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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]

Decision-making problem is a fundamental activity in human life every day. Regarding group decision-making, individuals have insufficient comprehension of the decision objects owing to the limited experience and knowledge and drawback the evaluation process. Therefore, the evaluation information is missing and the group cannot get the precise information for decision objects resulting in the generation of incomplete information systems. It is meaningful for individuals finding an approach to Leverage and tap into existing information. On the basis, the work presented in this paper focuses on group decision-making problems (GDM) with incomplete information. Under the framework of group decision-making, a GDM approach with an incomplete information system is presented combining the formal concept analysis and rough set theory. Firstly, we propose a concept called formal decision context and discuss the properties of the formal decision context, which could divide the incomplete information system into many cognitive units, and the definitions of cognitive units will be given. Then, the property and the relations of various cognitive units will be discussed. Additionally, the upper and lower approximation space under the formal decision context is founded considering the risk attitudes integrating fuzzy rough set and formal concept context. Considering the order relation of various cognitive units and decision objects, we introduce the dominance relation and the importance degree of each cognitive unit under the framework of formal decision context. Furthermore, a theoretical framework of the multi-criteria decision-making problem is presented and two models for the multi-criteria group decision-making problem are discussed combined with fuzzy rough set and formal decision context from two perspectives, including qualitative analysis and quantitative analysis. Finally, an illustrated example is applied to verify the validity and effectiveness of the proposed models. The contributions of this paper include two aspects. On the one hand, this paper provides a new perspective and paradigm considering MCGDM problems with the incomplete information system from the perspective of group consensus. On the other hand, this paper gives two approaches for group decision-making problems from sorting and set theory by the perspective of multi-granular rough set and group consensus. • A model of formal decision context with incomplete information to describe the cognitive situation. • An approach to MCDM problem combined rough set with formal decision context with incomplete information. • Two approaches to MCGDM problem from qualitative analysis and quantitative analysis. [ABSTRACT FROM AUTHOR]

Over the last twenty years, patient opinions of the received healthcare services have emerged as a crucial quality indicator and a valuable instrument for evaluating the service quality of health organizations. However, many surveyors still utilized uncertain information for conducting this evaluation process. Whereas, in the past few years, the issues of inconsistency, indeterminacy, and uncertainty in data have been growing and have become a matter of concern in dealing with decision-making issues. This paper proposes an improved RAFSI method based on a single-valued trapezoidal neutrosophic number (SVTraNN) as a multi-attribute framework for dealing with a healthcare service quality evaluation. The proposed framework consists of two features: a feature for determining numerical criteria weights involving the procedures of the Ordinal Priority Approach (OPA) and a feature for selecting the best polyclinic through contributing our new SVTraNN Harmonic and arithmetic mean to yield criterion functions in the RAFSI method. This paper then compares the results obtained by the proposed method with other SVTraNN decision-making methods. A comparative analysis and rank reversal testing of the improved method demonstrated that the multi-attribute framework proffers the option of capability and stability in overcoming inconsistency, indeterminacy, and uncertainty information efficiently during the healthcare service quality evaluation. [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]

Smart cities are technological settlements using the collected data to utilize resources and services effectively by combining information and communication technologies with various tools connected to the internet of things network. Sustainable energy systems in smart cities are systems can be evaluated by using multiple criteria decision making methods or methodologies based on several vague/imprecise evaluation criteria. In this paper, sustainable energy systems in smart cities are evaluated by interval-valued Pythagorean fuzzy (IVPF) sets with an integrated optimization based multi-expert fuzzy Best Worst Method (BWM) and TOPSIS methodology that can better handle uncertainty and vagueness in experts' linguistic assessments than existing methodologies. The considered criteria are weighted by multi-expert IVPF Best Worst Method, which has become a popular weighting method in recent years. Later, the energy alternatives for a real case study are prioritized by multi-expert IVPF TOPSIS method. In the analysis, the most important criterion is found as Environmental sustainability (C1) with the defuzzified weight of 0.218 while the other weights are as initial investment (C2) with 0.196, operating expenses (C3) with 0.163, technical feasibility (C4) with 0.154, social acceptability (C5) with 0.140, and scalability (C6) with 0.129. The obtained results indicate that "Investing in advanced technologies" in a smart city with relative degree of closeness (RDC) value of 0.798, has been determined as the best alternative among the considered five alternatives. It is closely followed by "Developing a transportation system" with the RDC value of 0.681. Sensitivity analysis shows that the ranking results are quite robust and reliable. The comparative analysis with crisp BWM and TOPSIS methodology is applied to check the validity of the proposed methodology. [ABSTRACT FROM AUTHOR]

Using multi-criteria decision making (MCDM) technique to rank alternatives is a well-known area of study in which aggregation operators, such as ordered weighted averaging (OWA) and induced OWA (IOWA) operators, play an important role in merging information and producing an overall ranking. The distance measures from ideal argument values in aggregation operators have gained attention in recent literature. Distance measures are traditionally used as argument variables, which leads to the depiction that the attribute cannot be aggregated directly. In this paper, a generalized form of distance-induced OWA (DIOWA) operators is proposed with distance measures used as order-inducing variables. A distinctive benefit of DIOWA operators is that they permit us to consider ideal argument values while simultaneously also taking the attribute values as argument variables. Three variants of DIOWA operators are proposed and investigated, namely a) the Hamming distance-induced OWA (HDIOWA) operator, b) the normalized Hamming distance-induced OWA (NHDIOWA) operator, and c) the weighted Hamming distance-induced OWA (WHDIOWA) operator. We highlight their important properties and provide proofs to necessary theorems, and also suggest the determination methods for calculating their associated weights. We discuss further extensions of the proposed DIOWA operators with the help of generalized and quasi-arithmetic means. We discuss the use of our proposed family of operators for two different decision making situations, and demonstrate their validity by an illustrative numerical example. Finally, we apply the proposed operators to a real-life problem of ranking Chinese provinces for their science and technology (S&T) development levels. The proposed operators are shown to be a useful addition to the aggregation toolbox for decision analysts. [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]

Highlights • MCDM has been useful to overcome the choice problem in mining and mineral processing. • AHP has been the most used MCDM technique in this area of research. • A large proportion of the published papers make use of hybrid methods. • This is the first review on the use of MCDM for the choice problem in this area. Abstract Despite the fact that the potential of multi-criteria decision making (MCDM) to overcome a variety of problems in mining and mineral processing has been widely recognised, no literature review in these fields has been conducted. This manuscript addresses this issue by providing a comprehensive overview of the applications and trends of MCDM methods for the choice problem (i.e., determining the best option from a set) in mining and mineral processing. 90 articles published between 1999 and 2017 were selected following a searching methodology and eligibility criteria detailed in this manuscript. In addition, for the purpose of the survey, different types of selection problems were identified. The results show that there are two phases of growth in the application of MCDM techniques to the choice problem in mining and mineral processing. The first phase, from 1999 to 2007, shows a very low number of publications with only a moderate increase by the end, whereas the second phase, from 2007 to 2017, shows a significant growth in the number of published articles. The review also shows that the most addressed problem has been the selection of mining methods, while the Analytical Hierarchy Process (AHP) has been the most used MCDM method. The rise in the application of hybrid MCDM methods is also discussed. This review paper provides insight into the current state of applications of MCDM in mining and mineral processing and discusses pathways for future research directions in the development of MCDM methods that would benefit these fields. [ABSTRACT FROM AUTHOR]

Highlights • Supply chain performance indicators evaluation is Multi-Criteria Decision-Making problem. • Criteria values are determined in interval valued fuzzy linguistic. • An integrated version of QFD and grey relational analysis. • The investigation carried out under a European Commission project. Abstract Objective This paper proposes a multi attribute decision support model in a supply chain in order to solve complex decision problems. The paper provides a platform to ease decision process through the integration of quality function deployment (QFD) and grey relational analysis (GRA) in demonstrating main supply chain drivers under fuzzy environment. Methodology The proposed method is important because of several points: First of all, in a supply chain system, evaluation factors are not really independent and must be addressed in relation to the external factors such as customer requirements. Hence, we have applied QFD tool. Second, due to the constant uncertainty in the supply chain environment, fuzziness among the factors has to be considered. So, an interval valued fuzzy model was implemented. Third, to examine the proposed decision system in reality, it was applied in R isk and U ncertain C onditions for A griculture P roduction S ystems (RUC-APS) project. Contribution An integrated version of QFD and GRA is presented. It is assumed that QFD can act to measure optimal solutions based on the distance to ideal solutions. In an interval-valued fuzzy environment the enormous volume of computation by Euclidean distance doesn't allow decision makers to obtain the results easily. This drawback is addressed using gray relational analysis. The gray relational coefficient is integrated to the fuzzy QFD to measure the distance of potential solutions from ideal solutions. This integration facilitates decision making process in further problems once big data are available. Results To obtain the importance degrees of logistic indicators in the supply chain, expert team considered the environmental, social & cultural, and economic factors as external dimension of the QFD. The other dimension of QFD includes supply chain drivers such as quality, environmental management system, supply chain flexibility, corporate social responsibility, transportation service condition, and financial stability. The decision model is solved and the ranking of indicators is achieved. A sensitivity analysis helps to test and check the performance of the decision model. [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]

• The applications of decision making techniques in healthcare and medical industry are reviewed. • We evaluated 202 published studies selected from 85 high-ranking journals. • Selected studies were classified into nine application areas. • AHP and fuzzy AHP techniques were the most frequently used decision-making technique. Decision making techniques have been widely used in healthcare and medical industry. This study systematically reviews the conventional and fuzzy decision-making techniques applied in healthcare and medical problems. In total, we evaluated 202 published studies selected from 85 high-ranking journals, in which 130 of those published studies were directly related to the decision-making processes in healthcare and medical issues. Selected studies were classified into nine application areas: environmental sustainability, waste management, service quality, risk management, medical equipment and material selection, health technology, operation researches in healthcare, hospital healthcare services and other application areas. Furthermore, we classified the selected studies by numerous significant perspectives such as the authors, application areas, utilized techniques, publication time periods, decision-making and study goals, and significant results of the papers. The statistical results show that the year 2012 was ranked first in terms of developed studies within the period of 1989–2018. The techniques of AHP and hybrid approaches were the most frequently implemented decision-making technique in healthcare fields. Notably, the results also show that decision-making approaches and techniques are mostly applied to evaluate and rank different applications of service quality in healthcare and medical industries. [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]

As a practical popular methodology for dealing with fuzziness and uncertainty in Multiple Criteria Decision-Making (MCDM), Fuzzy AHP (FAHP) has been applied to a wide range of applications. As of the time of writing there is no state of the art survey of FAHP, we carry out a literature review of 190 application papers (i.e., applied research papers), published between 2004 and 2016, by classifying them on the basis of the area of application, the identified theme, the year of publication, and so forth. The identified themes and application areas have been chosen based upon the latest state-of-the-art survey of AHP conducted by [Vaidya, O., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research, 169(1), 1–29.]. To help readers extract quick and meaningful information, the reviewed papers are summarized in various tabular formats and charts. Unlike previous literature surveys, results and findings are made available through an online (and free) testbed, which can serve as a ready reference for those who wish to apply, modify or extend FAHP in various applications areas. This online testbed makes also available one or more fuzzy pairwise comparison matrices (FPCMs) from all the reviewed papers (255 matrices in total). In terms of results and findings, this survey shows that: (i) FAHP is used primarily in the Manufacturing, Industry and Government sectors; (ii) Asia is the torchbearer in this field, where FAHP is mostly applied in the theme areas of Selection and Evaluation; (iii) a significant amount of research papers (43% of the reviewed literature) combine FAHP with other tools, particularly with TOPSIS, QFD and ANP (AHP’s variant); (iv) Chang’s extent analysis method, which is used for FPCMs’ weight derivation in FAHP, is still the most popular method in spite of a number of criticisms in recent years (considered in 57% of the reviewed literature). [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]

Highlights • We propose a new VIKOR method that incorporates incomplete criteria weights. • We can express our preferences about criteria effectively via incomplete criteria weights. • The proposed VIKOR method ranks alternatives using the extreme points of the weights. • We newly re-interpret VIKOR method in terms of decision making under uncertainty. Abstract Multiple criteria decision making (MCDM) methods provide an effective means of assisting decision makers to choose the best alternative given multiple criteria. The VIKOR method, among others, is characterized by a trade-off between the maximum group utility of the majority (aggregation of all criteria) and the minimum individual regret of the opponent (each of criteria). In this paper, we propose a new VIKOR method that makes use of incomplete criteria weights instead of previous weighting methods such as entropy for objective weights, and equal, analytic hierarchy process (AHP), or fuzzy for subjective weights. The proposed VIKOR method ranks alternatives using the aggregated scores of alternatives computed by multiplying the extreme points of a set of criteria weights by the precise or interval consequences of alternatives. Further, we show that the VIKOR method combines both the expected opportunity loss and the minimax regret by reinterpreting it in terms of a decision making under uncertainty (DMUU) approach. Therefore, the VIKOR method, a well-known MCDM method, can be used for prioritizing strategies under states of nature. [ABSTRACT FROM AUTHOR]

Highlights • Interval- valued intuitionistic hesitant fuzzy entropy and VIKOR techniques are proposed. • The preferred alternative by IVIHF- VIKOR method is closer to the ideal solution. • The IVIHF entropy method utilized to calculate the importance of the criteria. Abstract In this paper, we proposed interval valued intuitionistic hesitant fuzzy entropy for determine the importance of the criteria and interval valued intuitionistic hesitant fuzzy VIKOR method for ranking the alternatives. Industrial Robots are utilized to perform complicated and hazardous tasks accurately and also used to enhance the quality and efficiency of the work. Selecting an industrial robot for performing a particular task depends on the work and the associated criteria of the robot. The materials handled by the robots are different like powdered, adhesive, bulky, brittle etc. In this manner, choosing a suitable robot from the set of available industrial robots to handle a particular material is a challenging task. To get a more conscionable decision result, a decision organization contains a lot of decision makers. The interval- valued intuitionistic hesitant fuzzy set is utilized as a competent mathematical tool for enunciate individuals hesitant thinking. An interval- valued intuitionistic hesitant fuzzy set (IVIHFS) concedes a set of several possible interval- valued intuitionistic fuzzy membership and non- membership values. Finally, the proposed interval- valued intuitionistic hesitant fuzzy entropy and VIKOR techniques utilized for industrial robot selection. [ABSTRACT FROM AUTHOR]

Highlights • Using several evidence for determining the state of a transaction (fraud or not fraud). • Declaring the problem of uncertainty in fraud detection. • Using Multi Criteria Decision Making method and intuitionistic fuzzy method for the fusion of fraud evidence. • Getting better results compared with Dempster–Shafer fusion method. Abstract Detection of fraudulent transactions is a vital factor for financial institutions, and finding more effective and accurate methods is of tremendous importance. The use of supervised data mining techniques is not feasible in many cases due to the lack of access to labeled data. Fraud detection is a complex task, and unsupervised methods like clustering and outlier detection techniques employed alone do not yield satisfactory results. Another issue is epistemic uncertainty due to the absence of sufficient information on the behavioral aspects of different customers, which also leads to poorer results for fraud detection and makes the fraud detection system inapplicable in real world environment. In this paper, using multi criteria decision method, intuitionistic fuzzy set, and evidential reasoning, a new method for detection of fraud was introduced, which infuses several behavioral evidence of a transaction concerning the effect of uncertainty for them. Transactional behavior was modeled by considering the trends of different main and aggregated variables at different periods and the extent of deviation of the new arrived transaction from each of these trends were considered as behavioral evidence. The final belief, which is the result of the combination of much evidence using the proposed method, will determine the originality of a newly arrived transaction. Finally, using a real world dataset, the results of the new method were compared with the results of Dempster–Shafer method in terms of the number of frauds discovered and the number of erroneous alerts they issued. The findings showed that the method introduced in this study has higher accuracy and lower false alarms compared to Dempster–Shafer method while the computational complexity of this method makes its implementation time longer. [ABSTRACT FROM AUTHOR]

Highlights • A data mining method for topology optimization is proposed. • The method sequentially applies clustering and association rule analysis. • A distance measure for pairs of configurations is defined. • Confidence and lift indexes in multiobjective topology optimizations are defined. • The method is applied in 3- and 4-objective design problems in 2 and 3 dimensions. Abstract Optimum design problems, including structural optimization problems, often include multiple objectives. A multiobjective optimization problem usually provides a number of optimal solutions, called non-dominated solutions or Pareto-optimal solutions. In multiobjective topology optimization scenarios, decision makers face the challenging task of choosing the most effective solution that meets their needs; serial comparisons among a set of Pareto-optimal solution are cumbersome, as are trial-and-error attempts to find an appropriate solution among a host of alternatives. On the other hand, the recent integration of data mining techniques in multiobjective optimization methods can provide decision makers with important, highly pertinent, and useful knowledge. In this paper, we propose a data mining technique for knowledge discovery in multiobjective topology optimization. The proposed method sequentially applies clustering and association rule analysis to a Pareto-optimal solution set. First, clustering is applied in the design space and the result is then visualized in the objective space. After clustering, detailed features in each cluster are analyzed based on the concept of association rule analysis, so that characteristic substructures can be extracted from each cluster of solutions. In four numerical examples, we demonstrate that the proposed method provides pertinent knowledge that aids comprehension of the key substructures responsible for one or more desired performances, thereby giving decision makers a useful tool for discovery of particularly effective design solutions. [ABSTRACT FROM AUTHOR]

Highlights • This is the first application of Pareto-optimization on real data in AHP. • Methodological background of Pareto test is introduced. • Full description of the application procedure is provided. • Conclusions are summarized and suggestions are made to other potential appliers. • Results are directly disposable for public transport development decisions. Abstract Recent findings in operations research and decision theory show that the eigenvectors derived from pairwise comparison matrices are not Pareto optimal in all cases. Thus, looking at the traditional eigenvector method of Saaty, a better approximation may exist for characterizing the decision makers' opinion. The Pareto optimal modified vector may lead to better decisions for better expressing the real intention of the evaluator. This paper aims to examine Pareto-optimality on real data pairwise comparison matrices gained from a public transport Analytic Hierarchy Process (AHP) survey. Moreover, detecting the impact of weight score modification on the whole AHP structure and thus the significance of Pareto test is also in the scope of this study. Further, a detailed description of the general process of Pareto optimal AHP is also included. The application has been conducted in Mersin, Turkey with the purpose of determining public preference on the importance of developing supply quality elements in local bus transportation service. [ABSTRACT FROM AUTHOR]

Highlights • A new decision-making methodology called Simulated Uncertainty Range Evaluations (SURE) for handling uncertainty is proposed. • A real-world case study for a pharmaceutical company is used to test the methodology. • Only SURE correctly identified the alternative eventually chosen by the company. Abstract Managerial decision-making often involves the consideration of multiple criteria with high levels of uncertainty. Multi-attribute utility theory, a primary method proposed for decision-making under uncertainty, has been repeatedly shown to be difficult to use in practice. This paper presents a novel approach termed Simulated Uncertainty Range Evaluations (SURE) to aid decision makers in the presence of high levels of uncertainty. SURE has evolved from an existing method that has been applied extensively in the pharmaceutical and speciality chemical sectors involving uncertain decisions in whole process design. The new method utilises simulations based upon triangular distributions to create a plot which visualises the preferences and overlapping uncertainties of decision alternatives. It facilitates decision-makers to visualise the not-so-obvious uncertainties of decision alternatives. In a real-world case study for a large pharmaceutical company, SURE was compared to other widely-used methods for decision-making and was the only method that correctly identified the alternative eventually chosen by the company. The case study demonstrates that SURE can perform better than other existing methods for decision-making involving multiple criteria and uncertainty. [ABSTRACT FROM AUTHOR]

Best Worst Method (BWM) has recently been proposed as a method for Multi Criteria Decision Making (MCDM). Studies show that BWM compared with other methods such as Analytic Hierarchy Process (AHP), leads to lower inconsistency of the results while reducing the number of required pairwise comparisons. MCDM methods such as BWM require accurate information. However, it often happens in practice that a level of uncertainty accompanies the information. The main aim of this paper is to address this problem and provide an integration of BWM and Z-numbers, namely ZBWM. Providing BWM with Z-numbers enables the BWM method to handle the uncertainty of information of a multi-criteria decision. Additionally, the capabilities of the proposed method in the process of utilizing the linguistic information dealing with big data are highlighted. The proposed method is examined to address a supplier development problem. By experimental results, we show that ZBWM results lower inconsistency when compared with BWM. A Z-number contains subjectivity in its fuzzy part, which can be addressed in future applications of ZBWM. [ABSTRACT FROM AUTHOR]

The strategic supplier-related activity of supplier segmentation focuses on the evaluation of suppliers, identifying different approaches, identifying the most suitable criteria and proper methods to segment the suppliers. The main aim of the evaluation of suppliers is to form different groups from the selected suppliers to create different supplier management strategies for segments involved. Supplier development is another strategic supplier-related activity designed to upgrade the performance level of suppliers in order to create and maintain a network of competent suppliers, which has a major influence on the competitive advantages of a buying company. To allocate scarce resources more efficiently, we should design different supplier development strategies for different supplier segments. This is where we actually use the evaluation for suppliers. This paper proposes an integrative approach that includes capabilities and willingness as two dimensions for evaluating and subsequently segmenting suppliers. The results of that segmentation are then used as the main basis for supplier development. The integrative approach proposed in this paper is of significant importance, as it helps companies apportion their managerial resources more efficiently. We use a new multi-criteria decision-making method called Best Worst Method (BWM) to segment suppliers. A supplier development conceptual model is proposed to develop the suppliers in the different segments. The proposed framework is further applied to a medium-sized high-tech company as input to validate the model. [ABSTRACT FROM AUTHOR]

Supplier selection in supply chain is critical strategic decision for organization’s success and has attracted much attention of both academic researchers and practitioners. Supplier selection problem consists of stochastic and recognitive uncertainties. However, the requirement of large sample size and strong subject knowledge to build suitable fuzzy membership function restrict the applicability of probability and fuzzy theories in supplier selection problem. In response, this study proposed a new tool for supplier selection. In this paper, we applied the combination of grey system theory and uncertainty theory which neither requires any probability distribution nor fuzzy membership function. The objective of this paper is to develop framework for reducing the purchasing risks associated with suppliers. The proposed supplier selection method not only selects the most appropriate supplier(s) but also allocate optimal purchase quantity under stochastic and recognitive uncertainties. An example is shown to highlight the procedure of the proposed model at the end of this paper. [ABSTRACT FROM AUTHOR]

Safety evaluation is an important and challenging issue in many industries and is also a key component of risk management. Various evaluation methods have been proposed to make safety evaluation more consistent and objective. However, a major concern is that many existing safety evaluation measurements are still subjective and are not easy to obtain in a uniform way. This paper aims to develop a framework suitable for evaluating the safety performance at organizational or project levels in a comprehensive way that may be expected to reflect all risk assessment aspects and make best use of professional talent and experiences from different evaluators. In this paper, a structural evaluation logic is proposed, based on an improved grey clustering method. First, a grey clustering-based indicator system is developed to avoid the arbitrary selection of indicators. Then, a novel interval-grey-number reciprocal-judgment-matrix based AHP (GRAHP) is proposed that extends the classical analytic hierarchy process to deal with the possible contradictory opinions from experts in different fields as well as the standardization problem of collected independent and uncertain data in evaluation. Additionally, the transformation and optimization method of the new proposed grey hierarchy analysis model are given. Finally, an improved grey variable weight clustering evaluation model is built based on the above described methods. We illustrate the practical implementation of the proposed methods using actual aviation data from China. The results show that the proposed framework and methods have good ability in safety evaluation for large and complex engineering projects. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases can wreak havoc on human beings and lead to 30% of global death annually. The World Health Organization has always highlighted that there is a severe shortage of medical personnel, especially cardiologists, in most of the countries. In this paper, an electrocardiogram health identifier (ECGHI) has been proposed and developed for swift identification of heart diseases. The ECGHI has been applied to four most common types of cardiovascular diseases, namely Myocardial Infarction, Dysrhythmia, Bundle Branch Block and Heart Failure since these four types of cardiovascular diseases contribute to 25% of the overall population suffering from heart diseases. In the investigation of ECGHI, the binary classifier (BC) and multi-class classifier (MCC) are designed and analyzed. The MCC features a multi-class support vector machine (SVM) to diagnose the exact type of cardiovascular disease. The BC features a two-class SVM to identify healthiness of heart accurately. In this paper, the following indicators have been investigated, namely the overall accuracy, specificity, sensitivity, the dimensionality of feature vector, the total training and testing time of ECGHI and a newly defined confidence index. These six criteria form the basis to derive an analytic hierarchy process (AHP) to facilitate the multiple criteria decision making (MCDM) for the optimal evaluation of hyperplanes. Four kernels have been analyzed from which both the BC and MCC are evaluated and analyzed. The optimized ECGHI using BC yields an AHP Performance Score of 0.079 with score components (overall accuracy, specificity, sensitivity, average confidence index, dimensionality, total time for training and testing time) of 0.982, 0.978, 0.986, 0.608, 6, and 5.77 s respectively. Likewise, the optimized ECGHI using MCC yields an AHP Performance Score of 0.093 with score components of 0.882, 0.89, 0.874, 0.504, 9, and 7.32 s respectively. The BC is employed as a supplement of the MCC to achieve a further improvement in all six criteria. Such a novel process of identification and detection with high accuracy is referred as the MCC–BC scheme. The developed ECGHI (MCC) may identify the FOUR most common and important cardiovascular diseases simultaneously (with BC supplementing the MCC to achieve a high accuracy). Such simultaneous identification of cardiovascular diseases is the first of its kind in this research area, so no comparison can be made. The MCC–BC scheme will pave the way for speedy and accurate identification and detection of heart disease. The instant response of the ECGHI minimizes the probability of death from Myocardial Infarction, Bundle Branch Block, Dysrhythmia, and Heart Failure. [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]

The contrivance of this paper is two-dimensional. The first is to formulate an extended Dombi-Hamy mean operator in an interval-valued neutrosophic environment for assembling inter-related multiple parameters. The second one is to develop a new ranking procedure named as GLF ranking by introducing the concept of Gaussian likelihood function for solving multi-criteria decision-making problems in an interval-valued neutrosophic circumstance. Also, we have proposed a novel algorithm named Gaussian likelihood function based algorithm or GLF algorithm in solving multi-criteria decision-making problem from the field of medical science to find out the vital cause responsible for the occurrence of cancer in India. Finally, we have verified the flexibility and etiquette of GLF method by comparing it with the existing multi-criteria decision-making methods such as Aiwu's method and Jun Ye's method. • The formulation of the Gaussian likelihood function and its characteristics. • Research into a new GLF ranking system has been done. • The Dombi–Hamy mean operator is an aggregation operator that has been created. • A brand-new GLF strategy has been developed to address practical MCDM issues. [ABSTRACT FROM AUTHOR]

MCDM is considered as a complex decision-making tool involving both quantitative and qualitative factors. In recent years, several fuzzy FMCDM tools have been suggested to choosing the optimal probably options. The purpose of this paper is to review systematically the applications and methodologies of the fuzzy multi decision-making (FMCDM) techniques. This study reviewed a total of 403 papers published from 1994 to 2014 in more than 150 peer reviewed journals (extracted from online databases such as ScienceDirect, Springer, Emerald, Wiley, ProQuest, and Taylor & Francis). According to experts’ opinions, these papers were grouped into four main fields: engineering, management and business, science, and technology. Furthermore, these papers were categorized based on authors, publication date, country of origin, methods, tools, and type of research (FMCDM utilizing research, FMCDM developing research, and FMCDM proposing research). The results of this study indicated that, in 2013, scholars have published papers more than other years. In addition, hybrid fuzzy MCDM in the integrated method and fuzzy AHP in the individual section were ranked as the first and second methods in use. Additionally, Taiwan was ranked as the first country that contributed to this survey, and engineering was ranked as the first field that has applied fuzzy DM tools and techniques. [ABSTRACT FROM AUTHOR]

This paper presents the application of the new DEMATEL–MABAC model in the process of making investment decisions on the acquisition of manipulative transport (Forklifts) in logistics centers. The DEMATEL method was used to obtain the weight coefficients of criteria, on the basis of which the alternatives were evaluated. The selection of criteria for evaluating Forklifts was based on an analysis of available literature. The evaluation and selection of Forklifts was carried out using a new multi-criteria method – the MABAC ( M ulti- A ttributive B order A pproximation area C omparison) method. This paper presents a practical application and a sensitivity analysis of the MABAC method. The sensitivity analysis was conducted in three stages. In the first stage, a stability analysis was carried out on the solution reached by the MABAC method, depending on changes made to the weights of the criteria. In the second and third stages, a consistency analysis of the results from the MABAC method was carried out depending on both the changes in the measurement units in which the values of individual criteria are presented and on the formulation of the criteria. The SAW, COPRAS, TOPSIS, MOORA and VIKOR methods were tested under the same conditions. Based on the results obtained, it was shown that the SAW, COPRAS, TOPSIS, MOORA and VIKOR methods do not meet one or more of the conditions set, while the MABAC method showed stability (consistency) in its solutions. Through the research presented in this paper, it is shown that the new MABAC method of multi-criteria decision-making is a useful and reliable tool for rational decision-making. [ABSTRACT FROM AUTHOR]

This paper formulates supplier evaluation and selection as a multi-criteria decision-making (MCDM) problem with subjective and fuzzy preferences of decision makers over evaluation criteria. As an outcome, this paper provides decision makers with a decision support system that presents the Pareto fronts, a set of best possible high-quality suppliers and optimized business operation levels from such suppliers. In addition, this paper quantifies the importance of the agility criterion and its sub-criteria in the process of evaluating and selecting agile suppliers by measuring the magnitude of bullwhip effect and inventory costs. The proposed system uses a fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) to successfully determine the priority weights of multiple criteria and selects the fittest suppliers by taking the vagueness and imprecision of human assessments into consideration. More importantly, it presents approximated Pareto fronts of the resulting supplier chains for varying priority weights of the agility criterion and its sub-criteria. Finally, we compare business costs of agile and non-agile supply chains before and after reconfigurations of original supply chains in response to unexpected disruptions under two order allocation strategies, a skewed order allocation (SOA) strategy and an even order allocation (EOA) strategy. [ABSTRACT FROM AUTHOR]

City logistics (CL) tends to increase efficiency and mitigate the negative effects of logistics processes and activities and at the same time to support the sustainable development of urban areas. Accordingly, various measures and initiatives are applying and various conceptual solutions are defining. The effects vary depending on the characteristics of the city. This paper proposes a framework for the selection of the CL concept which would be most appropriate for different participants, stakeholders, and which would comply with attributes of the surroundings. CL participants have different, usually conflicting goals and interests, so it is necessary to define a large number of criteria for concepts evaluation. On the other hand, the importance of the criteria is dependent on the specific situation, i.e., a large number of factors describing the surroundings. In situations like this, selecting the best alternative is a complex multi-criteria decision-making (MCDM) problem consisting of conflicting and uncertain elements. A novel hybrid MCDM model that combines fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), fuzzy Analytical Network Process (ANP) and fuzzy Višekriterijumska Optimizacija i kompromisno Rešenje (VIKOR) methods is developed in this paper. The model provides support to decision makers (planners, city administration, logistics service providers, users, etc.) when selecting the CL concept, which is successfully performed in this paper for the City of Belgrade. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Generalised-Fuzzy-TOPSIS method as a versatile evaluation model. The model is suitable for different types of fuzzy or interval-valued numbers, with or without subjective weights of criteria being defined by evaluators. Additionally, we extend the final ranking step of the TOPSIS method, which is the calculation of closeness coefficient based on the separation from Negative Ideal Solution (NIS) and proximity to Positive Ideal Solution (PIS). Experiments show that with the same focus on PIS and NIS distances, our proposed ranking is identical to TOPSIS, and also performs very well when varying the distance weights. The applicability of the proposed method is demonstrated with relevant examples of technology and material selection in the context of additive manufacturing. Sensitivity analyses, based on subjective weights of criteria, degree of optimism, evaluators’ weights in group decision making, and distance weights, are presented to assist managers in making more informed decisions. [ABSTRACT FROM AUTHOR]

Real world decision making problems under uncertainty face different challenges. These challenges include lack of information, the necessity of quick decisions, and problems may change across time. When the uncertainty involved is due to fuzziness and vagueness, the use of fuzzy linguistic information can facilitate the elicitation of decision makers’ preferences of alternatives by allowing assessment of alternatives in unipolar scales. However, in some cases decision makers need to express negative, positive and neutral attitudes that cannot be modeled by unipolar scales. This paper aims at developing a dynamic linguistic multi-criteria decision making model dealing with bipolar linguistic scales in which both alternatives and criteria may vary across time. In order to consider the historical evolution of alternatives leading up to the current assessments, a fusion process based on transformation functions and uninorm aggregation operator is proposed to deal with the membership functions of bipolar linguistic assessments. Finally the performance of this model is compared with a non-dynamic method in a supplier selection problem. [ABSTRACT FROM AUTHOR]

Existing evolutionary approaches to automatic composition generate only a few melodies in a certain style that is specified by the setting of parameters or the design of fitness functions. Thus, their composition results cannot cover the various tastes of music. In addition, they are not able to deal with the multidimensional nature of music. This paper presents a novel multi-objective evolutionary approach to automatic melody composition in order to produce a variety of melodies at once. To this end, two conflicting fitness measures are investigated to evaluate the fitness of melody; (1) stability and (2) tension . Resorting to music theory, genetic operators (i.e., crossover and mutation) are newly designed to improve search capability in the multi-objective fitness space of music composition. The experimental results demonstrate the validity and effectiveness of the proposed approach. Moreover, the analysis of composition results proves that the proposed approach generates a set of pleasant and diverse melodies. [ABSTRACT FROM AUTHOR]

This paper proposes an intuitionistic fuzzy decision method based on prospect theory and the evidential reasoning approach, aiming at analyzing multi-attribute decision making problems in which the criteria values are intuitionistic fuzzy numbers and the information of attributes weights is unknown. Firstly, the measures of entropy and cross entropy are defined for intuitionistic fuzzy sets by taking into consideration the preference of decision maker towards hesitancy degree. Secondly, combined with bounded rationality, the prospect decision matrix is calculated in the light of prospect theory and intuitionistic fuzzy distance. Thirdly, the correlational analyses are conducted between the attribute weights and three indicators which are entropy, cross entropy and prospect value, and optimization models for identifying attribute weights are built under the circumstances that the weights are incomplete and unknown. Finally, in order to avoid the loss of decision making information, the evidential reasoning approach is applied to the calculation of comprehensive prospective values for all alternatives. Following the value calculation, the ranking and the optimal alternative are determined based on the comprehensive prospective values. Illustrating examples demonstrate that the proposed method is reasonable and feasible. [ABSTRACT FROM AUTHOR]