relief worker training. Four strategies can be grouped into the cause side and seven strategies on the effect side. The key recommendation is to rapidly adopt digital supply chain applications in the HSC. HSC stakeholders also need to pay increased attentions towards fundraising activities targeting PMS. This study advances the understanding of PMS in the context of HSC. The decision-maker can adopt the study's findings to reduce the complexities that occurred during PMA. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new decision making method for selection problems under uncertainty is proposed. • The method considers both the objective weights and the subjective preferences. • The method is applied to the selection of renewable energy technologies. • The best option varies depending on the coefficient factor of the subjective weight. • The method is applicable to other selection problems in different areas. The use of renewable energy technologies is a key factor for sustainable development but their selection from several alternatives is a difficult task that relies on the careful assessment of relevant criteria. While Multiple Criteria Decision Making (MCDM) methods have been used successfully in various renewable energy technology selection problems, the decision process becomes more challenging when preferential judgements are made on the basis of non-homogenous and imprecise input data, and when there is uncertainty due to disparities among decision makers. This paper presents a hybrid MCDM method capable of overcoming these problems by taking into account quantitative and qualitative data under a probabilistic environment in the context of group decision making. In this method, qualitative data is fuzzified and used along with quantitative data to develop a hybrid model. A coefficient factor allows decision makers to vary the weight of each quantitative model so that the resultant criteria weights and overall alternatives' scores consider both subjective considerations and objective information. An example is presented to showcase the usability of the method developed for ranking and evaluating renewable energy technologies in the mining industry. In addition, the impact of different coefficient factors on the final results was assessed by means of sensitivity analysis. The results indicate that the method developed is able to minimise the loss of valuable objective information, caused by the subjective bias of qualitative weights during the evaluations, by adjusting the coefficient factors of the hybrid model during the calculations. [ABSTRACT FROM AUTHOR]

Purulia district is a drought-prone area in West Bengal, India. Naturally, the farmers of the Purulia district could not plant the crops due to the absence of water resources. Water resource management (WRM) has the potential mechanism to address the available water resources. This paper developed a multi-criterion water resource management technique for defining the available water resources in the agriculture field. We have introduced a novel ranking method of generalized single-valued non-linear bipolar neutrosophic numbers based on possibility measures. Additionally, we have first defined the possibilistic mean of generalized single-valued non-linear bipolar neutrosophic number. Using the possibilistic mean, we have invented the positive and negative rank expositors ( S μ + , S μ − ) for authenticity, hesitate, and falsity membership function. In the present situation, water resource management in agriculture has become a big problem worldwide. Multi-criteria decision-making technique is necessary for developing this situation. Using the proposed multi-criteria decision-making method, we have solved a real water resource management problem in the Purulia district under GSVnTbN-environment. In this decision-making problem, we have considered the GSVnTbN-environment. Because during the summertime, water demand is very high, and at the end of summer, when monsoon season comes, water demand is deficient in the agriculture field of Purulia district. Sometimes the monsoon season, rainwater does not occur entirely. At that time, monsoon crop planting was tough in the agriculture field. So, this district's water scarcity nature is a non-linear and uncertain type of nature. Therefore, we proposed an MCDM method for water resource management problems under GSVnTbN-environment. • MCDM technique of water resource management (WRM) problem. • Possibilistic mean of GSVnbN number. • Positive and negative rank expositor ( S μ + , S μ − ) of GSVnbN number. • WRM problem of agriculture field in the Purulia district. • WRM problem solved under GSVnbN environment. [ABSTRACT FROM AUTHOR]

• The interval type 2 fuzzy Bonferroni mean Ordered Weighted Averaging is developed. • Seven Bonferroni mean quantifier Ordered Weighted Averaging models are formulated. • The optimal weights are solved from the mixed integer linear programming models. • The models are applied to various linguistic decision making aggregation schemes. In this paper, the new aggregation models for the interrelated multi-criteria decision making (MCDM) problems based on the quantifier guided ordered weighted averaging (QGOWA) and Bonferroni mean (BM) operator with interval type 2 fuzzy sets (IT2FS) are developed. In most MCDM problems, the decision criteria might not be totally independent. For some MCDM methodologies that are not considering the interrelationship of the criteria, the decision results suggested are meaningless. The BM operator can express the interrelationship of the input arguments, which serves as a mean type aggregator. Yager introduced the OWA operator which is associated with the orness level (attitudinal character) by means of the quantifiers. Different quantifier functions are associated with the respective different orness levels. This is referred to as the QGOWA operator. Besides, the real world MCDM problems are mostly under uncertain environments. To address such MCDM problems, the linguistic criteria weights and the alternative rates are better characterized by IT2FS. The major contributions of this paper are to propose the interrelation MCDM aggregation models with various extensions, to construct the mixed integer linear programming models for obtaining the optimal QGOWA BM IT2FS weights, and to formulate a new interrelation MCDM paradigm. The developed aggregation models are: (1). Ordinary MCDM aggregation; (2). BM with OWA weights; (3). BM with OWA weights and personal importance; (4). BM with QGOWA weights; (5). BM with QGOWA weights and personal importance; (6). BM with QGOWA weights and attitudinal characters; (7). BM with QGOWA weights, attitudinal characters and personal importance. A new MCDM aggregation methodology with application based on the developed models is introduced. The application results from the MCDM aggregation methodology demonstrate that the final decision prioritization are actually affected by the various orness levels predetermined by the decision experts. [ABSTRACT FROM AUTHOR]

• D numbers are introduced to deal with the vagueness in decision-making. • A novel MCDM model based on D numbers and linguistic fuzzy variables is proposed. • A hybrid BWM-MABAC-D multi-criteria based decision model is proposed. • D numbers methodology is very flexible to deal with the vagueness in HCW problem. • Multi-criteria techniques were compared based on D numbers and fuzzy approaches. Healthcare waste (HCW) management is a complex and challenging problem. It is one of the priorities in health. An increase in the number of the health services provided leads to an increase in the amount of HCW, which has particularly been noticeable in recent years. Since this is the waste that may pose a risk to humans and the environment, it is necessary to ensure an adequate treatment of the same. HCW management is particularly important in developing countries, due to inappropriate disposal methods, underfunding and a lack of the infrastructure. In order to achieve the cost-effectiveness and sustainability of this area, HCW should be minimized through an adequate treatment of the same. The Public Enterprise Zdravstvo Brčko (Brčko Health System) has intensively been addressing the HCW management issue. They have decided to upgrade the HCW system by purchasing a new infectious waste treatment facility. The paper is aimed at creating a new original integrated multicriteria decision-making model based on D numbers for processing fuzzy linguistic information. This model will serve to support management in the procurement of the mentioned facility. The model integrates the benefits of different approaches and theories. An initial model was formed, consisting of the six potential solutions evaluated based on the 18 criteria classified into the following four groups: social, environmental, economic and technological. Four experts in this field evaluated the criteria and potential solutions. Then, a new Best-Worst Method based on D numbers (BWM-D) was applied in order to determine the significance of the criteria. After that, a Multi-Attributive Border Approximation Area Comparison Based on D numbers (MABAC-D) was developed and applied so as to evaluate and select an infectious waste treatment facility. The results have shown that the alternative A1 gives the best results, whereas the alternative A5 shows the worst results. Finally, a sensitivity analysis was performed to validate the obtained results. In this part of the paper, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS-D) and VIšekriterijumska Optimizacija Kompromisnog Rešenja (VIKOR-D) were developed in order to validate the results. When procuring a new Contagious Waste Treatment System, the characteristics of the available devices need be perceived and all the criteria need be taken into account in order to provide a device which will solve the HCW problem in the best way. This paper has shown how D numbers can be used when making a selection of an HCW management device, and also how all the characteristics of such a device can be perceived and how the device demonstrating the best characteristics can be selected. [ABSTRACT FROM AUTHOR]

Blockchain is one of the most disruptive technologies introduced in Bitcoin, which engaged great attention from the industry and academia and determined a rapid growth of other Distributed Ledger Technologies (DLTs). In the complex architecture of a DLT system, a consensus protocol plays a key role by ensuring that all participants agree on the data integrity without any central authority. A wide range of consensus protocols have been designed with different concepts and properties (e.g., lower energy consumption, better scalability, smaller latency, higher throughput, etc.). The key requirements for consensus protocols passing from one blockchain system to another often differ significantly, and there is no one-fit-all protocol. Therefore, selecting the most suitable consensus protocol for a particular DLT system is essential, but at the same time a challenging step, as decision-makers need to make a trade-off between conflicting requirements. This paper introduces a framework for selecting the most suitable consensus protocols depending on the identified criteria, priorities, and other requirements by incorporating Multi-Criteria Decision-Making (MCDM) techniques. We demonstrate its potential by identifying the preferable consensuses for the three most common types of existing blockchain systems and on an actual application for bike renting. Moreover, the collected data and tools are freely available, ensuring full replicability, reusability, and further development. • A comprehensive literature review dedicated to selecting consensus protocols. • A new open-source data collection reflecting 18 state-of-the-art consensus protocols. • The first MCDM-based framework to identify preferable consensus protocols. • Demonstration on three main types of blockchains and bike renting application. [ABSTRACT FROM AUTHOR]

• An auxiliary algorithm is proposed to construct initial stage of the MCDM methods. • A multi-objective fuzzy optimization model is formulated. • An extensive comparative analysis is conducted in terms of the addressed methods. • The stability and validity of the results are tested and discussed. • Key findings and managerial insights are provided. This paper examines the performances of the multi-criteria decision-making (MCDM) methods and optimization model in solving multi-attribute shortest path problems such as the safest shortest path under a fuzzy environment. To the best of the knowledge of the authors, this is the first study performing comparative analysis on finding the multi-attribute shortest path by employing well-known techniques in terms of computational effort and results in a fuzzy environment. To this end, the safest shortest path problem, where the risk and distance values concerning arcs on a directed network are defined as triangular fuzzy numbers, is handled. The solution process is carried out under two main headings: (i) To start the solution with MCDM methods, an auxiliary algorithm that constructs a fuzzy decision matrix is proposed. Then, Fuzzy-Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Simple Additive Weighting (F-SAW), and Fuzzy Evaluation Based on Distance from Average Solution (F-EDAS), that are fuzzy-based MCDM methods, are employed to rank the alternative paths. (ii) A multi-objective fuzzy optimization model is formulated, and the most reasonable paths are obtained considering different α-cut levels. Following that, comparative analysis is performed through a set of scenarios considering the different weights of the criteria to see the variability in the rankings. Besides, the addressed fuzzy-based MCDM methods are compared in terms of computational complexity. Overall, the main findings and managerial insights regarding the effectiveness and performance of the methods discussed in the solution process are provided. [ABSTRACT FROM AUTHOR]

The Artificial Neural Network (ANN) is an important machine learning tool used in medical data classification for disease diagnosis. The learning algorithm in ANN training plays a crucial role in classification performance. Various approaches have been successfully applied as a learning algorithm for ANN training. This paper performs an experimental study that investigates the performance of different metaheuristics as learning algorithms to train the ANN for medical data classification tasks. The experiments are carried out on 15 well-known medical datasets. A comparative study is conducted with the classical Levenberg–Marquardt (LM) and other thirteen recent and relevant metaheuristics. Different evaluation criteria such as accuracy, sensitivity, specificity, precision, Geometric Mean, F-Measure, false-positive rate (FPR) are considered for performance estimation. The classification results are analyzed using Multi-Criteria Decision Making (MCDM) method, and the results with analysis establish that the Equilibrium Optimizer algorithm outperforms all the other algorithms included in the comparative study. • Artificial Neural Network training using metaheuristic algorithms. • An experimental study in medical data classification. • Performance analysis using multi-criteria decision making. • Equilibrium Optimizer shows superior performance over the competitive algorithms. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposes a novel quantification of two key concepts in expert/intelligent systems. • These concepts are the knowledge and ignorance levels. • Some theoretical results are developed to provide a holistic and in-depth treatment. • Some illustrative examples demonstrate key computational issues. • These results can become an integral foundation for more important developments. People use knowledge on several cognitive tasks such as when they recognize objects, rank entities such as the alternatives in multi-criteria decision making, or for classification tasks of decision making / expert / intelligent systems. When people have sufficient relevant knowledge, they can make well-distinctive assessments among entities. Otherwise, they may exhibit some uncertainty. This paper establishes two differential equations, of which one is for the interaction between the knowledge level and the uncertainty level, and the other is for the interaction between the ignorance level and the uncertainty level. By solving these two differential equations under certain boundary conditions, one can derive that the proposed knowledge level indicator is equivalent to Wierman's knowledge granularity measure up to a constant (exactly, ln2). Moreover, the knowledge level indicator and the ignorance level indicator are found to be in a complementary relationship with each other. That is, more knowledge implies less ignorance, and vice-versa. The results of this study bridge a critical gap that exists in the understanding of the concepts of knowledge and ignorance. [ABSTRACT FROM AUTHOR]

• A new form of the ranking based on optimal points MCDM method is developed. • A new weighting method is developed called Win, Loss, Draw method. • RBOP imitates the decision maker's behavioral pattern to find the best alternative. Supplier evaluation is a complex multi-criteria decision-making (MCDM) problem that deals with assessment of suppliers as the potential alternatives against various types of criteria. We consider the context where decision makers (DMs) have complete information about the suppliers and criteria. To address the needs of decision makers, a multi-criteria evaluation method named Ranking based on optimal points (RBOP) is developed in this paper. By imitating and simulating human decision-making behavioural patterns, the developed MCDM method selects the best alternative that is closer to what the DM desires. Furthermore, a novel subjective MCDM weighting method s called win-loss-draw (WLD) method is also developed, which is also based on human behavioural pattern. A real case study of domestic cheese brands is considered to apply the developed methods to select the best cheese supplier for an Iranian hypermarket. Compared to other MCDM methods, outputs of the RBOP method show some differences due to the impact of WLD method, which intensified divergence and optimal points during the decision-making process. [ABSTRACT FROM AUTHOR]

• Define Choquet integral based intuitionistic fuzzy arithmetic aggregation operator. • Combine the defined operator with ordered weighted aggregation operator. • Propose a decision-making method based on the combined operator. Intuitionistic fuzzy set (IFS), a classical extension of fuzzy set, is a powerful tool to describe fuzzy information in multi-criteria decision-making (MCDM) because of its membership and non-membership functions. Intuitionistic fuzzy aggregation operators are the standard mathematical tools for the combination of several inputs with respect to criteria into one unique output. The traditional intuitionistic fuzzy aggregation operator-based methods have been criticized because of reasons that include disregard for comprehensive correlative relationships of criteria and ignorance for combination of fuzzy measures of criteria and weights of positions. In this paper, the effectiveness of traditional aggregation operator-based MCDM techniques is improved by a novel method to select the best alternative(s) under the intuitionistic fuzzy environment. We begin by defining the Choquet integral-based intuitionistic fuzzy arithmetic aggregation (CIIFAA) operator, the remarkable properties of which are proved in details. Further, the Choquet integral based intuitionistic fuzzy hybrid arithmetic aggregation (CIIFHAA) operator is defined as well as the proofs of its properties. The CIIFHAA operator can not only capture the comprehensive correlative relationships of criteria in a simpler manner, but also combine the weights of positions. Then, we put forward a MCDM method based on CIIFHAA operator with intuitionistic fuzzy evaluations. An illustrative example is solved, the best alternative and the ranking of alternatives are obtained, which can verify the feasibility of the proposed method. Finally, two comparisons are conducted to illustrate the stability and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

• PHFLTSs are adopted to express epistemic uncertainty of group members for risk assessment. • MCDM methods by PHFLTSs namely SNA, MCM, BWM, MDM, and TOPSIS are used in the proposed FMEA model. • The subjective, objective and integrated weights of group members and risk factors are considered. • A case study with sensitive and comparative analyses is used to verify the proposed FMEA model. Failure mode and effects analysis (FMEA) usually requires multi-domain specialists to implement the group risk assessment for identifying and eliminating system failures. Therefore, this paper combines several multi-criteria decision making (MCDM) techniques with probabilistic hesitant fuzzy linguistic term sets (PHFLTSs) to implement risk assessment of failure modes by a panel of specialists. It aims at overcoming some defects existing in the conventional FMEA, such as without epistemic uncertainty and group risk assessment, as well as with some questions incurring from the risk priority number (RPN). Consequently, group members utilize PHFLTSs to express their subjective uncertain risk assessments on failure modes, in which the social network analysis (SNA) and maximizing consensus method (MCM) are exploited to derive the subjective and objective weights of group members respectively, afterwards their integrated weights are employed to aggregate individual risk assessments into the collective risk assessment. Additionally, the subjective and objective weights of risk factors are garnered by the best-worst method (BWM) and maximizing deviation method (MDM) separately, from which their integrated weights are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) so as to obtain the risk ranking of failure modes. Finally, an example with sensitive and comparative analyses is presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

• Strong correlation between an algorithm ranking results and PCA. • PCA is a valid quantitative approach to criteria weight assignment in group MCDM. • PCA is an excellent tool for personnel selection or hiring processes. Assignment of criteria weights during group multi-criteria decision-making (MCDM) processes is a challenging and time-consuming process often rife with subjectivity. This is particularly evident in hiring or personnel selection processes. Both the military and corporate aviation communities have required a large influx of pilots recently due to an increasing pilot shortage. This paper outlines a study conducted to evaluate the effectiveness of principal component analysis (PCA) as an objective weight assignment method to establish a rank order of United States Army pilots based upon their emotional intelligence, safety attitude, and safety citizenship scores. PCA results were compared to an algorithm tracking the rank order of pilots using every possible criteria weighting combination to test PCA's validity as a criteria weight assignment method. Regression analysis demonstrated a strong correlation (p < 0.000) between the algorithm ranking results and PCA's ranking results, reinforcing PCA's validity as a quantitative approach to criteria weight assignment in group MCDM applications. Implications of this study include increased efficiency and reduced subjectivity in group-based MCDM pilot selection processes. [ABSTRACT FROM AUTHOR]

• The modified stochastic grey correlation analysis model for multi-objective decision making. • Improved memetic meta -heuristic algorithm for acquiring high-quality non-dominated solutions. • The coordinated criteria weights (CWs) based on minimum deviation principle (MDP) • The quantified risk information propagating from uncertain inputs to decision making. The long-term multi-objective reservoir operation (LTMORO) involving multiple competitive goals such as energy, water supply and ecology protection is usually implemented under uncertain and changeable environment. Conventional methods usually obtain trade-off solutions and select most preferred one by multi-criteria decision making (MCDM) under deterministic environment. However, these methods are difficult to handle real-world reservoir system where multiple uncertainties exist in criteria performance values (PVs) and weights (CWs). In this paper, framework for solving reservoir operation and MCDM problem under various uncertainties is developed. A novel multi-objective method based on shuffled frog leaping algorithm (SFLA) is proposed to obtain high-quality trade-off solutions. Besides, hybrid utility forms based on TOPSIS, grey correlation analysis (GCA) or other models are impacted by subjectivity of combination coefficient, resulting in extra uncertainties for MCDM. To this end, the stochastic multi-criteria acceptability analysis (SMAA) model is developed by constructing new utility function based on SMAA-2 and modified GCA. The modified GCA helps to offset the uneven distribution problem in conventional version and enhance differentiation of utility function in SMAA-2. Moreover, deterministic CWs are obtained based on minimum deviation principle and two types of stochastic CWs following probability distributions are used to estimate CWs uncertainty. The risk caused by uncertain information propagating from PVs and CWs to decision results is also quantified. Finally, efficiency of established framework is tested by conducting three numerical experiments compared with deterministic GCA and SMAA-2. Results indicate that, compared with deterministic models, the novel framework provides decision maker with more reliable decision support and quantified risk information. The novel SMAA-GCA model produces relatively high probabilities for solutions to obtain their respective ranks compared with that of SMAA-2. It is effective to reduce impact of complex uncertainties on MCDM for LTMORO under stochastic environment. [ABSTRACT FROM AUTHOR]

Decision-making techniques are among important topics applied in operations research. Multi-Criteria Decision-Making (MCDM) is one of the very commonly used subjects of this issue. Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) is among the most utilized MCDM approaches in terms of both convenience and efficiency. To provide the reality and ease of application of these methods as much as possible Interval Type-2 Fuzzy Numbers (IT2FN)s, a special kind of type-2 fuzzy sets (T2FS)s, are frequently used together with MCDM methods. However, before the final stage of the solution to the problem, defuzzification techniques are applied for obtaining the optimum solution. Switching to crisp numbers before the last step of the procedure reduces the effectiveness of using the IT2FNs. The aim of this paper is to contribute to the literature with a new idea of fuzzy metric function whose result is again a fuzzy number. The result of this study will be a serious contribution to the literature with its metric function structure that is a fuzzy number. A metric function whose result will be a fuzzy set can also be made for different fuzzy structures. Also, a new partial order relation will be given for IT2FNs to define this fuzzy metric function. Moreover, this new fuzzy metric will be adapted to the TOPSIS method. Hence, it will be assured that the defuzzification operations are used only in the final sorting stage and the advantage of using fuzzy numbers will be able to continue in further steps. The advanced TOPSIS with a new fuzzy metric will be applied to a video chat program selection problem as an example. • A new fuzzy order relation has been defined. • A new fuzzy metric function with fuzzy output is defined. • More successful results were obtained by advancing the TOPSIS method in the literature. [ABSTRACT FROM AUTHOR]

• A new method for big social data analysis to reveal traveller' behavior is presented. • EM clustering approach is used for customers' segmentation. • Neural network combined with fuzzy logic is used for preference prediction. • The method uses HOSVD for data dimensionality reduction. • The method is evaluated on eco-friendly hotels data. Sustainable tourism is an emerging trend around the world. Eco-friendly (green) hotels are environmentally friendly properties that are becoming more popular among green travellers. Electronic Word-of-Mouth (e-WOM) is a method of communicating with customers to share their experiences and is a powerful marketing tool for hotel marketing. This paper investigates the role of online reviews of eco-friendly hotels for preference learning using multi-criteria decision-making and machine learning techniques. We develop a new method using multi-criteria decision making, supervised and unsupervised learning techniques. The Expectation-Maximization (EM) algorithm is used as an unsupervised learning technique to cluster travellers' online reviews. We use the Higher-Order Singular-Value Decomposition technique along with a similarity measure to find the most similar customers based on their preference. To predict travellers' preference for eco-friendly hotels, we employ a neuro-fuzzy system, the Adaptive Neuro-Fuzzy Inference System, as a supervised learning technique. To select the most important criteria, we use the entropy-weight approach in each segment. Several experiments were performed on the collected data from the Czech Republic's eco-friendly hotels on the TripAdvisor platform. The results demonstrated that the hybrid approach is effective for customers' segmentation, and preference learning and prediction in eco-friendly hotels. [ABSTRACT FROM AUTHOR]