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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]

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

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]

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]

This paper introduces a novel Integrated Interval Rough Pivot Pairwise Relative Criteria Importance Assessment (IRN PIPRECIA) model combined with Interval Rough Combined Compromise Solution (IRN CoCoSo), marking a significant advancement in sustainable traffic flow management for commercial vehicles. This innovative merger is a first in literature, methodologically enhancing the evaluation of road sections based on critical parameters including passenger car equivalent (PCE) 85%, AADT, road conditions, and accident data. Our model systematically fills the research gap in holistic traffic performance analysis, providing a unique tool for prioritizing road safety and efficiency. The key scientific contribution is the model's ability to integrate causal and consequential traffic factors into a single framework, offering a novel multi-criteria decision-making (MCDM) approach. Results, validated through various verification models, show the integrated model's effectiveness in real-world scenarios, confirming its robustness and stability. With strong engineering application potential, our work supports urban planners and traffic managers in making informed, sustainable decisions. The model extends beyond traditional traffic analysis, promising a shift towards more adaptive, data-driven infrastructure management. Future research will aim to refine the criteria basis and explore real-time decision-making through advanced MCDM applications. [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]

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]

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]

• 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]

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]

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]

Abstract: Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study. [Copyright &y& Elsevier]

• 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]

• 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]

In this paper, we present a novel approach for computing the Pareto frontier in Multi-Objective Markov Chains Problems (MOMCPs) that integrates a regularized penalty method for poly-linear functions. In addition, we present a method that make the Pareto frontier more useful as decision support system: it selects the ideal multi-objective option given certain bounds. We restrict our problem to a class of finite, ergodic and controllable Markov chains. The regularized penalty approach is based on the Tikhonov’s regularization method and it employs a projection-gradient approach to find the strong Pareto policies along the Pareto frontier. Different from previous regularized methods, where the regularizator parameter needs to be large enough and modify (some times significantly) the initial functional, our approach balanced the value of the functional using a penalization term ( μ ) and the regularizator parameter ( δ ) at the same time improving the computation of the strong Pareto policies. The idea is to optimize the parameters μ and δ such that the functional conserves the original shape. We set the initial value and then decrease it until each policy approximate to the strong Pareto policy. In this sense, we define exactly how the parameters μ and δ tend to zero and we prove the convergence of the gradient regularized penalty algorithm. On the other hand, our policy-gradient multi-objective algorithms exploit a gradient-based approach so that the corresponding image in the objective space gets a Pareto frontier of just strong Pareto policies. We experimentally validate the method presenting a numerical example of a real alternative solution of the vehicle routing planning problem to increase security in transportation of cash and valuables. The decision-making process explored in this work correspond to the most frequent computational intelligent models applied in practice within the Artificial Intelligence research area. [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]

• Novel Chebyshev distance measures between Pythagorean membership grades. • An extended elimination and choice translating reality method via Chebyshev metric. • Chebyshev metric-based preference functions and concordance/discordance indices. • Theoretical and comparative analysis with current Pythagorean fuzzy distances. • Application to bridge-superstructure construction and validation of effectiveness. This paper aims to propose novel Chebyshev distance measures for Pythagorean membership grades and establish their based elimination and choice translating reality method (ELECTRE) for addressing multiple criteria decision-making problems under uncertainty of Pythagorean fuzziness. Pythagorean fuzzy (PF) sets have a significant effect on fuzzy modeling for intelligent informatics and decision support because the degrees of membership, non-membership, and indeterminacy, strength of commitment, and direction of commitment featured by PF information are extended for a wider coverage of information span. The theory of PF sets is a powerful tool in dealing with imprecise and ambiguous evaluations for realistic problems and modeling intelligent decision making for complex systems. This paper focuses on both theory and applications of the Chebyshev metric for PF contexts, and special attention is devoted to the theoretical development of Chebyshev distances in connection with Pythagorean membership grades based on various types of representations. To surmount the difficulties confronted by the existing measures, such as low comprehensivity, incomparability in scaling, ignorance of square degrees in metric specification, double weighting, and inappropriate normalization, this paper makes a comprehensive comparison to validate the effectiveness and superiority of the proposed Chebyshev distance measures. To support decision making within complicated PF environments, this paper develops an extended ELECTRE approach based on the Chebyshev distance measure to conduct multiple criteria decision analysis involving PF information for determining partial and complete rankings of candidate alternatives. In particular, this paper constructs novel Chebyshev metric-based preference functions depending on the individual characteristics of the criteria. The developed PF ELECTRE approach leads to using all the information that characterizes a PF set via the concepts of the scalar function and various Chebyshev metric-based comparison indices, such as (net) concordance indices, (net) discordance indices, and the overall precedence index. Practical applications with a comparative analysis in the field of bridge-superstructure construction are conducted to examine the usefulness and advantages of the proposed methodology in the real world. [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]

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 application of Data Envelopment Analysis (DEA) as a tool for efficiency evaluation has become widespread in public and private sector organizations. Since decision makers are often interested in a complete ranking of the evaluated units according to their performance, procedures that effectively discriminate the units are of key importance for designing intelligent decision support systems to measure and evaluate different alternatives for a better allocation of resources. This paper proposes a new method for ranking alternatives that uses common-weight DEA under a multiobjective optimization approach. The concept of distance to an ideal is thereby used as a means of selecting a set of weights that puts all the decision units in a favorable position in a simultaneous sense. Some numerical examples and a thorough computational experiment show that the approach followed here provides sound results for ranking alternatives and outperforms other known methods in discriminating the alternatives, therefore encouraging its use as a valuable decision tool for managers and policy makers. [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]

• 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]

• 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]

Abstract: Despite the importance of decision-making (DM) techniques for construction of effective decision models for supplier selection, there is a lack of a systematic literature review for it. This paper provides a systematic literature review on articles published from 2008 to 2012 on the application of DM techniques for supplier selection. By using a methodological decision analysis in four aspects including decision problems, decision makers, decision environments, and decision approaches, we finally selected and reviewed 123 journal articles. To examine the research trend on uncertain supplier selection, these articles are roughly classified into seven categories according to different uncertainties. Under such classification framework, 26 DM techniques are identified from three perspectives: (1) Multicriteria decision making (MCDM) techniques, (2) Mathematical programming (MP) techniques, and (3) Artificial intelligence (AI) techniques. We reviewed each of the 26 techniques and analyzed the means of integrating these techniques for supplier selection. Our survey provides the recommendation for future research and facilitates knowledge accumulation and creation concerning the application of DM techniques in supplier selection. [Copyright &y& Elsevier]

Abstract: We consider the issue of warehouse evaluation towards successful logistic and supply chain management. Suppose a company has managed a chain of owned warehouses, and now this company is in need of acquiring some new and profitable warehouse adding to its operation chain. A key business decisions here is how to choose the most profitable warehouses from a number of potential warehouses. In reality, the challenge is that the future profitability is unpredictable. Therefore, it is infeasible to rank potential warehouses directly for choice. To address such a problem, this paper proposes a new rule-based decision model. This model includes the following characteristics: (i) decision information is provided via interval-valued intuitionistic fuzzy values; (ii) multiple experts as a group of decision makers are involved; (iii) both subjective evaluations from experts and objective data of historical profitability are employed; (iv) both certain and uncertain information are exploited. The core decision mechanism is, making use of uncertain information of owned warehouses, to induce a collection of “if…then…”rules, and subsequently to exploit these rules for prediction of preference orders of all potential warehouses. Therein, we develop and integrate multiple techniques for the purposes of (a) aggregation of uncertain information; (b) construction of pairwise comparison; (c) induction of certain and uncertain rules; and (d) decision rules exploitation. We finally elaborate our discussion with a numerical example illustrating the application of the proposed decision mechanism to supply-chain domain problems. [Copyright &y& Elsevier]

Abstract: Many multi-criteria decision making (MCDM) methods have been proposed to handle uncertain decision making problems. Most of them are based on fuzzy numbers and they are not able to cope with risk in decision making. In recent years, some MCDM methods based on prospect theory to handle risk MCDM problems have been developed. In this paper, we propose a hybrid approach combining prospect theory and fuzzy numbers to handle risk and uncertainty in MCDM problems. So, it is possible to tackle more challenging MCDM problems. A case study involving oil spill in the sea illustrates the application of the novel method. [Copyright &y& Elsevier]

Abstract: This paper presents an intelligent decision support system for evaluating and selecting specific ships under uncertainty. A task-oriented procedure is developed for determining the relative importance of the evaluation and selection criteria with respect to a specific shipping task. A fuzzy multicriteria analysis algorithm is developed for determining the overall performance of each ship across all the selection criteria and their associated sub-criteria. An intelligent decision support system capable of integrating the developments above is proposed for facilitating the ship evaluation and selection process. An example is presented to demonstrate the effectiveness of the proposed intelligent decision support system. [Copyright &y& Elsevier]

Abstract: Supplier selection is usually involved a multi-criteria evaluation to assist decision-making. This study proposed a simple method for vendor selection. The proposed procedure utilizes the structure of criteria in analytic hierarchy process (AHP) model and employs consistent fuzzy preference relations (CFPR) to construct the decision matrices. The advantage of CFPR is computational simplicity and efficiency. The computation can be done by using an Excel spreadsheet or writing a simple computer program. Also, the CFPR method guarantees the consistency of decision matrices. In this paper, a step-by-step empirical example demonstrates the usage of this proposed method. [Copyright &y& Elsevier]

Abstract: Due to the increasing competition of globalization, selection of the most appropriate personnel is one of the key factors for an organization’s success.The importance and complexity of the personnel selection problem call for the method combining both subjective and objective assessments rather than just subjective decisions. The aim of this paper is to develop a new method for solving the decision making process. An intuitionistic fuzzy multi-criteria group decision making method with grey relational analysis (GRA) is proposed. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers into a group opinion. Intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. GRA is applied to the ranking and selection of alternatives. A numerical example for personnel selection is given to illustrate the proposed method finally. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to develop a new approach for measuring the decision makers’ weights in group decision making setting, in which the decision information, provided by multiple decision makers, is expressed in interval-valued intuitionistic fuzzy numbers. There are two key issues being addressed in this approach. The first one is to develop an ideal decision of group, which is the mean of group decision. The second one is to select the similarity measure between each individual decision and the ideal decision according to the idea of the TOPSIS of . A numeric example is also given to clarify the developed approach and to demonstrate its effectiveness. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce the expected values for intuitionistic trapezoidal fuzzy numbers and presented a handling method for intuitionistic trapezoidal fuzzy multicriteria decision-making problems, in which the preference values of an alternative on criteria and the weight values of criteria take the form of intuitionistic trapezoidal fuzzy numbers. Simple and exact formulas are proposed to determine the expected weight values of the criteria for an alternative and weighted expected value for an alternative. Therefore, the expected value method is extended for ranking alternatives and the most desirable one(s) can be selected according to the weighted expected values. Finally, an illustrative example shows that the proposed method is simple and effective. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to investigate the multiple attribute decision making problems with intuitionistic fuzzy information, in which the information about attribute weights is incompletely known, and the attribute values take the form of intuitionistic fuzzy numbers. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional gray relational analysis (GRA) method, by which the attribute weights can be determined. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. Then, based on the traditional GRA method, calculation steps for solving intuitionistic fuzzy multiple attribute decision-making problems with incompletely known weight information are given. Furthermore, we have extended the above results to an interval-valued intuitionistic fuzzy environment and developed modified GRA method for interval-valued intuitionistic fuzzy multiple attribute decision-making with incompletely known attribute weight information. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness. [Copyright &y& Elsevier]

Abstract: Customer relationship management (CRM) is a multi-perspective business paradigm which aims maximizing the benefits gained from relationships with customers. The aim of this paper is to compare the CRM performances of e-commerce firms using a multiple criteria decision making (MCDM) approach. Analytical network process (ANP) is a MCDM methodology which can take the inner and outer dependencies among multiple criteria into consideration. As there are dependencies among CRM performance evaluation criteria, ANP is used for comparing the CRM performances of the e-commerce firms under consideration. A sensitivity analysis also provided in order to monitor the robustness of the proposed ANP framework to changes in the weights of evaluation criteria. To the authors’ knowledge, this will be the first study which evaluates CRM performance using ANP. [Copyright &y& Elsevier]

Abstract: Collaborative R&D projects that are applied by innovative research teams (CIRT projects) are supported by government funding agencies in a number of countries due to the complexity and multidiscipline research of innovation. Government funding agencies invest heavily to CIRT projects every year. Thus, it is important to select the desired CIRT projects to avoid undesirable budget consumed. The purpose of this paper is to propose an integrated method for CIRT project selection. In this method, competitiveness and collaboration of candidate innovative research teams (IRTs) are used to assess and select projects. The criteria for competitiveness and collaboration are finalized in light of literature review as well as real situations. A formal decision procedure that aggregates competitiveness and collaboration performances of CIRTs is then presented. It integrates analytic hierarchy process (AHP), scoring method and weighted geometric averaging method. Some sample data from the National Natural Science Foundation of China (NSFC) is used to illustrate the potential application of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: The use of elements of artificial intelligence, including knowledge-based systems, becomes more and more widespread in aiding design problem solutions. The authors have been working on problems of control systems for many years. A design process involves many decision problems connected with, for example, a choice of a subsystem structure, subunits or particular elements selection. Because of such regards, it was decided to extend knowledge-based system with a module for support of such decision making. In this paper, an elaborated module for decision-making support is considered. The basic theoretical assumptions concerning the accepted method of multiattribute decision making based on pairwise comparison in categories of hierarchical decision process (AHP) is presented. Accepted knowledge representation in AHP method and pairwise comparison method and methods of expert knowledge acquisition are discussed. The module functioning is illustrated by an example of choice of temperature sensors in a system of fuel transport to Diesel engine of a main propulsion unit of a ship. [Copyright &y& Elsevier]

Abstract: Since the correct energy policy affects economic development and environment, the most appropriate energy policy selection is excessively important. Recently some studies have concentrated on selecting the best energy policy and determining the best energy alternatives. In most of these studies, multicriteria and fuzzy approaches to energy policy making are frequently used. The fuzzy set theory is a powerful tool to treat the uncertainty in case of incomplete or vague information. In this paper, a fuzzy multicriteria decision-making methodology is suggested for the selection among energy policies. The methodology is based on the analytic hierarchy process (AHP) under fuzziness. It allows the evaluation scores from experts to be linguistic expressions, crisp or fuzzy numbers. In the application of the proposed methodology, the best energy policy is determined for Turkey. [Copyright &y& Elsevier]

Abstract: The most commonly used measures for evaluating the competing mutual funds are “Treynor Ratio”, “Sharpe Ratio” and “Jensen’s Alpha”. One also uses another measure called the “Information Ratio”. However, it is not clear which measure is the most robust. The purpose of our study is to evaluate the performance of mutual funds under the broad framework of multi-attribute decision analysis approach where each criterion can be taken into consideration in making a final ranking of the mutual funds. In this paper we adopt the concepts of “Ideal” and “Anti-Ideal” solutions as suggested by , and study the extended technique for order preference by similarity to the ideal solution (TOPSIS) method using two different “distance” ideas, namely – “Minkowski’s metric” and “Mahalanobis” distances. The broad framework with the two aforementioned distances is then applied to evaluate the performance of 82 Taiwanese mutual funds for consecutive 34 months. The empirical results show that using TOPSIS methods incorporated Minkowski’s distance measure to evaluate the mutual funds’ performance perform well. [Copyright &y& Elsevier]

Customer evaluation plays an important role as a part of the order acceptance process of suppliers in optimally allocating resources and prioritizing orders accordingly. In this paper, a new class of fuzzy methods for evaluating customers is applied. Firstly, our approach tackles the issue of uncertainty that is inherent in the problem of customer evaluation that involves qualitative criteria by employing the method proposed by Yong [Yong, D. (2006). Plant location selection based on fuzzy TOPSIS. International Journal of Advanced Manufacturing Technology, 28(7–8), 839–844] in order to efficiently transform linguistic assessments of the weights of criteria and of the ratings of customers into crisp numbers. Secondly, the TOPSIS method is modified in order to integrate the behavioral pattern of the decision maker into its “principle of compromise”. In this context, a new model for the aggregating function of TOPSIS that is based on a fuzzy set representation of the closeness to the ideal and the negative ideal solution is applied. In particular, we use the class of intersection connectives proposed by Yager [Yager, R. R. (1980). On a general class of fuzzy connectives. Fuzzy Sets and Systems, 4(3), 235–242] that enables a formal definition of the relation between the closeness to the ideal solution and the closeness to the negative ideal solution. Thus, a class of methods is formulated whose different instances correspond to different behavioral patterns of the decision makers, e.g. with preference to customers that make as much profit as possible but also avoid as much risk as possible or to customers that are performing well in at least one of the profit and risk criteria. A numerical example, illustrating the application of this class of methods to customer evaluation is given. [Copyright &y& Elsevier]

Abstract: Travel websites provide customers diverse services including travel information and products through the internet. More and more people are used to plan their itineraries and acquire travel information by visiting travel websites. In the face of the strong competition environment, in order to profit by making customers proceed with transactions on the websites, travel websites should pay more attention to the improve the electronic service quality (e-SQ). e-SQ evaluation for travel websites can be treated as a multiple-criteria decision making (MCDM) problem modeled by the given hierarchical structure. As for evaluating e-SQ, since the traditional Likert scales cannot deal with uncertain assessments according to human intuition for the service quality evaluation, the fuzzy number is employed to measure decision-makers’ subjective preference. This paper aims to develop a genetic-algorithm-based method to automatically determine the degrees of importance of respective criteria. Then, critical criteria for evaluating service quality can be easily identified. In the empirical study, four representative travel websites in Taiwan are selected to determine the critical criteria concerned by customers. The findings provide useful information to travel websites for improving e-SQ. [Copyright &y& Elsevier]