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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]

• 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]

There are more complex heterogeneous preference expressions and methods (HPEMs) in heterogeneous multi-attribute group decision making (MAGDM) with several attribute sets, which makes the effective fusion of HPEMs become an important challenge in practical applications. The paper aims to propose a stochastic-integration-based decision support framework for the unified fusion of various HPEMs and provide some feasible solution methods. The construction process of decision support framework is embodied in recombining and connecting the HPEMs into "information flows" according to the traditional multi-attribute decision making procedures. For the information flows, three types of solution methods, such as the global solution method, the simplified solution method, and the consistency-measure-based solution method, are proposed to transform them into a final superiority-probability-based pairwise comparison matrix (SPM). Based on the final SPM, a ranking method is developed that uses probabilities to present the superiority relationship between two alternatives. In addition, a comparison discussion is given to highlight the advantages and disadvantages of the research. And two application examples are used to illustrate the validity of the proposed framework and solution methods. The decision support framework proposed in this paper is more open and inclusive, providing a feasible way for the unified fusion of various HPEMs. The proposed solution methods not only realize the sufficient solution, but also extend the decision results to probabilistic form, making the decision results more consistent with heterogeneous environment. The research in the paper can further enrich the methodology of heterogeneous MAGDM system and provide methods and technic support for complex group decision-making problems. [ABSTRACT FROM AUTHOR]

• Investigate a novel TrFBWM to yield the TrF criteria weights. • Develop the TrFZSG to rank the alternatives. • Propose a TrF-BWM-ZSG by fusinging the BWM and ZSG. This paper investigates a hybrid emergency decision-making (EDM) method called TrF-BWM-ZSG by combining the BWM (best-worst method) and ZSG (zero-sum game) in trapezoidal fuzzy (TrF) environment. Firstly, this paper defines the entropy and similarity for trapezoidal fuzzy numbers (TrFNs). Based on the entropy and similarity measures, a bi-objective programming model is constructed to distribute the weights for experts. Then, based on the multiplicative consistent trapezoidal fuzzy reference comparisons (TrFRCs), a novel TrFBWM is investigated to yield the TrF criteria weights. This novel TrFBWM highlights two innovations: (i) A crisp goal programming weight-determining model is constructed by using the multiplicative consistent TrFRCs; (ii) A reasonable consistency ratio is defined, in which the consistency index is determined according to the consistent transitivity of TrFRCs. Afterwards, the TrFZSG is developed to achieve the alternatives' ranking order by extending the classical ZSG into TrF environment. Lastly, the availability of the TrF-BWM-ZSG is corroborated through a real EDM case of Henan rainstorm disaster. The advantages of the TrF-BWM-ZSG are manifested by several valuable comparisons. Some desirable results derived by the TrF-BWM-ZSG are received: (i) The optimal alternative selected by the proposed TrF-BWM-ZSG suits the actual rescue situation; (ii) The proposed TrF-BWM-ZSG owns high generality since TrFN can reduce to crisp number, interval and triangular fuzzy number; (iii) The developed novel TrFBWM is helpful to preserve more inherent information of TrFNs. [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]

In literature, many studies try to analyze healthcare usage and generated decision support systems. In this paper, the aim is to generate a decision support system for insurance companies in the United States according to the federal dataset. The data set contains variables from the United States Healthcare Administration formal data group and they are; group of age, group of healthcare insurance, group of nationality, Corporate Social Responsibility (CSR) usage, and Advance Premium Tax Credit (APTC) usage. The best model for each state in the United States is determined with the dataset from 2014 November to 2015 February. In this study, various statistical models are attempted to generate independent and optimal models for each state. In the model selection process, three different metrics are used. Accuracy rate, f1score and precision score are used in the model selection process and models are compared to each other according to these three metrics for each state uniquely. Consequently, the proposed model is used with a decision support scheme to support customer service workers in healthcare insurance companies. The decision support system under consideration can facilitate a substantial decrease in the duration of interactions between customer service representatives and clients. Additionally, this system can generate more logically sound and efficient customer offers. [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]

The paper proposes a new information set of multiple interacting fuzzy linguistic set(MIFLS) to describe the interaction of uncertain linguistic information. The property of the MIFLS and its characteristic of processing consensus information are also discussed, the introduction of MIFLS provides a new perspective to improve the consistency of decision makers' evaluation information, and the application of MIFLS can improve the accuracy of decision making. Furthermore, we propose an optimization consistency information model based on the MIFLS and TOPSIS method, an example of emergency decision plan is used to illustrate the applicability and efficiency of the proposed model. The optimal consistency adjustment is not only the highly consistent evaluation information, but also the minimal difference between the original information and the adjusted information. The proposed MIFLS is fit for dealing with the interaction of decision information, which means MIFLS is one of the effective tools to processing the interaction of uncertain fuzzy set and dealing with the emergency decision making problem. More important, the MIFLS not only provides a way to quantify the correlation of multi-attribute information, but also provides a method how to improve the consistency of decision-maker information. [ABSTRACT FROM AUTHOR]

Aiming at three issues in large-scale group decision-making (LSGDM): risk attitudes, dynamically changing roles, and dynamically adjust evaluation information of decision makers (DMs), this paper constructs an LSGDM model by considering risk attitudes and dynamically changing roles. Firstly, we introduce the concept of risk attitudes and the method of identification and creatively combine it with Decision Making Trial and Evaluation Laboratory to divide DMs into four categories including core DMs, driven DMs, independent DMs, and followers. Considering the dynamically changing roles of DMs, an adjustment method for evaluation information is proposed. Then a double recognition mechanism is developed to determine whether the consensus has been reached. Moreover, we construct a method to determine the weights of DMs. Based on the above research, an LSGDM model based on probabilistic double hierarchy linguistic term set is constructed and applied to a practical problem of the selection of emergency medical distribution schemes. Finally, a sensitivity analysis is performed to examine the impact of different adjustment coefficients on the results, and the validity of our method is further illustrated by comparative analysis. [ABSTRACT FROM AUTHOR]

In multiple attribute decision making (MADM) problems, when belief distributions (BDs) are used to express the preference information of decision-maker, obtaining the attribute weights using the difference among the assessments is a complex but interesting challenge. This paper introduces the weighted similarity measure of belief distribution to distinguish the alternatives to the greatest extent. After proposing the concepts of similarity measure and weighted similarity measure for two belief distribution vectors, an optimization model is developed to generate the attribute weights. After that, the comparison rules and the ideal belief distributions are defined, then the regret theory is extended to rank alternatives with the consideration of the psychological behavior of the decision-maker. The main characteristic of the proposed method involves: (1) An optimization model based on weighted similarity measure is proposed to derive attribute weights in BD environment; (2) The psychological behavior is taken into account to assist the decision-maker in avoiding regret; Finally, the applicability and validity of the proposed method are demonstrated by a medical example in which doctors could make the diagnosis with minimal regret. [ABSTRACT FROM AUTHOR]

In the Big Data era, context-aware mobile recommender systems are crucial in assisting citizens and tourists in making informed decisions, providing a suitable way for users to find the relevant data. These systems should be proactive, able to detect the ideal time and location to provide recommendations for a specific item or activity. To accomplish this, push-based recommender systems can be employed, utilizing context rules to determine when a recommendation should be initiated. However, there is very limited reported experience in defining and implementing such systems and a complete generic solution that adapts flexibly to the preferences of users and protects their privacy is still missing. In this paper, we present a novel approach where appropriate types of recommendations are provided automatically, without the need for user input. Our proposal allows users to easily activate, deactivate, customize, and create rules for improved personalization. Additionally, the module that, based on the context, decides the types of recommendations required is executed on the user's mobile device, reducing wireless communication and safeguarding the user's privacy, as context data are evaluated locally. To illustrate the approach, we have developed R-Rules, a prototype for Android devices focused on the triggering of recommendation rules, which provides a friendly user interface that facilitates user personalization. We have evaluated various technological options and demonstrated the feasibility, performance, and scalability of the proposal, as well as its suitability to users' needs. • Generic architecture for mobile push-based recommendations. • Decision module executing on the user device: privacy and wireless savings. • Flexible user-defined rules to decide when to fire a specific type of recommendation. • Android prototype and extensive experimental evaluation. [ABSTRACT FROM AUTHOR]

Intelligent computing is an independent algorithm system that imitates the laws and principles of nature's biological chain to design and solve practical problems in modern society, it can help people better handle some big data problems through artificial intelligence technology and different cognitive characteristics. Especially, when dealing with group decision problems, it is often considered as human behavior based on cognitive computing. The q-rung orthopair fuzzy set (q-ROFS) is not only a generalization of Pythagorean fuzzy set (PFS), but also can deal with the decision problem of multi-source information in a wider area. In this paper, we first give the detailed explanations of the relationship between the q-ROFS and q-rung orthopair fuzzy number (q-ROFN)by an example, and itis pointed out that there are some defects in the existing two ranking methods for the Pythagorean fuzzy numbers (PFNs) and intuitionistic fuzzy numbers (IFNs) through counterexamples, and then all q-ROFNs are mapped into the unit triangle of the first quadrant through q-compression mapping, and the concept of distance factor for q-ROFNs is proposed by Euclidean distance between each q-ROFN and the maximum element (1,0).Secondly, a novel inverse score function for q-ROFNs are put forward by the distance factor and hesitancy degree, and the monotonicity of the inverse score function are discussed. Finally, a new ranking criterion of q-ROFNs is given by the new score formula, and then the rationality of the ranking criterion is proved, and through the method of solving the conditional extreme value of multivariate function (Lagrange multiplier method), there is indeed an extreme value point (corresponding to the maximum q-ROFN) on υ = μ. Besides, a q-rung orthopair fuzzy decision-making method for multi-source information is given by the new ranking criterion and aggregation operations, and the comparison of examples shows that the inverse score function and decision-making method do have many advantages in the q-ROFN environment. [ABSTRACT FROM AUTHOR]

• Proposing a physical asset criticality analysis decision model based on BIA. • Risk Appetite and physical asset continuity measures are related. • Defining sub-criteria for impact and likelihood estimation in criticality analysis. The paper tackles the issue of managing physical assets in asset-intensive industries, where continuity plans are crucial for business operations during disruptions. However, resource constraints often hinder companies from implementing these plans for all their assets. Moreover, efforts to maximize resource utilization efficiency and minimize environmental impacts are limited to isolated disciplines, necessitating an integrated approach across natural, social, and technological systems. To address these challenges, this study proposes a framework based on a hybrid fuzzy BWM-TOPSIS methodology for identifying critical physical assets. The framework incorporates Business Impact Analysis (BIA) to determine essential products, a physical asset risk assessment matrix (PARAM) to assess critical assets based on likelihood and impact, and a mathematical model to estimate asset continuity parameters. The framework is applied to a petrochemical company in a case study, offering a guide for estimating the probability and impact of physical asset failure while accounting for inherent uncertainties. By adopting this framework, companies can efficiently manage their physical assets, prioritize continuity plans, and address resource constraints and uncertainties. This research contributes to practical tools and approaches that assist industries in sustaining operations and ensuring business continuity during disruptions. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) plays a vital role in achieving energy revolution and the goals of carbon peak and carbon neutrality. The optimal planning of IES is of great significance for improving the overall efficiency of the system and promoting its sustainable development. Focusing on this issue, this paper proposes a planning framework integrating multi-objective optimization with fuzzy multi-criteria decision making (MCDM). In this framework, IES planning is modeled as a multi-objective optimization problem that, for the first time, simultaneously minimizes energy consumption, carbon emissions, and economic costs. Thereafter, the optimization problem is solved by a multi-objective state transition algorithm based on decomposition (MOSTA/D), which generates a Pareto set that realizes multiple conflicting objective tradeoffs. Furthermore, to comprehensively evaluate the Pareto optimal solutions, an evaluation criteria system is established from various perspectives, and a novel MCDM approach is proposed. This approach combines the analytic network process-entropy weighting technique, which takes into account the correlation between criteria as well as subjective preference and objective information, with fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) for scientifically ranking and selecting solutions under uncertainty. The simulation results of an IES planning case study demonstrate that the optimal scheme determined by the proposed method achieves the best overall benefit for IES, with significant annual economic cost savings, primary energy savings, and carbon dioxide emission reduction rates of 2.27%, 40.36%, and 56.25%, respectively, proving the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Atanassov's intuitionistic fuzzy sets play a vital role in handling vagueness and uncertainty successfully as they associate an additional non-membership degree to each element of the universal set. Interval-valued intuitionistic fuzzy (IVIF) sets are more generalized quantities in this direction as they use intervals (⊂ [ 0 , 1 ]) to define the membership and non-membership grades of each element of the set. However, without defining basic arithmetic operations on the IVIF values/sets, the study is incomplete. This paper broadly examines the division and subtraction operations over any arbitrary IVIF values/sets using the concept of Hamming distance between them. A deterministic linear optimization model is proposed and solved in order to obtain the complete expressions for these operations. Next, various fundamental properties and relationships are examined and proved for the proposed arithmetic operations over IVIF values/sets. Furthermore, an example of a multi-criteria decision-making problem under uncertain conditions is studied and analyzed using the developed arithmetic operations on IVIF numbers. • Derived the generalized subtraction and division operations for IVIF values/sets. • The notion of Hamming distance and dual-simplex algorithm are used. • A deterministic LPP is proposed to obtain the expressions for the operations. • The fundamental properties and relationships for the basic operations are explored. • A case study of multi-criteria decision-making problems has been discussed. [ABSTRACT FROM AUTHOR]

The classification of huge complex datasets to extract the information needed for making informed decisions has gained a lot of importance in today's scenario due to the outspurt of data. With an increase in dataset size, the means of obtaining the desired information via data clustering techniques also became complex. This paper illustrates a framework for selecting the best combination of the Kernel function and the Lattice structure for better classification based on the size of the data. The performance of the SOM majorly depends on the choice of Kernel Function, Lattice Structures, and the size of the datasets. The types of kernel functions deployed in this study are Gaussian, Exponential, and Laplacian Kernel along with the lattice Structures such as hexagonal, Rectangular, and Square which result in nine combinations respectively. These nine combinations of the Kernel Function and the Lattice Structure have been tested on datasets ranging from small to large size using the performance metrics. A frequency table has been arrived at based on the classification of the dataset size, combinations of Kernel functions, and Lattice Structures. The results of this study infer that the combination of Gaussian Kernel -Square Lattice performs better for small-size datasets wherein the Exponential and Laplacian Kernel functions along with Hexagonal Lattice perform better for mid-size datasets, while the Laplacian Kernel – Square Lattice perform better for large size datasets. [ABSTRACT FROM AUTHOR]

In high-end equipment project selection, which involves high technical complexity, significant capital investment, and cross-industry applications, it is essential to involve experts from diverse fields in the decision-making process. To address the challenge of large-scale group decision-making, this paper proposes a three-way large-scale group decision-making model for triple-path consensus reaching process based on a rewards-and-punishments mechanism. The model consists of three stages. Firstly, the group consensus degree is calculated based on the initial input. If the specified threshold is not met, the consensus adjustment iteration stage is entered and decision makers are divided into three regions based on the Pareto principle. Secondly, separate calculations are conducted for the moving benefits and costs of decision makers in different regions, with the total benefits and costs used as targets for the NSGA-II algorithm. The Pareto solution set is obtained, and the solution with the least number of moving objects is selected as the three-way decision-making movement strategy. This process is repeated until the consensus threshold is reached or one of the regions is empty. Finally, based on the final adjustment results, scores are calculated for each project and a ranking is established as a selection suggestion. To demonstrate the feasibility and effectiveness of the model, ten high-end equipment projects are taken as examples, and the sensitivity of the model is tested by adjusting the number of decision makers. In conclusion, the proposed three-way large-scale group decision-making model based on a rewards-and-punishments mechanism for triple-path consensus reaching process is an effective and feasible approach for high-end equipment project selection. [ABSTRACT FROM AUTHOR]

The pervasiveness of the Internet coupled with the advantages of cloud have almost made cloud services a necessity for organizations and individuals alike. However, it is a complex decision to choose a cloud vendor because of the large number of criteria and alternatives. Furthermore, the criteria in the case of cloud vendor selection are often interactive (synergic or opposing). To address this need, an approach is presented in this paper to select the most suitable cloud vendor while considering the nuances of human decision making, such as interactive criteria, and varying behavioural characteristics of the decision-makers (DMs). To this end, the recent Choquet logit models are deployed and generalized to add to their modelling abilities. Specifically, the following choice models are developed: generalized Choquet logit, generalized attitudinal Choquet logit, generalized Choquet mixed logit, and generalized attitudinal Choquet mixed logit models. The proposed generalized logit models have the Choquet logit, and the attitudinal Choquet logit models as their special cases. Thus the contributions of this work are two-fold: first an approach is developed to select suitable cloud vendor considering the human nuances and the actual practical aspects of the problem, and secondly a new class of powerful choice models is developed which may be useful in aiding any complex human decision-making, in which the cognitive power of the DM proves to be limited. The method is designed to consider the real nuances of the human decision making such as varying attitudes and varying degree of criteria interaction. The effectiveness of the proposed models in cloud vendor selection is tested through a real world case study from the Indian state of Tamil Nadu. In the case-study, there are 9 cloud vendors in the fray and 4 experts with different attitudes and different suggestions for the best cloud vendor. An approach is developed to solve this problem and a final ranking of the cloud vendors is determined considering experts suggestions. Apart from finding the best cloud vendor, the case study also helps in studying the role of an expert's attitudinal tendency on his preferences. Thus, concomitantly, the proposed approach also guides how to choose the best alternative based on the opinions of different experts with different attitudinal tendencies, without necessitating a forced consensus. • New probabilistic discrete choice models. • Representation of the complex decision making attitude. • Consideration of interaction among the criteria. • Variants of the proposed logit models. • Real Application. [ABSTRACT FROM AUTHOR]

Concept drift is a prevalent phenomenon in data streams that necessitates detection and in-depth understanding, as it signifies that the statistical properties of a target variable, which the model aims to predict, change over time in unforeseen ways. Existing detection methods predominantly aim to identify the drift start time, which lack comprehensive understanding of data streams, leading to a loss of drift information. In this paper, we present a novel Quadruple-based Approach for Understanding Concept Drift in Data Streams (QuadCDD) framework that not only detects and predicts the concept drift start point but also offers a more detailed analysis of concept drift through the use of quadruples, encompassing drift start, drift end, drift severity, and drift type. Our framework employs quadruples to enable informed decision-making and adopt appropriate actions to handle various concept drifts, effectively maintaining high and stable performance in data streams with concept drift. Experimental results validate the effectiveness of our QuadCDD framework in accurately detecting and understanding concept drifts, as well as in preserving the stability and performance of models in the presence of these drifts. • Introduced a novel framework, QuadCDD, for concept drift Understanding. • The approach enables effective handling of various concept drifts. • Maintains high performance in data streams with drift. • Fosters a detailed understanding of concept drift. [ABSTRACT FROM AUTHOR]

In large-scale group decision-making (LSGDM) events, conflicts among decision makers (DMs) usually occur, causing serious damage to the decision-making process. Accurate conflict detection and timely management in LSGDM can improve the efficiency of the consensus reaching process (CRP) and reduce the overall conflict degree. This paper presents a conflict management-based consensus reaching process (CM-CRP) to achieve centralized and efficient management of DMs. In CM-CRP, to further manage the conflict relationships among DMs, a new clustering algorithm is considered where DMs with conflicting opinions are clustered into a subgroup. To improve the accuracy of the management and precisely the portray behaviors of DMs, for a multi-attributes LSGDM event, the confidence of DMs at the attribute level is captured. Further, the dynamic opinion weight operator is proposed in CM-CRP combining the confidence level and conflict degree of DMs, which enables a more accurate measurement of DMs' receptivity to others' opinions. Comparative experiments prove that the proposed CM-CRP outperforms the existing CRP, and several simulations investigate the effect of different factors on the CM-CRP. • The model proposed can manage conflict among decision-makers intensively. • The clustering algorithm promotes information fusion and knowledge exchange. • New confidence levels improve decision efficiency and quality. • Dynamic opinion weights describe the decision-makers' receptivity to opinions. [ABSTRACT FROM AUTHOR]

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming an already trained Supervised Classification model, post-hoc explanations can be obtained via so-called counterfactual analysis: a counterfactual explanation of an instance indicates how this instance should be minimally modified so that the perturbed instance is classified in the desired class by the given Machine Learning classification model. Most of the Counterfactual Analysis literature focuses on the single-instance single-counterfactual setting, in which the analysis is done for one single instance to provide one single counterfactual explanation. Taking a stakeholder's perspective, in this paper we introduce the so-called collective counterfactual explanations. By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints. Making the process of constructing counterfactuals collective instead of individual enables us to detect the features that are critical to the entire dataset to have the individuals classified in the desired class. Our methodology allows for some instances to be treated individually, as in the single-instance single-counterfactual case, performing the collective counterfactual analysis for a fraction of records of the group of interest. This way, outliers are identified and handled appropriately. Under some assumptions on the classifier and the space in which counterfactuals are sought, finding collective counterfactual explanations is reduced to solving a convex quadratic linearly constrained mixed integer optimization problem, which, for datasets of moderate size, can be solved to optimality using existing solvers. The performance of our approach is illustrated on real-world datasets, demonstrating its usefulness. • A new model for Explainable Artificial Intelligence is proposed. • Counterfactual Explanations for a full group of individuals are sought. • Sparsity both at individual and group level is achieved. [ABSTRACT FROM AUTHOR]

E-democracy provides a virtual online platform characterized by information transparency and equal interaction, which facilitates the participation of multiple agents in government decision making. This paper proposes a comprehensive loss analysis-based decision support method and applies it to a case study of e-democratic multi-agent cooperative decision-making. Multiple agents represent different interests and provide opinions with the goal of maximizing their own revenues. Agents' opinions are naturally prone to differences and even conflicts, which may have a negative impact on the harmony of the social system. To this end, this study develops a two-stage type- α constrained minimum-revenue-loss consensus (TS- α -CMRLC) model. In Stage 1, an α -CMRLC model is adopted to obtain the optimal solutions of agents' opinions with minimizing the social revenue loss and preventing excessive revenue loss. In Stage 2, the concept of reputation loss is defined and an α -CMRLC model considering reputation loss is proposed. In this matter, the agent with high reputation but low consensus can reduce its revenue loss through the cost of reputation loss. The research results show that an increase in some agents' reputation losses leads to a decrease in their revenue losses, but it may cause an increase in the social revenue loss. We perform a comprehensive loss analysis to evaluate the performance of reputation loss. Finally, the case study and comparative analysis reveal the feasibility and advantages of the proposed method. [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]

The paper aims to present one of the extensions of the single-valued neutrosophic set, by utilizing the features of the exponential and logarithmic (EL) parameters. The present idea is to integrate the features of the EL operations into the standard neutrosophic set to enhance their diversity. The proposed set is named an exponential-logarithm single-valued neutrosophic set. The major feature of the proposed set is to include one attitude character parameter which can help the decision-maker to give their decision as per their needs. In the defined set, the uncertainties which are present during the analysis are handled with the help of the advancement of the neutrosophic set. Later on, we stated some basic operations to aggregate the information by defining the several weighted operators. The characteristics of these proposed operators are also illustrated in detail. In addition to it, we state the new exponential entropy measure and investigate the impact of the membership degrees of the proposed set. Finally, we construct a multi-attribute group decision-making algorithm to address the decision-making problems based on the stated concept. The attribute weights are derived by using exponential entropy measures. Several case studies are illustrated and the performance of the algorithm is compared with several existing studies. The sensitivity analysis and advantages of the proposed algorithm are also stated. • Presented a new exponential-logarithm-based SVNS to handle the uncertainties. • Define new entropy measure for weight extraction. • Proposed MAGDM algorithm based on proposed operations. • Several case studies are taken to illustrate the method. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy (IF) theory has become main approach to representing imprecision and vagueness. The IF divergence measure (IFDivM) based on Jensen–Shannon divergence is perhaps the most widely used measure to compare the similarity of multiple intuitionistic fuzzy sets (IFSs). In the present paper, this IFDivM is examined and applied to multiple examples. It is found that some extant IFDivMs hardly satisfy the axiomatic definition, and in a few cases even unable to show divergence of trivial IFSs. To address these inconsistencies, a new IFDivM based on Jensen–Shannon divergence is proposed, free from these problems. The effectiveness of the proposed IFDivM is tested on several critical cases, and precise analysis of its properties is performed. It is proved that the proposed IFDivM satisfies the axiomatic definition of IFDivMs. To illustrate the practical significance of the IFDivM, a novel intuitionistic fuzzy (IF) TODIM method, based on the proposed IFDivM, is developed, termed as GIF-TODIM method. Unlike the existing IF-TODIM methods, GIF-TODIM does not suffer from the revere ordering inconsistencies. The proposed GIF-TODIM method and the proposed IFDivM are applied to a real-world case study on supplier selection. A detailed comparative analysis is performed taking the TOPSIS method and other IFDivMs as baselines. The role of attitude on the final choice is analyzed in great detail. It is found that the proposed GIF-TODIM method is indeed useful, effective, and superior to the counterpart methods, when it comes to real-world situations. Concomitantly, in the present work, it is also revealed that the TOPSIS method based on the 2-D Hamming distance is a special form of the proposed GIF-TODIM method, when decision-makers have the same attitude towards losses and gains. Thus, an interesting relationship between TOPSIS and TODIM is identified under the intuitionistic fuzzy environment, which is bound to propel significant research in the area of decision making under uncertain conditions. As a whole, the article offers comprehensive analyses of IFDivMs and the TODIM method under the intuitionistic fuzzy environment. • Construct a new IF divergence measure (IFDivM) based on Jensen–Shannon divergence. • Develop a GIF-TODIM method based on the new IFDivM. • Apply the proposed GIF-TODIM method to a supplier selection problem. • Provide parameter analysis and comparative analysis with TOPSIS and other IFDivMs. • Show that TOPSIS based on 2-D Hamming distance is a special form of our GIF-TODIM. [ABSTRACT FROM AUTHOR]

During the multi-attribute group decision-making (MAGDM) processing, the individuals often hold different opinions about the alternatives. It is necessary to aggregate the different individual opinions into a unified group opinion. In the real world, experts sometimes use linguistic expressions to evaluate attributes in uncertain environments. To address the problem of reducing the information loss of expert opinion aggregation in MAGDM, this paper proposes a MAGDM approach based on linguistic concept lattices in the context of uncertain linguistic expression. A linguistic concept lattice for multi-expert linguistic formal context is first constructed based on linguistic truth-valued lattice implication algebra, which can express both comparable and incomparable linguistic information in the decision-making process. Different expert opinions are aggregated via the extent of fuzzy linguistic concepts, which can reduce information loss in the aggregation process. Second, meet-irreducible elements in the linguistic concept lattice are introduced to reduce the computational complexity of obtaining all fuzzy linguistic concepts in the decision-making process. the distance between the intents of different fuzzy linguistic concepts is considered to enhance the rationality of linguistic decision results. In addition, the expert's decision-making process for each alternative is visualized via linguistic concept lattices. Finally, the case study and comparative analysis illustrate the validity and rationality of the proposed approach in MAGDM with linguistic information. • The fuzzy linguistic concepts are used to aggregate expert opinions. • The comparable and incomparable linguistic information is handled in MAGDM. • Visualize the decision-making process of experts by constructing concept lattices. • A novel linguistic concept lattice-based MAGDM approach is proposed. [ABSTRACT FROM AUTHOR]

Machine learning techniques have been widely used and are mostly performed by predicting point estimation. Nevertheless, there are various scenarios that require more information beyond only point estimation. Probabilistic prediction is a typical research topic that provides probability distribution while predicting, which addressed with much attention recently. Most of the previous works make an assumption on the probabilistic distribution to a certain extent, which may lead to potential errors in the subsequent decision-making procedure. In this paper, we propose a distribution-free method for regression problems on real-value response under the probabilistic prediction framework and present an effective boosting-based method to perform the training process. Moreover, we introduce a further improved method to accomplish an unbiased mean estimation of the target distribution. Thorough experiments on multiple benchmark data are conducted to demonstrate the effectiveness of the proposed method with regard to different measures. • A distribution-free framework for probabilistic forecasting. • Use a mixture of Gaussian distribution as the target distribution. • An effective boosting-based strategy. • Providing unbiased mean estimation. [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]

Trajectory prediction aims to estimate the future location of the vehicle based on its historical motion state, which is essential for driving decision-making and local motion planning of smart vehicles. However, affected by the multiple complex interaction in the traffic scene, predicting future trajectory accurately is a challenging task. The majority of existing methods only focus on modeling the inter-vehicle interaction, while ignoring the influence of road alignment and driver's lane-change intention, making the poor performance of models, especially for long-term prediction or when the vehicle maneuvers laterally. To overcome the deficiencies, this paper proposes Intention-convolution and Hybrid-Attention Network (IH-Net) for reliable trajectory prediction. Specifically, we analyze the correlation of lane-change behavior and the motion state of the vehicle, and then the Intention-convolutional Social Pooling module (I-CS) is introduced to extract complete interaction including the driver's lane-change intention and inter-vehicle interaction. In addition, we construct a novel Hybrid Attention Mechanism (H-AM) to explore the trajectory periodicity formed under the restriction of road alignment, as well as the impacts of different features on trajectory prediction, which is used to improve the model's long-term prediction capacity. The model's prediction accuracy with RMSE loss function is tested on two public datasets NGSIM and highD, and the results demonstrate that IH-Net remarkably outperforms the state-of-art methods in long-term prediction. [ABSTRACT FROM AUTHOR]

This paper focuses on the approach for solving the satisfactory Pareto-optimal solutions of the general form of bilevel multi-followers programming(BMFP), including the BMFP with partial shared variables among followers, the so-called referential-uncooperative BMFP, the uncooperative BMFP, and the cooperative BMFP. Without losing generality, the BMFP with partial shared variables among followers will be taken as a representative to explain the universal applicability of the proposed generic interactive intuitionistic fuzzy method. Based on the theory of intuitionistic fuzzy set, the new definition of satisfactory Pareto-optimal solutions is introduced by means of membership function, non-membership function, and score function. In order to obtain satisfactory Pareto-optimal solutions of BMFP with partial shared variables among followers, a prior minimization optimization problem and a series of corresponding auxiliary maximization optimization problems are formulated, and we strictly prove that the optimal solution of the prior minimization optimization problem is not necessarily the satisfactory Pareto-optimal solutions, but at least one satisfactory Pareto optimal solution can be derived by further solving these auxiliary maximization optimization problems. Then, a detailed interactive intuitionistic fuzzy algorithm framework that consider both the balance of satisfactory degree between the leader and the followers and the balance of satisfactory degree among all followers is proposed. In the part of numerical experiments, four examples(including a linear BMFP problem with partial shared variables among followers, a BMFP model of a road network problem, an uncooperative BMFP problem, and a nonlinear BMFP problem with partial shared variables among followers) are illustrated to demonstrate the applicability, effectiveness and superior performance of the proposed technique. • The definition of satisfactory Pareto-optimal solutions to BMFP is first introduced. • A prior optimization problem and the auxiliary problems are formulated. • At least one satisfactory Pareto-optimal solution can be derived is strictly proved. • Detailed interactive intuitionistic fuzzy algorithm framework is established. • Four numerical examples are given to explain the effectiveness of proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces an extension of a well-known Multiple Criteria Decision Aiding method, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Most of the TOPSIS applications assume that preferences are monotonic for each evaluation criterion and that qualitative scales are converted into quantitative ones before the method is applied. However, both assumptions have been subject of discussion and criticism in the literature. To this solution, this paper introduces a normalization technique based on simulations that permit taking into account non-monotonic preferences as well as qualitative criteria. An additional novelty lies in the integration of the Multiple Criteria Hierarchy Process, which extends the applicability of the method to problems in which criteria are hierarchically structured. To deal with robustness concerns, the Stochastic Multicriteria Acceptability Analysis will be used in the new proposal, giving information in statistical terms on the goodness of the considered alternatives. The new method has been applied to evaluate a set of banks listed in the LSE's FTSE350 Index. [ABSTRACT FROM AUTHOR]

• The paper explores a new family of methods for heterogeneous MAGDM. • Diversified opinions are unified into random values to retain structural feature. • The SPM is developed to measure the superiority probability between alternatives. • Probabilistic rank is introduced to fit with heterogeneous decision environment. Heterogeneous multi-attribute group decision making (heterogeneous MAGDM) is a hot topic in complicated decision-making environment. This paper proposes a new approach for heterogeneous MAGDM problems, including three main research points. Firstly, different from the commonly used unified transformation process, the study provides a randomization method to transform heterogeneous opinions into random values, which can keep the structural features of the original data as much as possible. Secondly, the random values are integrated into superiority-probability-based pairwise comparison matrix (SPM) by combining stochastic simulation and multi-attribute decision making methods. Then, a maximization-similarity-based consensus approach is developed to obtain the collective SPM. The collective SPM can not only objectively present the superiority relationship between any two alternatives, but also balance the opinions of multiple experts. Thirdly, a new type of ranking, probabilistic rank order, is proposed, which is more consistent with the heterogeneous and uncertain characteristics of the decision-making opinions. At last, an example regarding missile weapon system selection is chosen from previous studies to illustrate the validity of the proposed methods. Another example about the predictive evaluation of city development potential is constructed to further illustrate the practical application prospects of the research methods. [ABSTRACT FROM AUTHOR]

• The paper explores a new family of methods for heterogeneous MAGDM. • Diversified opinions are unified into random values to retain structural feature. • The SPM is developed to measure the superiority probability between alternatives. • Probabilistic rank is introduced to fit with heterogeneous decision environment. Heterogeneous multi-attribute group decision making (heterogeneous MAGDM) is a hot topic in complicated decision-making environment. This paper proposes a new approach for heterogeneous MAGDM problems, including three main research points. Firstly, different from the commonly used unified transformation process, the study provides a randomization method to transform heterogeneous opinions into random values, which can keep the structural features of the original data as much as possible. Secondly, the random values are integrated into superiority-probability-based pairwise comparison matrix (SPM) by combining stochastic simulation and multi-attribute decision making methods. Then, a maximization-similarity-based consensus approach is developed to obtain the collective SPM. The collective SPM can not only objectively present the superiority relationship between any two alternatives, but also balance the opinions of multiple experts. Thirdly, a new type of ranking, probabilistic rank order, is proposed, which is more consistent with the heterogeneous and uncertain characteristics of the decision-making opinions. At last, an example regarding missile weapon system selection is chosen from previous studies to illustrate the validity of the proposed methods. Another example about the predictive evaluation of city development potential is constructed to further illustrate the practical application prospects of the research methods. [ABSTRACT FROM AUTHOR]

• New Fermatean fuzzy cross entropy-based optimization weighting procedure is developed. • An integrated Fermatean fuzzy weighting procedure is developed for decision expert's weight. • A double normalized MARCOS approach with Fermatean fuzzy information is proposed. • A case study of warehouse location for the automotive manufacturing company is discussed. • The comparative study and sensitivity investigation are made to confirm the robustness of the obtained results. The automotive industry is one of the most competitive sectors, and it requires a well-structured logistics system to meet the industry' vital requirements such as just-in-time, lean and agile supply chain operations, productivity and sustainability. Well-located and well-designed warehouses can make reaching these aims for the automotive industry possible and more accessible. Hence, determining a location for a warehouse is a highly critical, tactical, and managerial resolution for the automotive industry, as there is a strong correlation between well-located warehouses and the well-structured logistics network in the automotive industry. Although the WSS is a significant decision-making problem, we observed four critical and severe gaps in the existing literature: (1) the authors preferred to apply traditional objective & subjective frames, and they overlooked existing highly complicated uncertainties. (2) The number of studies focusing on the WSS problem in the automotive industry is surprisingly scarce. (3) It is not sufficiently clear how these factors used in the previous studies were determined, which causes doubts about their reliability. (4) there is no satisfactory evidence of which approaches were used to identify the factors in the previous papers. By considering these gaps, we propose two approaches which can be accepted as a novelty of the paper. First is the extension of the Delphi techniques based on the Fermetean fuzzy sets (FFs) used for identifying the criteria. It also combines the two traditional approaches (i.e., literature review and professionals' evaluations to identify the criteria) with the FF-Delphi technique. The second is the Double Normalized MARCOS approach based on FFs (FF- DN MARCOS) implemented to identify the weights of the criteria and ranking performance of the alternatives. The proposed model was implemented to identify the best warehouse location for the automotive manufacturing company. The results show that the C1 "energy availability & cost" criterion is the most influential criterion and the C5 proximity to port and customs criterion is the second most crucial factor. Then we executed a comprehensive sensitivity analysis, and the results approved the suggested model's validity and robustness despite excessive modifications in the criteria weights. [ABSTRACT FROM AUTHOR]

The issue of classification of legal norms is much debated in legal theory. One of the most recurring topics in this area is the distinction between two types of legal norms: rules and principles. The papers presents a formal model of principle-based reasoning. The model presented in the paper provides formal grounds for decision making including principles. The model is based on the goal- and value-based reasoning mechanism, which introduces a framework allowing for autonomous goal-setting based on values that should be promoted. • Comprehensive discussion of the concept of legal principle in decision making. • A novel proposal of formalization of legal principles. • A novel proposal of formalization of reasoning with legal principles. • An argumentation framework allowing for decision making in a legal environment. • Decision making in situations with a lack of satisfactory options. [ABSTRACT FROM AUTHOR]

Conflict issue has been a topic of immense interest in evidence theory because the current methods still do not accurately reflect the conflict degree between evidence bodies. Thus, this paper defines mass Jensen–Shannon divergence (MJSD) to reflect the conflict degree. MJSD considers both the external difference and the external difference of the mass function. Furthermore, this paper proposes an enhanced mass Jensen–Shannon divergence (EMJSD). EMJSD has all the advantages of MJSD. It is a distance-like measure, which satisfies some axiom of distance measure, such as non-negativity and symmetry. Compared with the current methods, EMJSD fully measures the internal and external difference between the mass functions. Therefore, EJMSD overcomes the drawbacks of the classical methods when measuring differences between mass functions. Some numerical examples are used to explain its properties and advantages. Additionally, this paper presents a new method to fuse multi-source information. A numerical example shows that this fusion method is more useful for decision-making under the framework of multi-source information fusion. We then construct a new generation method of mass function in the data-driven environment. The experimental results show that when compared with other method, the new generation method more accurately reflects the support degree of proposition. Finally, this paper applies the generated mass functions to the new fusion method and other methods. The results indicate that the new fusion method has better recognition performance in practical applications. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved filtering algorithm for regular constraint, which demand that the sequence of all assignments for variables can be accepted by a Deterministic Finite Automaton (DFA) or a regular language. Regular constraint is a type of global constraint in Constraint Satisfaction Problems (CSPs) and a natural expression of deterministic patterns in planning and decision making, and it utilizes the properties of DFA to pre-specify the optional paths for multiple variables. Thus, it is sensible to improve the filtering algorithm that enforces Generalized Arc Consistency (GAC) for regular constraint in order to minimize memory and time consumption. With the goal in mind, we encode DFA using bit vectors and then devise a bit operations algorithm to maintain the domain consistency for regular constraint. Lastly, we carry out an experiment to compare to the state-of-the-art filtering algorithm. The experimental results demonstrates that the proposed algorithm significantly improves both memory and time. • The improved filtering algorithm improves efficiency for regular constraints. • Our filtering algorithm uses a large number of bit operations. • The proposed algorithm maintains generalized arc consistency. • The experimental results show that our algorithm provides a significant improvement. [ABSTRACT FROM AUTHOR]

• Linguistic Z-numbers are utilized to represent risk evaluation information. • The DEMATEL method is utilized to analyze relationships among human error factors. • The ORESTE method is extended for ranking the risk of human error factors. The human error factor is one of the leading causes of medical errors. Among risk analysis techniques for human error factors, HFACS (Human Factor Analysis and Classification System) method has been regarded as one of the most valuable approaches due to its advantage in potential failure classification. However, the conventional HFACS method is insufficient to handle the risk analysis problem for human error factors considering the interactive relationship among these factors. Moreover, the current fuzzy HFACS frameworks cannot address quantitative risk analysis issues, including imprecision and reliability of information. Thus, this paper constructs an integrated linguistic Z-number-based HFACS framework for analyzing the risk of human error factors. This framework can capture the uncertainty and reliability of the risk evaluation information and the interactive relationships among factors. First, the linguistic Z-number-DEMATEL (Decision Making Trial and Evaluation Laboratory) method is used to generate the comprehensive risk matrix of human error factors identified by the HFACS method. Then, an extended linguistic Z-number-ORESTE method based on the score function is presented to prioritize the risk of human error factors, which can show the preference, indifference, and incomparability relationship among human error factors. Finally, a case study of a healthcare system is conducted to illustrate the reliability of the proposed method. The result of this case indicates that inadequate resources are the most severe risk. Sensitivity analysis and comparative analysis indicate the necessity of considering the effect of the semantics of language terms and the interrelationships between human error factors on risk analysis results. These results show that the proposed hybrid framework is a reliable means to analyze the risk of human error factors within the HFACS method. [ABSTRACT FROM AUTHOR]

With the increasing demand for Chinese cold chain logistics, accurate forecasting becomes significantly important for decision-making and planning, but the data scarcity is still a challenging problem for modeling method. This paper proposes a novel grey prediction model with bi-level structure to improve the performance of cold chain logistics demand forecasting. The model adapts to the data characteristics and generates corresponding background series to adjust the parameters. A differential evolutionary algorithm is employed to merge the time response functions generated from the bi-level structure. The proposed model extends the applicability compared with existing similar models and can provide higher accuracy for data sequences with a high developing rate. The model outperforms the classic grey model and its extensions in terms of forecasting accuracy. The trend of Chinese cold chain logistics demand from 2021 to 2025 is studied with the proposed model. The computational results show that the proposed grey prediction model is significantly better than other models. [ABSTRACT FROM AUTHOR]

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation provided by these algorithms enables transparency and explainability, which is necessary for decision making in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models, data sets, and source code publicly available online. [ABSTRACT FROM AUTHOR]

• A multi-stage decision model for UPG carbon-neutral potential analysis is proposed. • Rough-fuzzy numbers are utilized to manipulate internal and external uncertainties. • The interrelationships between attributes are taken into account by the RF-DEMATEL. • A RF-MARCOS method is developed to identify the carbon-neutral potential of each UPG. • The study aims to contribute theoretically and practically to the related fields. As a hub connecting power production and consumption, the urban power grid (UPG) is the basis for realizing the carbon–neutral goal. To ensure carbon–neutral progress, it is necessary to conduct an effective analysis of carbon–neutral potential from the UPG perspective. Given this, this paper proposes a multi-stage decision model that integrates Decision Making Trial and Evaluation Laboratory (DEMATEL) and Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) methods under the rough-fuzzy (RF) environment. Firstly, a multi-dimensional attribute system is innovatively established, and then the rough-fuzzy number is presented to assemble assessment information of groups and to manipulate internal and external uncertainties during decision process. Subsequently, the RF-DEMATEL is utilized to determine the importance-weights of the attributes, in which the interrelationships between attributes are fully considered. Finally, the RF-MARCOS is developed and applied to obtain carbon–neutral potential analysis results based on utility functions in relation to the ideal and anti-ideal solutions. A case study is conducted based on the proposed methodology, and the stability and robustness of the results are verified through sensitive and comparative analysis. This research contributes theoretically and practically to related literature by providing a systematic model capable of handling decision problems with complexity and uncertainty. [ABSTRACT FROM AUTHOR]

The literature on fraud analytics and fraud detection has seen a substantial increase in output in the past decade. This has led to a wide range of research topics and overall little organization of the many aspects of fraud analytical research. The focus of academics ranges from identifying fraudulent credit card payments to spotting illegitimate insurance claims. In addition, there is a wide range of methods and research objectives. This paper aims to provide an overview of fraud analytics in research and aims to organize the discipline and its many subfields. We analyze a sample of almost 300 records on fraud analytics published between 2011 and 2020. In a systematic way, we identify the most prominent domains of application, challenges faced, performance metrics, and methods used. In addition, we build a framework for fraud analytical methods and propose a keywording strategy for future research. One of the key challenges in fraud analytics is access to public datasets. To further aid the community, we provide eight requirements for suitable data sets in research motivated by our research. We structure our sample of the literature in an online database. The database is available online for fellow researchers to investigate and potentially build upon. • We analyze close to 300 manuscripts related to fraud analytics. • 23 unique fraud analytical challenges are detected and described. • We provide a wholistic overview of fraud analytical domains and methods. • We map out 4 key areas for future research in fraud analytics. • We present a novel taxonomy for fraud analytical literature based on our research. [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]

For a proper management of large-scale group decision-making (LSGDM) problems, it is essential to consider multiple factors such as the relationship between the decision-makers, the dimensionality reduction, and the cost of guaranteeing that the involved decision-makers reach an agreement. Therefore, this paper proposes an adaptive consensus framework for LSGDM to efficiently derive agreed decisions when many decision-makers are required to participate in the decision process. First, the notion of trust propagation is applied to construct a trust network between decision-makers, which is combined with the similarity between their opinions to classify them into clusters that are weighted according to their size, and the concept of harmony that is measured by the harmony degree. Afterwards, a consensus-reaching process is carried out taking into consideration both intra- and inter-cluster consensus degrees. According to the intra- and inter-consensus level of each cluster, we propose an adaptive process with three different adjustment mechanisms based on chance-constrained robust minimum cost consensus models. The performance of the proposed framework is then illustrated by a numerical experiment. Finally, a sensitivity analysis is performed to show the validity of the proposed model. • A trust-similarity clustering method considering trust propagation is proposed. • The concept of harmony degree is proposed to measure the weight of cluster. • Both the intra- and inter-cluster consensus level are measured simultaneously. • A chance-constrained robust-based minimum cost consensus model is established. [ABSTRACT FROM AUTHOR]

This paper studies the optimal selection of planting Chinese Herbal Medicine (CHM), which has many attributes and stakeholders, and where some assessment information cannot be obtained directly from the official websites. The assessment information is categorized into two groups: precise and interval numbers that can be obtained from the official sites, and precise information obtained through expert surveys and questionnaires. A three-step group decision-making mechanism is proposed for the CHM problem. First, an analytical method based on interval evidence reasoning is formed to unify different forms of information into the same interval belief structure. Next, a model based on the geometric area method is formed to obtain the weights of the attributes. Third, the extended interval regret theory is used to rank the alternative solutions. Applying perceived utility value and the ranking of the alternatives, an optimal solution for planting CHM is found. Comparative and sensitivity analyses highlight the flexibility and reliability of the suggested solution. By evaluating the various parameters, decision-makers in CHM can prioritize their decisions for maximal benefit. [ABSTRACT FROM AUTHOR]

To efficiently manage information security, firms typically allocate limited budgets to protect their information assets based on asset types and hacker types. This paper builds a game-theoretical model to discuss the strategic interaction between a firm that tries to protect its core and non-core assets and a strategic hacker that can launch a targeted attack or indiscriminate attack. The analysis results show that irrelevant of the hacker types, when the firm's core assets become more important or more vulnerable, both the firm and the hacker increase their efforts on core assets. Considering hacker types, the hacker who launches a targeted attack will always exert the highest efforts and lowest efforts on the firm's two types of assets, while exerting moderate efforts when launching an indiscriminate attack. While considering asset types, when the gap between the value of core and non-core assets is large, the hacker exerts more effort on the core asset than that on the non-core asset, thus the firm has to exert more effort on the core asset than that on the non-core asset. With an increasing budget, the firm will allocate more security efforts for core assets than for non-core assets, while the hacker always decreases its efforts irrelevant to hacker types. Moreover, we find that the hacker can get higher payoffs from a targeted attack than an indiscriminate attack when considering a budget while getting equal payoffs from the two hacker types without a budget, however, the firm always needs to pay more to respond to the targeted attack no matter considering the budget or not. These findings highlight the importance of distinguishing asset type and hacker type and give some management insights that can guide a firm that faces a security budget to determine the optimum security effort. Future research directions are discussed based on the limitations and possible extensions of this study. [ABSTRACT FROM AUTHOR]

Power Plants (PPs) is considered as critical facilities in each region because of essential role through energy generation processes. These facilities are also depended to water availability especially in water stress areas. Due to the critical water shortage in many areas around the world, it is necessary to make an optimal condition among water consumption and the increasing demand for electricity to prevent any further conflict of interests between industry, householders and the environmental goals. There are different techniques for controlling Water Consumption (WC) in these industries. This paper develops a smart Decision Support System (DSS) for monitoring, prediction and control sections based on Artificial Intelligent (AI) and integration of the PESTEL matrix and Multi Criteria Decision Making (MCDM) methods. Monitoring section comprises Fuel Consumption (FC), Atmospheric Temperature (AT), Power Plant Temperature (PPT) and Power Plant Efficiency (PPE), in which FC has the most influence on WC based on ANOVA evaluations in both cold and warm seasons. The prediction results have illustrated that Adaptive Neuro Fuzzy Inference System model is more efficient for the WC estimation with a correlation coefficient over 0.99. Ordered Weighted Averaging (OWA) also demonstrated that in the optimistic and pessimistic states, the most priority is linked to E3 (Establishment of evaporation control systems by contractor companies and concluding a guaranteed purchase contract with a power plant worth one and a half times the current amount of water price). In the last step of technical approaches, the smart controlling system is added for execution of water-energy nexus in the PP based on proportional–integral–derivative controller system. Finally, the performance of the DSS is approved with more than 80% agreement of experts and more than 90% precision in prediction procedure through this investigation. Application of this DSS can also be helpful for developing countries to achieve the UN Sustainable Development Goals. [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]

Ordinal classification is a common classification problem, which widely exists in multi-attribute decision making problems. The dominance-based rough set approach (DRSA) is a knowledge acquisition tool for ordinal classification tasks. Nevertheless, the existence of noise information in the collected data greatly reduces the accuracy of the DRSA and some extended model. An effective way to solve this issue is to improve the robustness of these models. In addition, most existing feature selection strategies based on dominance rough sets mainly focus on the monotonic classification consistency between features and decision, but ignoring the classification information provided by features combination. Motivated by these two issues, this paper proposes a robust fuzzy dominance rough set model to combat noise interference and develop a feature selection approach based on the proposed model for ordinal classification tasks. First, a non-linear vague quantifier is adopted to construct the robust model, and related dependency function is introduced. Second, the rank entropy based uncertainty measures are explored to characterize the contribution of features combination for ordinal classification. Based on the proposed uncertainty measures, a new feature evaluation index is presented. Meanwhile, the corresponding feature selection algorithm is designed. Finally, numerical comparative experiments are performed to indicate that the proposed model and feature selection algorithm have good capability. • The fuzzy dominance rough set using a non-linear vague quantifier is proposed. • An uncertainty measure describing attribute combinations is proposed. • A classifier embedded feature selection algorithm is designed. • Experiments show the good performance of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]