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• 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]

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]

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]

• 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]

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]

The problem-solving decision-making process often requires involvement of a group of individuals who have differing interests and conflicting multiple evaluation criteria. Therefore, the greatest concern in multiobjective group decision-making problems is how to arrive at a best decision that is agreeable to all the members of the group. Many previous studies focused on handling this concern based on decision rules, such as the consensus or ranking selection approaches. Although many contributions to the literature were made by past studies on this issue, disagreement remains on finding an effective way to address the subjectivity issue in group decision-making. This paper introduces a new approach called the preference clustering-based mediating group decision-making (PCM-GDM) method for minimizing the subjectivity issue. The PCM-GDM method basically employs two concepts: (1) clustering the preferences of the group members in a decision and (2) utilizing a mediating agent as a final decision-making tool. The new approach was applied to a case study of sample concrete bridge decks in the state of Indiana. The results of this study confirm that the proposed approach can significantly improve the multicriteria group decision-making results by providing a way to exclude biased judgments by decision-makers that can interfere with the development of one best alternative. The proposed approach advanced the reliability of the conventional decision-making knowledge, which is dependent on a consensus or the ranking of approaches by human experts to reach one solution. [ABSTRACT FROM AUTHOR]

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

[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]

Group decision making is an important part of multiple criteria decision making and the analytic hierarchy process (AHP). The aim of this paper was to compare group AHP methods. Seven simple group AHP aggregation techniques that could be attractive for applications selected from the vast array of group AHP models proposed in the literature were selected for evaluation. We developed three new measures of evaluation: group Euclidean distance, group minimum violations, and distance between weights for the purpose of evaluation. The results of seven group AHP methods of the theoretical example were evaluated by three new evaluation measures, satisfactory index and fitting performance index. Furthermore, a case study of a decision making problem from the construction engineering field was performed and nine group AHP aggregation techniques, seven of them formerly presented and two new two stage group approaches were applied. Finally, the case study was evaluated using all five measures for each of the nine group decision making methods. The results showed that not all group AHP methods are equally convenient and that the selection of the method depended on the specific application. [ABSTRACT FROM AUTHOR]

How to select the suitable suppliers in the supply chain is critical for an organization’s success and has attracted much attention of both researchers and practitioners. Supplier selection can be regarded as a complex group multiple criteria decision making problem requiring consideration of a number of alternative suppliers and quantitative and qualitative criteria. Additionally, decision makers cannot easily express their judgments on the alternatives with exact numerical values in many practical situations, and there usually exists uncertain and incomplete assessments. In response, this paper proposes an extended VIKOR method for group multi-criteria supplier selection with interval 2-tuple linguistic information. The feasibility and practicability of the proposed interval 2-tuple linguistic VIKOR (ITL-VIKOR) method are demonstrated through three realistic supplier selection examples and comparisons with the existing approaches. Results show that the ITL-VIKOR method being proposed is more suitable and effective to handle the supplier selection problem under vague, uncertain and incomplete information environment. [ABSTRACT FROM AUTHOR]

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

• New hybrid weighting method for Human Resource Information System assessment. • New hybrid evaluation method for group decision-making under uncertainty. • Human Resources practitioners' method and academia accepted methods are different. • High divergence in the level of importance on the Internet of Things. This paper evaluates the human resource information systems provided by different vendors using two new hybrid multicriteria decision-making methods that require ordinal data as inputs. First, the grey-point-allocation full-consistency (Grey-PA-FUCOM) weighting method is proposed. The Grey-PA-FUCOM combines the simple point-allocation method widely used by human resource managers and the advanced FUCOM method widely accepted by scholars in grey system theory. Second, the grey-regime method, an extension of the classical regime method based on grey system theory, is used to account for the uncertainty in the evaluation. Next, the Grey-PA-FUCOM weights are applied in conjunction with the grey-regime scheme to evaluate the five vendors. Finally, to validate the results of this study, grey relational analysis with grey numbers, the grey weighted sum model, and a technique for order performance based on the similarity to the ideal solution with grey values are used. [ABSTRACT FROM AUTHOR]