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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]

As an effective approach in ranking decision making units (DMUs), cross-efficiency evaluation is only extended to two basic network systems: series and parallel, which is not applicable to the common complex network system in reality. By using the directional distance function (DDF), this paper extends the cross-efficiency evaluation to a complex system called the parallel two-stage system, which consists of several independent parallel processes and each of them is a sub-system with a general two-stage series structure. The proposed method is able to decompose the cross-efficiency measure of the system into the weighted average of those of processes, stages and overall stages, respectively, and as a result, successfully achieves the layer-by-layer decomposition of cross-efficiency measures under complex nested networks. An empirical study on 40 open-ended equity funds in China from 2018 to 2020 verifies that the newly proposed method not only has a stronger performance discrimination power than self-evaluation, but also can identify the relationships among efficiencies of the system, processes and stages, and meanwhile it can reflect the effects of processes and stages on the system. More importantly, our method has a better practical value for actual fund investment selection. [ABSTRACT FROM AUTHOR]

• The score function is proposed for hesitant Fermatean fuzzy set. • Unify the framework of Regional Green Development Level Evaluation. • Improve MULTIMOORA method is proposed with hesitant Fermatean fuzzy information. • Level Difference Maximization Method to combine subjective and objective weights. • Case study of RGDL is presented to show the potentiality of the developed method. Economic green development has received increase attention. However, the uncertainty decision processes in the development of green economic growth has brought challenges to the evaluation, which leads to the evaluation index system has not yet reached a consensus. In order to solve the above problems, this paper, based on the established and developed regional green development level evaluation (RGDLE) system in existing literature, employed improved MULTIMOORA group decision model under the hesitant fuzzy Fermatean environment (HFFS-MULTIMOORA) and an innovative weighting method to develop the RGDLE process. This evaluation framework describes the complexity of the RGDLE process. The Level Difference Maximization Method to combine the average-based subjective weight-determining method and the Shannon-entropy-based objective weight-determining method is developed to fit RGDLE problems with cognitive complex information. This innovative index system and HFFS-MULTIMOORA method based group decision-making framework is further applied to evaluate the green development level of five provinces and municipalities along the Yangtze River Economic Zone, China. Results showed the optimal green development levels ranking of the five alternative provinces and municipalities of the Yangtze River Economic Zone, China. Moreover, the effectiveness and feasibility of the model framework was verified by robustness and comparative analysis between the existing method. This paper contributes not only to the literature on RGDLE but also to giving decision-makers and researchers inside and outside China valuable inspiration and references for improving the evaluation efficiency and giving policy suggestions. [ABSTRACT FROM AUTHOR]

• Develop a complete problem-solving approach for group decision making. • Orient complex large-scale groups and time-sensitive emergency decision scenarios. • Design a feasible mechanism of group consensus strategies under time pressure. • Propose feedback recommendation strategies with a subgroup identification method. • A real case study of China's fight against the COVID-19 epidemic. Group Decision Making (GDM) has been well studied in the last two decades. Yet, two challenges exist: (a) how to resolve large-scale groups in GDM and achieve the consensus of preferences and (b) how to conduct GDM under risk and emergency conditions. In this paper, we develop a complete problem-solving approach for GDM that orients twofold settings of the complex large-scale group and the time-sensitive emergency decision scenarios. The crux of the matter is to design a feasible mechanism of group consensus strategies in the environment of time pressure and natural language preferences. To solve this problem, we propose a closed-loop mechanism of feedback recommendation strategies accompanied with a new subgroup identification method. This mechanism is underlain by a fourfold decomposition of complex large-scale groups, which entails multiple thresholds of group consensus, group hesitation, and time-related iteration of loops. Our mechanism and the whole GDM approach thoroughly orient the most intuitive representation of preferences - human natural language, which can be elicited and quantitatively formulated in probability linguistic preference systems. We illustrate the proposed approach through a real case study of China's fight against the COVID-19 epidemic. We verify that our mechanism can perfectly tradeoff between the effectiveness and the efficiency of complex large-scale GDM under risk and emergency. The results of this research provide proposals for mechanisms on large-scale GDM and are expected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards. [ABSTRACT FROM AUTHOR]

In order to cope with the growing problem of flood disasters, it is crucial to improve the urban flood resilience. Hence, this paper develops a novel evaluation methodology to find the weak links and capacity gaps of urban flood resilience. Firstly, according to the three periods of flood, the urban flood resilience evaluation indicator system is divided into the predictive capacity subsystem before the flood, the absorptive and coping capacity subsystem and the recovery capacity subsystem during the flood, and the adaptive capacity subsystem after the flood. This indicator system also creatively introduces the density of hydrological and meteorological station networks into the predictive capacity subsystem, and divides the adaptive capacity more meticulously into learning capacity at the governmental and individual-society levels. Secondly, a novel three-reference-point (T-R-P) based group ELECTRE III method is proposed to rank the flood resilience of different cities. This method integrates subjective and objective information to obtain more reasonable expert weights, and then sets thresholds with T-R-P to obtain more comprehensive and objective evaluation results. Furthermore, the proposed methodology is applied to six flood-prone cities in China, and its practicality and availability are verified through comparative analysis with other three evaluation methods and sensitivity analysis of the indicator weights. Finally, based on the results, targeted suggestions are made to enhance the flood resilience of the six cities. • A time-based evaluation framework for flood resilience has been established. • Expert weights are determined by integrating heterogeneous information. • A novel three-reference-point based group ELECTRE III method is proposed. • The effectiveness of the proposed method is verified by empirical analysis. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a powerful nonparametric approach for assessing the performance of decision making units (DMUs). Traditional DEA models usually utilize a self-evaluation mode to evaluate DMUs, which have the following shortcomings such as efficiency overestimation and incomplete ranking. As the extension of traditional DEA, cross-efficiency (CE) evaluation remedies these limitations. It connects self-evaluation with peer-evaluation to appraise DMUs and rank them. However, most existing studies on CE evaluation utilize the arithmetic average method to aggregate self-evaluation efficiency and peer-evaluation efficiency, while ignoring the significance of self-efficiency and the irrational psychology of decision-makers (DMs). Observed by the aforementioned phenomena, this paper employs regret theory to portray the regret aversion behavior of DMs and further proposes a novel regret cross-efficiency aggregation (RCEA) method. Considering aggressive and benevolent cross-efficiency models, we apply the RCEA method to calculate the regret-based aggressive cross-efficiency and the regret-based benevolent cross-efficiency, and introduce a parameter reflecting the psychological preference of DMs to aggregate them. To attain consensus between DMUs' expectations for efficiency and the ultimate integration results, we design a regret cross-efficiency adjustment algorithm. This algorithm provides a more acceptable evaluation result and derives a full ranking for all DMUs. Finally, the validity and feasibility of the proposed model are illustrated by evaluating the efficiency of scientific research activities in 13 universities in China. • We develop a novel regret cross-efficiency aggregation method. • Aggregate the regret cross-efficiency in line with DMs' psychological preferences. • A novel regret cross-efficiency ranking model considering consensus is proposed. • The validity of our model is verified by the evaluation of 13 Chinese universities. [ABSTRACT FROM AUTHOR]