Search

In contemporary healthcare systems, the selection and judicious choice of the right medical device becomes paramount consideration for accurate diagnosis, patient care, mitigate risk and cost incurred on diagnosis of disease. Moreover, medical devices are requisite indispensable tools to help healthcare professionals for effective diagnosis, selection of the right treatment amongst different options, and continuous monitoring of the patient. Thus, to select the best manufacturing company is as equally important to achieve the desired goals. This paper presents a decision making algorithm based on fuzzy theory that helps the decision maker in selecting the most suitable worldwide medical device manufacturer. Fuzzy theory is extensively used in managing databases and analyzing data to summarize information and draw meaningful conclusions. This paper utilizes the aggregation operators within the context of the complex q-rung orthopair fuzzy environment by utilizing Yager t-norm and t-conorm operations. Some aggregation operators are applied, namely, complex q-rung orthopair fuzzy Yager weighted geometric operator, complex q-rung orthopair fuzzy Yager ordered weighted geometric operator, complex q-rung orthopair fuzzy Yager weighted averaging operator and complex q-rung orthopair fuzzy Yager ordered weighted averaging operator. Additionally, the notable properties of these operators are proved. These aggregation operators are generated specifically for multi-criteria decision-making. Decision making algorithm is used to choose the most relevant medical device manufacturer (alternative) to validate its efficacy. The results demonstrate the versatility of parameters, adaptability to multi-criteria decision making, and efficiency of the proposed aggregation operators and algorithm. Test with specific criteria is also conducted to verify the legality and validity of the decision making algorithm. At last, the acquired results underwent a comparative analysis against various established methodologies, confirming the method's capacity to produce precise and accurate outcomes. • Multi criteria decision making algorithm is devised in fuzzy environment. • Aggregation operators are introduced and notable properties are investigated. • Test with specific criteria is conducted for the validity of proposed operators. • The algorithm is utilized in medical industry to choose best device manufacturer. [ABSTRACT FROM AUTHOR]

Three-way multi-attribute decision-making (3MADM) integrated with double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) can not only effectively express the uncertainty of language, but also help reduce the risk of wrong decision-making. However, the existing compared methods for DHHFLTSs dismiss the variances in psychological reference points, resulting in mismatches in form and connotation for some linguistic terms. Furthermore, it is difficult or even impossible to obtain an accurate degree of difference using the existing DHHFLTS distance method for these linguistic terms. This directly affects the accuracy of obtaining conditional probabilities results in the three-way decision model. Therefore, this paper introduces the concept of the superior gradus for double hierarchy linguistic term set (DHLTS) and double hierarchy hesitant fuzzy linguistic element (DHHFLE), respectively. Then, some novel compared methods are defined that allow the identification of differences between linguistic variables. Subsequently, based on the superior gradus, a novel distance measurement is designed with a risk parameters. Through the adjustment of risk parameters, this method can respectively obtain optimistic and pessimistic results. Also, the relative loss functions designed for DHHFLTSs aim at getting more objective decision-making results. Finally, the paper proposes a novel 3MADM method under the double hierarchy hesitant fuzzy linguistic information system (DHHFLIS) and applies it to service assessment. To verify the effectiveness and rationality, the medical diagnosis data set is used, and the results are compared with other classic MAMD methods. • 3MADM with DHHFLTS reduces the risk of wrong decision-making. • Superior gradus is introduced to identify DHHFLTS's variances. • Novel distance measurement is designed with a risk parameters. • Relative loss functions for DHHFLTS enhance objective results. • Novel 3MADM method is applied in service assessment and compared with classic MAMD. [ABSTRACT FROM AUTHOR]

As a three-way approximation scheme to deal with fuzzy set, shadowed set approximately divides fuzzy objects with uncertainty into elevated, reduced, and shadow areas by a pair of decision thresholds α , β , thus effectively reducing the uncertainty of information. However, the current construction of shadowed set is all based on a single principle, lacking the comprehensive consideration of multiple principles, and these construction principles focus on the rationality of semantic interpretation of model construction process and threshold determination, and lack of consideration of the validity of partition results. To resolve these issues, this paper proposes a new shadowed set model through the game analysis between uncertainty and decision cost (UC-GTSS). First, Combining shadowed set construction with game theory, UC-GTSS is proposed based on game competition mechanism. Through the game analysis of uncertainty and decision cost, the optimal α , β is found, and a game payoff fusion algorithm based on Topsis is proposed. Second, the definition of UC-GTSS model expression, game player, strategy, payoff function, equilibrium analysis, optimization α , β determination and the influence of model parameters are analyzed and discussed. Third, the UC-GTSS model is extended and discussed based on the validity of the partition results, such as the game between approximate classification accuracy and coverage, the game between the Gini coefficient of the partition areas. Finally, through the analysis of algorithms, instances and data experiments, the construction process, validity, and rationality of UC-GTSS and its extended model are demonstrated. • This paper proposes a new shadowed set model through the game analysis between uncertainty and decision cost (UC-GTSS). • First, Combining shadowed set construction with game theory, UC-GTSS is proposed based on game competition mechanism. Through the game analysis of uncertainty and decision cost, the optimal threshold is found, and a game payoff fusion algorithm based on Topsis is proposed. • Second, the definition of UC-GTSS model expression, game player, strategy, payoff function, equilibrium analysis, optimization threshold determination and the influence of model parameters are analyzed and discussed. • Third, the UC-GTSS model is extended and discussed based on the validity of the partition results, such as the game between approximate classification accuracy and coverage, the game between the Gini coefficient of the partition areas. • Finally, through the analysis of algorithms, instances and data experiments, the construction process, validity, and rationality of UC-GTSS and its extended model are demonstrated. [ABSTRACT FROM AUTHOR]

Health-care waste (HCW) contains numerous hazardous, infectious, and radioactive contents that may cause serious health issues for society and drastic environmental problems. Therefore, it is crucial to dispose of HCW safely and appropriately. Four commonly used HCW disposal methods are incineration, steam sterilization, microwave, and landfill disposal. To select an appropriate HCW disposal method, there are several conflicting criteria. Furthermore, when information is lacking due to uncertainty and criteria weighing conditions are also unknown, the decision to choose an appropriate HCW disposal method becomes more challenging. So, the objective of this paper is to introduce a novel integrated multi-criteria group decision-making (MCGDM) approach to evaluate HCW disposal methods in which incomplete information is quantified using intuitionistic fuzzy sets (IFSs), unknown criteria weights are evaluated using the maximizing deviation method, and the evaluation of HCW disposal methods is done by applying the Archimedean t -norm and t -conorm-based interactive intuitionistic fuzzy weighted aggregated sum product assessment (WASPAS) method. Herein, the Archimedean t -norm and t -conorm cover a wide range of t -norms and t -conorms, including the Algebraic, Einstein, Hamacher, and Frank, etc., by adopting prescribed combinations of additive generators. The proposed MCGDM approach has been applied to evaluate the above-listed four HCW disposal methods for a case study conducted by Mishra et al. (2020) in the context of India. A sensitivity analysis is performed to examine the impact of changes in the parameters involved in the proposed MCGDM approach. A comparative study is also performed with some well-known multi-criteria decision making(MCDM) and MCGDM methods to demonstrate the consistency and stability of the proposed approach. Findings suggest that the newly developed MCGDM approach is more general, flexible, and provides realistic results to evaluate HCW disposal methods under uncertainty and unknown criteria weight information. • The paper proposes an Archimedean t-norm and t-conorm based IF-WASPAS method with unknown criteria weights to evaluate HCW disposal techniques. • The method applies IFS to quantify uncertainty while unknown criteria weights are evaluated using the maximizing deviation method. • Method has been applied to evaluate HCW disposal alternatives in context of India. • Sensitivity and comparative analyses are also conducted to examine the consistency and stability of the method. • The proposed MCDM approach is general,flexible, and provides realistic results under uncertainty and unknown criteria weights. [ABSTRACT FROM AUTHOR]

Recently, the increasing number of stroke survivors has created a greater demand for rehabilitation services. How to predict rehabilitation demands is important for policy makers in various countries. The demand prediction is essentially a multi-attribute decision-making problem. The diverse forms of information or data are often neglected or simplified in previous studies of decision-making problems. In this paper, heterogeneous multi-attributes including crisp numbers, interval numbers, and linguistic variables are defined to represent decision-relevant information concerning the patient's condition in cases when applied to real-life scenarios. Moreover, a deviation minimization optimization model combining CRITIC method and H2TLWMSM operator is proposed to assign the attribute weights with dual information. The WHMACBR system can be used to predict stroke rehabilitation demands for making the diagnosis and treatment decisions. The proposed decision-making system based on the CBR approach simulates the operations of human memory and reasoning, thus assisting physicians in addressing the ever-increasing demand for rehabilitation services of stroke survivors. Finally, the effectiveness and applicability of the case retrieval process of the presented CBR model are demonstrated through a stroke rehabilitation case study (top 3 cases ranked by similarity to the target case are C 6 , C 2 , and C 3) and comparison analysis with relevant existing CBR models. This indicates that the proposed decision-making system, by considering heterogeneous multi-attributes and their weights, is capable of providing more comprehensive and rational decision-related information in the field of rehabilitation medicine. • The model can be used to handle heterogeneous information expressed by various formats of data in stroke rehabilitation. • A deviation minimization optimization model was proposed. • The subjective assessment matrix is constructed considering human expression habits. • The proposed model reflects the practical significance to accelerate the realization of intelligent rehabilitation. [ABSTRACT FROM AUTHOR]

While uncertainty quantification (UQ) has served a prominent role in ensuring the safety of dynamical engineering systems, the lack of an integrated approach to handle the aleatory nature of ground motion records, a.k.a., record-to-record (RTR) variability, remains a bottleneck in seismic design. This paper presents a novel approach with two key features. First, on the multi-criteria decision-making (MCDM) front, a general-purpose collective decision support system is introduced by integrating Monte Carlo simulation, automated machine learning (AutoML), and a hybrid fuzzy-outranking MCDM technique. This allows for robust uncertainty capture at a group level without compromising computational efficiency—a departure from the traditional trade-off between MCDM competency in uncertainty handling and computational burden. Second, from an Earthquake Engineering perspective, the established AutoML-based community-level MCDM approach is combined with an efficient metamodel-aided reliability-based design optimization technique and a novel probabilistic-fuzzy seismic-design safety index. Conflicting criteria are classified into structural performance, design safety, and construction cost metrics. The results indicate that the developed intelligent seismic design paradigm properly captures uncertainties rooted in the RTR variability, input data, criterion weighting, and decision-makers' preferences from a community-level standpoint. It also offers acceptable error metrics during the ranking procedure in a user-friendly environment, at a significantly reduced computational expenditure. Moreover, it can be concluded that there is no imperative need for developing complex decision tools for robust-to-uncertainty seismic design. The implementation of the proposed MCDM-based UQ framework has reduced the design cost of an existing dam by 17%. [Display omitted] • A general-purpose stochastic-fuzzy multi-criteria decision-making tool is introduced. • An MCDM-based uncertainty quantification tool is suggested for robust seismic design. • A probabilistic-fuzzy seismic-design safety index captures ground motion variability. • An automated machine-learning pipeline reduces computational decision-making costs. • A hybrid stochastic-fuzzy MCDM dataset for earthquake/risk engineers is developed. [ABSTRACT FROM AUTHOR]

In this paper, a non-zero-sum game model based on a multi-objective evaluation model was adopted to solve the dynamic task allocation problem of underwater multiple Autonomous Underwater Vehicle (multi-AUV). By introducing a functional coordination mechanism, the high degree of coordination in practical confrontation scenarios was reflected, and the autonomous decision-making and task allocation process of multiple AUVs were clarified. Secondly, a multi-objective evaluation model including survival value, strike income, and ammunition loss was constructed, and weighted processing was performed on the multiple objectives using the Analytic Hierarchy Process (AHP) method, obtaining the benefits of each non-zero-sum strategy in dynamic games. In addition, a particle swarm optimization (PSO) algorithm that combines the theory of good point sets theory (G) and speed control factors (S) for improvement, called GSPSO, was used to find the optimal strategy in the game and allocate tasks. Finally, simulation analysis showed that the collaborative system with functional coordination mechanisms significantly improved the combat capabilities of underwater multiple AUVs. The multi-objective evaluation model combined with AHP can correctly and comprehensively evaluate the advantages and disadvantages of each strategy and correctly respond to changing confrontation tasks and decision preferences. The proposed algorithm improved the convergence speed and global search ability by enhancing the diversity of the population and the effectiveness of iterative solutions, ensuring real-time decision-making and task allocation in complex high-dimensional dynamic games. • Game theory method of multi-AUV confrontation in complex underwater environment. • A multi-objective evaluation model to evaluate in a non-zero-sum strategy. • The Analytic Hierarchy Process (AHP) weighting method for decision-making. • The GSPSO algorithm for the Nash equilibrium of the non-zero-sum game. [ABSTRACT FROM AUTHOR]

Abstract Failure mode and effect analysis (FMEA) is a forward-looking risk-management technique used in various industries for promoting the reliability and safety of products, processes, structures, systems, and services. However, FMEA has many defects in practical experimentation. Therefore, this paper proposes a new model that uses multiple-criteria decision-making in combination with grey theory for FMEA. This approach has several advantages, such as being able to add the expected cost into the original risk priority number (RPN) to reflect the actual resource limitations, consider the different weights of severity, occurrence, detectability, and cost based on the best–worst method in RPN element calculation, and use the grey interval linguistic variables to manage information uncertainty. Furthermore, this study applied probability-based grey relational analysis to calculate the RPN, which preserves the information of prioritized failure modes through interval analysis. To demonstrate the usefulness and effectiveness of the proposed model, real data from an international electronics company were applied. The proposed model can provide an alternative risk priority solution for product development. Graphical abstract Highlights • This paper proposed a novel hybrid model for failure mode and effect analysis. • The expected cost was added in the original risk priority number (RPN) calculation. • We applied BWM in RPN element calculation and grey interval to manage information uncertainty. • The real data from an international electronics company were applied to demonstrate the model. [ABSTRACT FROM AUTHOR]

Highlights • Representation of highly uncertain information using PF/IVPF sets. • Development of risk attitude-based score functions for PF/IVPF values. • Construction of risk attitudinal assignment models in the PF/IVPF context. • Application to financial decision-making problems. • Comparative analysis with other outranking approaches. Abstract The aim of this paper is to develop a novel outranking method using a risk attitudinal assignment model under complex uncertainty based on Pythagorean fuzzy (PF) sets and interval-valued Pythagorean fuzzy (IVPF) sets. Additionally, this paper applies the proposed methodology to address financial decision-making problems to investigate its applicability and effectiveness in the real world. These developed methods and techniques can provide a new viewpoint for capturing the amount of information conveyed by PF and IVPF values and can fully take into account the decision maker’s attitude towards risk-taking in the face of high uncertainty. Considering the diverse attitudes toward risk neutrality, risk seeking, and risk aversion, this paper introduces novel measures of risk attitude-based score functions within the PF/IVPF environments and investigates their desirable and useful properties. These properties provide a means of establishing linear orders and admissible orders in the PF/IVPF contexts. This means that the proposed measures can overcome some drawbacks and ambiguities of the previous techniques through score and accuracy functions and can address incomparable PF/IVPF information more effectively. As an application of the risk attitude-based score functions, this paper develops novel risk attitudinal assignment models to establish a useful outranking approach for solving multiple criteria decision-making problems. Two algorithms for PF and IVPF settings are developed based on the concepts of a precedence frequency matrix and a precedence contribution matrix for conducting multiple criteria evaluation and the ranking of alternatives. A financing decision on aggressive/conservative policies of working capital management is presented to demonstrate the applicability of the proposed outranking approach in real situations. Moreover, a comparison to the technique of risk attitudinal ranking methods is investigated to validate the advantages of the proposed methodology. Furthermore, a comparative analysis with a newly developed outranking method, the IVPF-ELECTRE (for the elimination and choice translating reality), is conducted via an application to the investment problem of software development projects. Compared with these benchmark approaches, the proposed methods can produce a more reasonable and persuasive result for ranking the order relationships of alternatives in the highly uncertain context. The practical studies and comparative discussions demonstrate an excellent performance of the developed methodology that is effective and flexible enough to accommodate more-complex decision-making environments. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a novel MCGDM method under dual hesitant fuzzy information. • Several professional degree measures are proposed in this paper. • This paper proposes a quasi-order for dual hesitant fuzzy sets. Abstract Dual hesitant fuzzy set (DHFS) is a useful tool that can assist the decision makers (DMs) to express their evaluation information more flexibly from two contrary points of view, which are the membership degrees and the non-membership degrees. In this paper, we mainly focus on the quasi-order for DHFSs and the professional degrees of the DMs in multi-criteria group decision-making (MCGDM) under dual hesitant fuzzy information. First, we define a quasi-order for DHFSs based on pairwise comparisons. The quasi-order could distinguish two different DHFSs more clearly and rationally. Second, we propose the professional degrees of the DMs based on dual hesitant fuzzy information and use the professional degrees to determine the weights of the DMs. The professional degrees could indicate the weights of the DMs from different viewpoints in MCGDM more reliably. In the process of decision-making, the professional degree of each DM to the decision-making problem could influence the final decision directly. On the one hand, the evaluation information has a close-knit relationship with the professional degree of each DM. On the other hand, the professional degrees of the DMs should be a very important aspect to determine the weights of the DMs directly. In addition, we combine the quasi-order and the professional degrees in MCGDM. Finally, we use a practical numerical example on the rural land conversion to illustrate the availability and practicability of the proposed MCGDM model. [ABSTRACT FROM AUTHOR]

Websites are one of the most widely distributed information resources. Educational institutions use this resource to ensure that the best quality of information transmission is achieved. As such, academic sites have become a very important aspect of academic institutions, one that affects their overall quality. Bearing in mind the importance of university websites’ quality, the authors of this paper presented a multi-criteria model for evaluating the quality of university websites. This paper presents the hybrid IR-AHP-MABAC (Interval Rough Analytic Hierarchy Process - MultiAttributive Border Approximation Area Comparison) model. The model is adapted to group decision making as based on the application of a new approach to treating uncertainties through the use of interval rough numbers (IRN). The modified IR-AHP method was used to determine the weight coefficients of the criteria in the group decision-making process. The results of the IR-AHP model are compared with results provided by the traditional AHP method and the fuzzy AHP approach. The IR-MABAC model was used for the evaluation of university websites. In order to verify the results of the IRN based approach, the IR-MABAC model was compared to the F-TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution), F-VIKOR (Fuzzy MultiCriterion Optimization and Compromise Solution), F-COPRAS (Fuzzy COmpressed PRoportional ASsessment), F-MAIRCA (Fuzzy MultiAtributive Ideal-Real Comparative Analysis), and F-TODIM (an acronym of Interactive and Multi Criteria Decision Making in Portuguese) models. The credibility of the IR-AHP-MABAC model was demonstrated by comparing the results of different multi-criteria techniques and analyzing viability. The results of the IRN approach and fuzzy comparison have shown that the new approach to dealing with imprecision yields credible, reputable ranks. [ABSTRACT FROM AUTHOR]

This paper aims to develop a hybrid emergency decision-making (EDM) method by combining best–worst method (BWM), multi-attributive border approximation area comparison (MABAC) and prospect theory (PT) in trapezoidal interval type-2 fuzzy rough (TrIT2FR) environment. In this hybrid method, the decision information is represented by trapezoidal interval type-2 fuzzy rough numbers (TrIT2FRNs). Firstly, this paper defines the TrIT2FRN and analyzes its desirable properties. Then, the TrIT2FR-BWM is developed to determine criteria weights. To develop the TrIT2FR-BWM , this paper completes the following three core issues: (i) propose an effective theorem to normalize the TrIT2FR weights; (ii) build a crisp programming model to transform the minmax objective of weight-determining model for the TrIT2FR-BWM ; (iii) design a consistency ratio for the TrIT2FR-BWM to check the reliability of the determined criteria weights. Afterwards, this paper extends the classical MABAC into TrIT2FR environment to calculate the border approximation area (BAA). Subsequently, the PT is used to rank the alternatives, in which the calculated BAA is selected as the reference point. Lastly, the validity of the proposed method is certificated with a real site selection case of makeshift hospitals on COVID-19. Sensitivity analysis and comparative analyses are conducted to illustrate the robustness and superiorities of the proposed method. Some valuable results are summarized as follows: (i) the best alternative determined by the proposed method conforms with the actual selection result, (ii) the proposed models in the TrIT2FR-BWM have strong robustness, (iii) PT is helpful to improve the decision quality of EDM. • Define the TrIT2FRN and analyzes its desirable properties. • The TrIT2FR-BWM is developed to determine criteria weights. • Extend the classical MABAC into TrIT2FR environment. [ABSTRACT FROM AUTHOR]

Ecological compensation is an important means to solve the problem of river basin water pollution, which has been widely used at home and abroad. However, the study of ecological compensation problems from the perspective of the management by using the graph model for conflict resolution (GMCR) is still a challenge. Thus, the aim of this article is to construct the graph model with intuitionistic preferences to model and analyze real life ecological compensation conflicts. Specifically, due to the lack of information and the inherent fuzziness of human cognition in ecological compensation conflicts, the intuitionistic preference structure is incorporated into the framework of the GMCR to represent the decision makers' (DMs') uncertain preference. Based on this, two sets of intuitionistic stability definitions are defined to analyze and solve the possible resolution for conflict problems with two DMs and multiple DMs, respectively. Note that a state is called intuitionistic stability for DM in a graph model based on the given intuitionistic stability definitions if and only if there is no reward for a given state to leave the initial state, that is to say, the evaluated value of the focus DM is less than an intuitionistic satisficing threshold of the same DM. Furthermore, the intuitionistic equilibrium is considered as the possible solution to the conflict problems and is intuitionistic stable for all DMs in the graph model. Finally, the ecological compensation conflicts of the Taihu Lake basin are used to demonstrate the usefulness and correctness of the graph model with intuitionistic preferences. • The intuitionistic preference is incorporated into the graph model to describe the uncertain preference of DM. • The proposed method is useful to model and analyze the ecological compensation conflicts of the Taihu Lake basin. • The paper provides a new research perspective for the graph model with uncertainty and its application. • The paper reveals the demands of DMs in ecological compensation conflicts and provides valuable decision making information. • The proposed method lays the foundations for developing detailed computational implementation algorithms of graph model. [ABSTRACT FROM AUTHOR]

In this paper, we propose new aggregation operators for multi-criteria decision making under linguistic settings. The proposed operators are based on two sets of criteria weights. Besides the primary conventional criteria weights, we introduce a method to deduce secondary criteria weights from the criteria evaluations, which reflect the role of the different criteria in discriminating among the alternatives. The properties of the proposed operators are investigated. An approach for the application of the said operators in a group multi-criteria decision making problem is presented. Following the same, the proposed operators are applied in a case study on supplier selection. The empirical validation of the proposed operators is performed on a set of 12 real datasets. Note : All usages of he, him, his in the paper, also refer to she, and her. [ABSTRACT FROM AUTHOR]

Among many decision problems, interval-valued multiplicative preference relation (IMPR) is widely utilized due to its ability to express uncertain information. A new approach to solve group decision making (GDM) with IMPRs is proposed in this paper, named stochastic group preference acceptability analysis with TODIM (SGPAA-TODIM) method, by combining TODIM method (an acronym in Portuguese of Interactive and multi-criteria Decision Making) with stochastic multi-criteria acceptability analysis (SMAA-2). It effectively circumvents information loss and considers the weight and risk preferences of experts. Firstly, the stochastic multiplicative preference relation is defined through stochastic simulation employing a certain density function, and its priority weight vector is determined using the logarithmic least squares method (LLSM). Then, the priority preference comprehensive matrix is proposed by extracting information of priority vectors. Moreover, the novel SGPAA-TODIM method is developed to analyze the stochastic parameter spaces, with the optimal rank determined based on the analysis of acceptability degrees associated with dominance rank. Finally, to demonstrate the validity and applicability of the proposed method, numerical examples are given. • The stochastic MPR is proposed through stochastic simulation. • The priority preference comprehensive matrix is defined for aggregating the priority vectors. • A novel method to solve group decision making problem. • Three examples are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The problem-solving task of the multi-criteria decision-making (MCDM) approach involves decision makers' (DMs') interaction by incorporating their preferences to arrive at one or more preferred near Pareto-optimal solution(s). The Pareto-Race is an interactive MCDM approach that relies on finding preferred solutions as the DM navigates on the Pareto surface by selecting the preferred reference direction and uniform/varying speed using the concept of Achievement Scalarization Function (ASF) or its augmented version (AASF). Selecting a reference direction or speed is possible, algorithmically, but the effectiveness of the Pareto-Race concept relies on suitable visualization methods that present the series of past solutions and convey the current objective trade-off information to assist the DM's in decision-making task. Traditional multi-dimensional Pareto-optimal front visualization methods like parallel coordinate plots, radial visualization, and heat maps are deemed insufficient for this purpose. Therefore, the paper proposes integrating the concept of the Pareto-Race MCDM approach with a recently developed interpretable self-organizing map (iSOM) based visualization method. The iSOM maps high-dimensional data into lower-dimensional space, facilitating visual convenience for the DM. The proposed method requires a finite-size representation of non-dominated solutions near the complete Pareto Front to generate iSOM plots of objectives. We also propose visualizing the metrics such as closeness to constraint boundaries, trade-off value, and robustness using iSOM to check the quality of the preferred solutions, which is rarely considered in existing MCDM approaches. iSOM plots of objective functions, closeness to constraint, trade-off, and robustness metrics assist the DM in effectively choosing new reference direction and step size in each iteration to arrive at the most preferred solution. The proposed iSOM-enabled Pareto-Race approach is demonstrated on benchmark analytical and real-world engineering examples with 3 to 10 objectives. [Display omitted] • Interactive MCDM using iSOM-aided Pareto-Race technique. • Introducing metrics such as closeness to constraint and robustness in Pareto-Race. • Visual comprehension of goodness of preferred solution and features in each iteration. • Visualization-based selection of step size and direction of search in Pareto-Race. • Demonstration on 3 to 10-objective analytical and real-world engineering examples. [ABSTRACT FROM AUTHOR]

The fuzzy decision-theoretic rough set (FDTRS) is a data processing method based on the fuzzy theory designed to handle decision problems with uncertainty and fuzziness. It simplifies the complexity of decision-making and provides decision strategies by integrating multiple sources of information. This approach makes the decision process more scientific and rational. The mainstream FDTRS model breaks the over restrictive limits of classical decision-theoretic rough sets, computes the fuzzy similarity relation between two objects utilizing the Gaussian kernel function, which is another major breakthrough and innovation in the field of decision-theoretic rough sets. However, this model suffers from the problems of insufficient decision information, single perspective and high decision-making cost when dealing with dynamic, complex and uncertain problems. Emerging in recent years, sequential three-way decisions not only provides a flexible mechanism to implement the idea of progressive computation, but also deals with dynamic, complex and uncertain problems. To this end, this paper proposes a sequential three-way decision model based on the fuzzy T-equivalence relation (FTE-based sequential three-way decision model) from the perspective of sequential three-way decisions, and gives relevant definitions and examples to effectively solve the problem of insufficient information. Then in order to solve the single-view problem, we extend the sequential three-way decisions to the case of multi-granularity, and propose FTE-based multi-granularity sequential three-way decisions in four aggregation strategies with the corresponding properties and algorithms. In addition, the rationality of models are tentatively analyzed through specific examples. Finally, six sets of UCI standard datasets are used to validate the proposed models, which demonstrate their good performance. These models contribute to the field of fuzzy decision-theoretic rough sets and sequential three-way decisions. • To enhance post-decision information, we combine the three-way decision with traditional fuzzy rough set models. • We introduce the concept of multi-granularity, using varied fusion strategies to provide a multi-perspective solution. • A more robust model is established, extensive experiments validate its effectiveness. [ABSTRACT FROM AUTHOR]

A lot of investigations have been published in the supplier selection area. However, few papers consider the problem from the perspective of risk aversion. In this paper, generalized intuitionistic fuzzy soft set (GIFSS) combined with extending gray relational analysis (GRA) method is proposed to select an appropriate supplier from the perspective of risk aversion in group decision-making environment. The proposed approach consists of two phases. In the first phase, the weights of decision makers are determined by using an extended GRA method with intuitionistic fuzzy soft set (IFSS). In traditional GRA method, the ideal reference is expressed in a vector, while, in this paper, the ideal reference for all individual decisions is expressed in a matrix, which is suitable for risk aversion. The weight of decision makers is utilized to aggregate individual opinions of decision makers. In the next phase, to eliminate the bias of decision makers in the choice of supplier and rule out the possibility of errors occurring in the evaluation of alternatives, the general manager will further validate it by utilized the GIFSS. Finally, a numerical example for supplier selection is given to illustrate application of the method, and the comparisons with other methods are also made. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationships among rough sets, soft sets and hemirings. The concept of soft rough hemirings is introduced, which is an extended notion of a rough hemiring. It is pointed out that in this paper, we first apply soft rough sets to algebraic structure-hemirings. Further, we first put forward the concepts of C -soft sets and CC -soft sets, which provide a new research idea for soft rough algebraic research. Moreover, we study roughness in hemirings with respect to MSR -approximation spaces. Some new soft rough operations over hemirings are explored. In particular, lower and upper MSR -hemirings ( k -ideal and h -ideal) are investigated. Finally, we put forth an approach for multicriteria group decision making problem based on modified soft rough sets and offer an actual example. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate that the Theorem 3.1 in a recently published paper by Garg [Appl. Soft Comput. 38 (2016) 988–999] is incorrect by a counterexample. Further, we point out the shortcomings of Garg's proposed generalized improved score function for practical decision making. [ABSTRACT FROM AUTHOR]

A failure mode and effect analysis (FMEA) procedure that incorporates a novel Perceptual Computing (Per-C)–based Risk Priority Number (RPN) model is proposed in this paper. The proposed model considers linguistic uncertainties and vagueness of words, because it is more natural to use words, instead of numerals, for an FMEA user to express his/her knowledge when he/she provides an assessment. Therefore, it is important to consider the inherited uncertainties in words used by humans for assessment as an additional risk factor in the entire FMEA reasoning process. As such, we propose to use Per-C to analyze the uncertainties in words provided by different FMEA users. There are three potential sources of risks. Firstly, the risk factors of Severity ( S ), Occurrence ( O ), and Detection ( D ) are graded using words by each FMEA user, and indicated as interval type-2 fuzzy sets (IT2FSs). Secondly, the relative importance of S , O , and D are reflected by the weights given by each FMEA user in words, which are indicated as IT2FSs. Thirdly, the expertise level of each FMEA user is reflected by words, which are expressed as IT2FSs too. The proposed Per-C-RPN model allows these three sources of risks from each FMEA user to be considered and combined in terms of IT2FSs. A case study related to edible bird nest farming in Borneo Island is reported. The results indicate the effectiveness of the proposed model. In summary, this paper contributes to a new Per-C-RPN model that utilizes imprecise assessment grades pertaining to group decision making in FMEA. [ABSTRACT FROM AUTHOR]

An economic crisis can be measured from different perspectives. A very commonly used measure is that of a country’s economic growth. When growth is lower than desired, the economy is assumed to be near stagnation or in an economic recession. This paper connects entrepreneurship and economic growth in decision-making problems assessed with modern aggregation systems. Aggregation techniques can represent information more comprehensively in uncertain and imprecise environments. This paper suggests several practical aggregation operators for this purpose, such as the ordered weighted average and the probabilistic ordered weighted averaging weighted average. Other aggregation systems based on macroeconomic theory are also introduced. The paper concludes with an application in an entrepreneurial uncertain multi-criteria multi-person decision-making problem regarding the selection of optimal markets for creating a new company. This approach is based on the use of economic growth as the fundamental variable for determining the preferred solution. [ABSTRACT FROM AUTHOR]

In the field of Multi-Criteria Decision Analysis (MCDA), the deployment of sensitivity analysis has become a fundamental approach to testing the robustness and reliability of the results obtained. Its importance lies in its ability to gain additional insight into potential changes that affect the desirability of the decision variants being evaluated. Through different methodologies, it reveals the vulnerability of results to changes in the underlying data, thus offering valuable information to support decision making. This research paper highlights a compendium of 250 researches involving the application of sensitivity analysis in a multi-criteria context. Using a structured approach of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this study provides a comprehensive review process. The main objectives are to identify and categorize common sensitivity analysis techniques explained in the literature and to describe a selected framework that facilitates the execution of these techniques. The research demonstrates the importance of sensitivity analysis to enhance the credibility of MCDA outcomes and contributes to the field of decision support. • Extensive review on comprehensive analysis of sensitivity analysis in MCDA. • Identifies main categories of sensitivity analysis methods in MCDA. • Review on techniques reveal stability of rankings. • Review on techniques reveal changes in alternatives' preferences. • Frameworks for conducting sensitivity analysis in MCDA. [ABSTRACT FROM AUTHOR]

In decision making environments under uncertainty, assessments are frequently expressed in linguistic terms. When people express their opinions using linguistic terms, the meanings ascribed to these terms may not always align. This phenomenon is captured by the concept of a linguistic perceptual map, which draws from the established lattice of hesitant fuzzy linguistic term sets. Each individual or group of people (referred to as a 'profile') possesses their own distinct perceptual map. By projecting and aggregating the opinions of these individuals or groups onto a common perceptual map, an average opinion and a level of consensus are derived. This paper extensively studies the mathematical properties of the projection function. We prove that it is a monomorphism between lattices, preserving crucial order relations. Additionally, we progress beyond existing research by introducing an interpretation function. This function facilitates the translation of the aggregated result (referred to as the 'centroid') from the common perceptual map to each individual's perceptual map. The properties of the interpretation function are also subject to analysis, demonstrating its role in preserving previous order relations, despite not being a morphism. To illustrate the practicality of these concepts, we propose a methodology that we apply to a real-world case study involving data in the form of ratings from the Amazon books platform. The results obtained highlight that utilizing distinct perceptual maps for each user profile statistically enhances the degree of consensus compared to scenarios where perceptual maps are not differentiated. [Display omitted] • Lattice structure of the common perceptual map to aggregate decision-maker profiles. • Projection function from each DM profile to the common perceptual map. • Centroid and degree of consensus in linguistic perceptual maps for GDM. • Interpretation function to translate results to each linguistic perceptual map. • Case study based on book ratings. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy logic-based machine learning (ML) algorithm is introduced. This proposed ML algorithm accepts picture fuzzy sets (PFS) as the fuzzified input and incorporates genetic algorithm (GA) during the training process. The proposed ML algorithm is then incorporated into two well-known decision-making methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation Based on Distance from Average Solution (EDAS). These two decision-making methods and the proposed ML algorithm are then applied to solve a multi-attribute decision-making (MADM) problem related to the evaluation and ranking of public listed companies based on their stock performance, in accordance with investors' personalities. The actual daily closing stock price of five public listed companies from the big market capitalization (Big Cap) category traded in the Kuala Lumpur Stock Exchange (KLSE) for a period of 10 years is used as the datasets for this study. Monte Carlo simulation is used to verify the accuracy of the results. In addition, a comprehensive comparative study of some recent PFS-based decision-making methods in the existing literature and the proposed methods is conducted, and all the typical instances of the investors' personalities are observed. The results obtained through this comparative study corroborates the results obtained via the proposed methods, and this proves the effectiveness of the proposed methods. The differences in the results obtained via the different methods are analyzed and discussed, and this again proves that the results obtained via the proposed methods are effective and consistent with the judgments of human experts. • Two new multi-attribute fusion decision-making algorithms for stock trading are introduced. • Algorithms have customizable investor personality traits for modeling of investor behavior. • Context-specific fuzzification procedures for the fuzzification of actual data is introduced. • User-customizable fuzzification procedures are given to enumerate investors' personalities. • Proven that our proposed models are able to simulate human judgments and intuition accurately. [ABSTRACT FROM AUTHOR]

Healthcare services create medical waste that may be dangerous to healthcare personnel, patients, the general public, and the environment. Medical waste disposal method selection is among the most important decisions that must be made by healthcare organizations, and such a problem has a number of contradictory criteria and alternatives. On the other hand, decision experts may have considerable uncertainty while evaluating these alternatives. In this paper, new fuzzy multi-criteria decision-making (MCDM) methodologies are provided for assessing the medical waste disposal alternatives. The CRiteria Importance Through Intercriteria Correlation (CRITIC) is used for obtaining criterion weights in an objective manner, and the Weighted Aggregated Sum Product ASsessment (WASPAS) approach is utilized to rank the alternatives. For modeling the uncertainty in the nature of the problem, the proposed methodology is developed in single and interval-valued spherical fuzzy environments. Single-valued spherical fuzzy sets enable users to model the membership, non-membership, and hesitancy parameters independently. On the other hand, interval-valued spherical fuzzy sets provide increased fuzziness modeling capacity. The step-by-step solution of the proposed methodologies are followed by sensitivity and comparative analyses, and a discussion. This study contributes to the work of both academics and practitioners in the healthcare industry, as well as other sectors facing similar types of decision-making problems. • CRITIC-WASPAS method is extended with single and interval-valued spherical fuzzy sets. • The proposed methodology provides extensive fuzziness modeling capacity. • The CRITIC phase enables calculating criteria weights in an objective manner. • Medical waste disposal alternatives are prioritized with the WASPAS technique. • Sensitivity analyses for decision expert, criterion, and threshold parameter weights are provided. [ABSTRACT FROM AUTHOR]

This paper discusses the issues related to the process of global decision-making on the basis of knowledge which is stored in several local knowledge bases. The approach considered in this paper is very general because we do not assume any additional conditions on the sets of objects or the sets of conditional attributes of local knowledge bases. The paper proposes a new approach to the organization of the structure of multi-agent decision-making system, which operates on the basis of dispersed knowledge. In the presented system, the local knowledge bases will be combined into groups in a dynamic way. We will seek to designate groups of local bases on which the test object is classified to the decision classes in a similar manner. Then, a process of the elimination inconsistencies in the knowledge will be implemented in the created groups. Global decisions will be made by using one of the methods for analysis of conflicts. The paper includes the definition of a multi-agent decision-making system with dynamically generated clusters and a description of a global decision-making process. In addition, the paper presents the results of experiments carried out on data from the UCI repository. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a systematic method to solve knowledge management performance evaluation (KMPE) problems. This method includes an integrated evaluation process starting from the measurement to the output of KMPE and combines subjective and objective indicators together. Firstly, we established an index system, involving the process of knowledge management, the organizational knowledge structure, economic benefits and efficiency. And based on this index system, a synthetic evaluation method is presented, using triangular fuzzy number to measure indexes and facilitating the KMPE with a group support system (GSS). To know better of the proposed method, we have an example to illustrate. Finally, the empirical study conducted in this paper indicates that the evaluation method has strong practicability and operability. Besides, the evaluation is enabled by using a group support system: the more objective scoring can be achieved due to synchronic/asynchronous and anonymous participation; Decision-makers improve their efficiency by the clear demonstration analysis results. The systematic method of KMPE based on the index system is able to improve organizations’ efficiency in performance evaluation process. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of integrating new classes on the fly into on-line classification systems. The main focus is on visual inspection tasks, although the concepts proposed in this paper can easily be applied to any other on-line classification systems. We use evolving fuzzy classifiers (EFCs), which can adapt their structure and update their parameters in incrementally due to embedded on-line adaptable classifier learning engines. We consider two different model architectures — classical single model and an all-pairs approach that uses class information to decompose the classification problem into several smaller sub-problems. The latter technique is essential for establishing new classes quickly and efficiently in the classifier, and for reducing class imbalance. Methodological novelties are (i) making appropriate structural changes in the EFC whenever a new class appears while operating in a single-pass incremental manner and (ii) estimating the expected change in classifier accuracy on the older classes. The estimation is based on an analysis of the impact of new classes on the established decision boundaries. This is important for operators, who are already familiar with an established classifier, the accuracy of which is known. The new concepts are evaluated in a real-world visual inspection scenario, where the main task is to classify event types which may occur dynamically on micro-fluidic chips and may reduce their quality. The results show stable performance of established classifiers and efficient (low number of samples requested) as well as fast integration (steeply rising accuracy curves) of new event types (classes). [ABSTRACT FROM AUTHOR]

Consensus decision-making is fuzzy by nature, yet fuzzy consensus decision-making in a medium to large number of decisions is not widely used since it demands additional information that requires extra decision-maker effort. Consensus decision-making rests on properly measured agreement. This paper proposes a fuzzy measure of agreement through fuzzy kappa based on fuzzy partitions. These fuzzy partitions enable decision-makers to assess their decisions with a degree of confidence. A fuzzy partition is built for each decision-maker considering his/her confidence degrees when categorising a set of alternatives or solutions. This enables decision-makers to more easily capture the fuzzy nature of the decision. In addition, this paper presents a real-life experiment based on a innovation contest to show the feasibility of using confidence degrees in real-life applications compared to traditional consensus decision-making. The results suggest that the use of confidence degrees improves the level of agreement in the consensus decision-making process through fuzzy kappa coefficients, and it also improves the level of agreement in the consensus decision-making process. [ABSTRACT FROM AUTHOR]

This paper firstly introduces the idea of interval-valued hesitant Fermatean fuzzy set (IVHFFS). It is one of the significant theoretical and practical features, which derive from the integration of hesitant Fermatean fuzzy set (HFFSs) and interval number. In this study, we firstly present the basic concept, various operations and distance measures of IVHFFSs. Then, based on the operations of IVHFFSs, some aggregation operators (AOs) are proposed for aggregating the interval-valued hesitant Fermatean fuzzy information. Furthermore, some elegant properties of these operators are discussed in detail. To show the applicability, we discuss a decision analysis process on IVHFFSs environment with the help of conventional complex proportional assessment (COPRAS) methodology. Finally, the selection process of desalination technology for treating the feed water is also taken to validate the developed decision analysis procedure on IVHFFSs. This paper considers various criteria and sub-criteria in the assessment procedure and determines the most significant criteria influencing the desalination technology selection process are technical, social, environment and economic criteria, with significance weights of 0.3972, 0.289, 0.1653 and 0.1486, respectively. The work concludes that the reverse osmosis (RO) is the suitable desalination technology under the considered criteria followed by electrodialysis (ED), and multiple-effect distillation (MED). Moreover, the sensitivity investigation and comparative study are presented to verify the robustness and stability of the proposed method. The findings of this study show that the proposed model can suggest more feasible performance while facing several input uncertainties and influencing factors. • Advanced the idea of HFFSs to IVHFFSs and proposes various theoretic concepts. • Presented the normalization procedure of IVHFFSs. • Gave the interval-valued hesitant Fermatean fuzzy weighted aggregation operators. • Extended the classical COPRAS method under IVHFFS environment. • Demonstrated the practicality and the managing capacity. [ABSTRACT FROM AUTHOR]

With the increasing product variety, companies aim to become better than their competitors by providing a superior product developed with a customer-oriented product design approach and a quality strategy. In order to achieve this, companies should well understand customer expectations and quickly be able to convert these expectations to technical characteristics. Since the expectations consist of mostly subjective judgments, this evaluation process contains vagueness and impreciseness. A triplet represents the uncertainty in subjective judgments: the degrees of belongingness or Truthiness (T), non-belongingness or Falsity (F), and indeterminacy (I). For this reason, in this paper, a neutrosophic Quality Function Deployment (QFD) methodology based on neutrosophic AHP and neutrosophic DEMATEL is developed and applied to the design of a car seat. In this methodology, the weighting of customer requirements is performed by neutrosophic AHP. The relationships among the technical characteristics are determined by neutrosophic DEMATEL for the customer-oriented product design, considering both impreciseness in the data and indeterminacy of the decision-makers. In other words, the contribution of this paper is that the proposed methodology provides better integration of the voice of customers into technical characteristics through a practical fuzzy multi-criteria decision analysis. Based on the results, it is revealed that seat height is the most important technical characteristic, followed by vertical travel range and horizontal travel range. Moreover, validity and verification of the proposed methodology have been tested with other methods presented in the literature. Sensitivity analyses have been carried out to show the flexibility of the given decisions under different cases. Lastly, possible implications on theoretical and managerial aspects have been discussed. • A customer-oriented product design approach is developed and applied for car seats. • A new methodology based on QFD, DEMATEL, AHP and neutrosophic sets is proposed. • The proposed approach can handle uncertainty in customer expectations. [ABSTRACT FROM AUTHOR]

This paper presents fully fuzzy fixed charge multi-item solid transportation problems (FFFCMISTPs), in which direct costs, fixed charges, supplies, demands, conveyance capacities and transported quantities (decision variables) are fuzzy in nature. Objective is to minimize the total fuzzy cost under fuzzy decision variables. In this paper, some approaches are proposed to find the fully fuzzy transported amounts for a fuzzy solid transportation problem (FSTP). Proposed approaches are applicable for both balanced and unbalanced FFFCMISTPs. Another fuzzy fixed charge multi-item solid transportation problem (FFCMISTP) in which transported amounts (decision variables) are not fuzzy is also presented and solved by some other techniques. The models are illustrated with numerical examples and nature of the solutions is discussed. [ABSTRACT FROM AUTHOR]

Research on decision making in an imprecise environment has gained and will continue to gain overriding importance in recent years. In this context, decision making on the location of hazardous plant installations is an issue of relevance. The authors believe that there is a need of a standard model to overcome the discrepancies in Environmental Impact Assessment (EIA) for rationalization of site selection. An inappropriate choice of a site for an upcoming hazardous industrial plant may lead to further degradation of existing environment thus resulting in hazardous effects on mankind as well as all other living organisms. It could be argued that there exists quantifiable (measurable parameters) and epistemic (non measurable parameters) uncertainties in the scoping phase and the overall EIA process. The uncertainties could be modeled using soft computing techniques in ranking the sites for upcoming hazardous industry and justify the results in linguistic terms. The case study in the paper relates to the application of Back propagation Artificial Neural Network (BP-ANN), Learning Vector Quantization (LVQ-ANN), Ant Colony optimization with Fuzzy Soft Sets (FSS) and Fuzzy Indexing (FI), in ranking the existing/upcoming power plant locations in India. Comparative evaluation of these methods is also an integral part of the paper. [ABSTRACT FROM AUTHOR]

Most real-world problems naturally involve multiple conflicting objectives, such as in the case of attempting to maximize both efficiency and safety of a working environment. The aim of multi-objective optimization algorithms is to find those solutions that optimize several components of a vector of objective functions simultaneously. However, when objectives conflict with each other, the multi-objective problem does not have a single optimal solution for all objectives simultaneously. Instead, algorithms attempt to search for the set of efficient solutions, known as the global non-dominated set, that provides solutions that optimally trade-off the objectives. The final solution to be adopted from this set would depend on the preferences of the decision-makers involved in the process. Hence, a decision-maker is typically interested in knowing as many potential solutions as possible. In this paper, we propose an extension to a previous piece of work on multi-objective cooperative coevolution algorithms (MOCCA). The idea was motivated with a practical problem in air traffic management to design terminal airspaces. MOCCA and a further study that was done on a distributed environment for MOCCA, were found to fit the need for the application. We systematically questioned key components of this algorithm and investigated variations to identify a better design. This paper summarizes this systematic investigation and present the resultant new algorithm: multi-objective co-operative co-evolutionary algorithm II (MOCCA-II). [ABSTRACT FROM AUTHOR]

To remain competitive and to provide timely and accurate services, the decision maker or system analyst view the reliability and maintainability issues as a part of corporate quest to improve quality of the products/processes and services delivered. But the collected or available data available from the historical records are mostly uncertain, limited and imprecise in nature. Thus in such situation(s), it is difficult, if not impossible, to analyze the behavior and performance of the system up to desired degree of accuracy. Thus attempt has been made by the author for analyzing the behavior and performance of the system using Vague Lambda–Tau methodology. Intuitionistic fuzzy set theory has been used for representing the classical(crisp) data into triangular fuzzy numbers because intuitionistic sets are characterized by a truth membership function and false membership functions (non-membership functions) so that sum of both values is less than 1. Sensitivity analysis has also been performed and the effects on system mean time between failures are addressed. The performance analysis of the system has been investigated through a composite measure of reliability, availability and maintainability (RAM) called RAM-Index which explore the effect of failure/repair rates on system performance. Based on their performance, the most critical component of the system has been investigated. The methodology improves the shortcomings of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The pulping unit of a paper mill situated in a northern part of India, producing approximately 200 ton of paper per day, has been considered to demonstrate the proposed approach. The results obtained by proposed approach are compared with the existing fuzzy Lambda–Tau and the crisp methodologies. [ABSTRACT FROM AUTHOR]

Blockchain technology (BT), which provides to record transactions among many computers as a distributed ledger, is commonly utilized by companies to develop and manage business processes. Since it is a multi-process and multi-agent complex system, the implementation process of BT contains many risk factors. Identifying and analyzing these risks in order to minimize their adverse effects during the implementation process is also completely critical for companies. Since evaluating these risks must be handled by considering many different perspectives, the evaluation of risk factors for BT can be considered as a multi-criteria decision-making (MCDM) problem. To cope with uncertainty in this process, the fuzzy set theory (FST) can be effectively utilized with MCDM methods to increase their ability to obtain more concrete and realistic results. Therefore, this paper proposes an MCDM methodology consisting of Decision-Making Trail and Evaluating Laboratory (DEMATEL) and cognitive mapping methods based on FST. These methods have been reconstructed with hesitant fuzzy Z-numbers that enable not only to state impreciseness of the assessments but also to consider indeterminacy and hesitancy of the experts to evaluate risk factors with respect to BT implementation. While the dependencies of risk factors have been determined via the hesitant fuzzy Z-DEMATEL method, their weights have been obtained through the hesitant fuzzy Z-cognitive mapping method. As a result, this study aims to prioritize risk factors related to the BT implementation process with a novel fuzzy decision-making methodology and provide a road map for companies. With this study, which is the first paper in the literature in terms of the subject of the study and the method adopted, an integrated, comprehensive fuzzy approach is introduced for the first time in evaluating BT risks. As a result of detailed research, risk factors that companies may encounter in BT use and the degrees of their importance have been determined. Through that, essential decision analysis has been put forward that companies can apply for their strategic decisions. A sensitivity analysis based on changes in the initial vector values has also been adapted to analyze the effects of the risk factors. [Display omitted] • Evaluation of risk factors for BT can be considered as an MCDM problem. • Hesitant fuzzy Z-numbers enable to represent both impreciseness of the assessments and hesitancy of the experts. • An integrated MCDM methodology extended with hesitant fuzzy z-number concept is introduced. • Need of Skilled Labor criterion is determined as the most influential risk factor based on the handled context. • A sensitivity analysis is also conducted to check the flexibility of the applied methodology's decisions. [ABSTRACT FROM AUTHOR]

Clustering and consensus reaching are two crucial processes for solving social network large-scale decision-making (SN-LSDM) problems. This paper explores the moderating effect of trust on the decision-making process, which is decomposed into examining the influence of trust constraint on clustering and the influence of trust loss on consensus reaching. We hold that low-trust experts are not suitable to be assigned to the same subgroup, even though their preferences are sufficiently similar. We propose a punishment-driven consensus reaching model for SN-LSDM problems. The model contains two main stages. In Stage 1, a trust-constrained K-means clustering algorithm is presented, which can overcome the defect of grouping low-trust experts caused by traditional clustering methods based on preference similarity. Then, a weight-determining method combining the size, cohesion, and overall trust degree of a subgroup is proposed. In Stage 2, a punishment-driven consensus reaching process is designed, which identifies four categories of consensus scenarios (high–high, high–low, low–high, low–low) through consensus measures and trust measures. Different adjustment strategies are established, and the moderating effect of trust loss on consensus reaching and consensus cost is investigated. Finally, both a case study on social capital selection in public–private partnership projects and a comparative analysis are implemented to reveal the feasibility and advantages of the clustering algorithm and consensus reaching model proposed in this paper. • Trust relationship and preference similarity are important in SN-LSDM scenarios. • The moderating effect of trust on clustering and consensus reaching is explored. • A TCop-Kmeans algorithm is designed that considers trust constraints. • A weight-determining method for clusters is developed that combines three indices. • A punishment-driven consensus reaching model for SN-LSDM problems is proposed. [ABSTRACT FROM AUTHOR]

This paper proposes MULTIMOORA-IFN2 technique for multi-criteria decision making MCDM). The proposed approach involves information fusion which allows considering information expressed in both crisp and fuzzy variables. What is more, we introduce the aggregation of the different parts of MULTIMOORA which makes the technique more operational, especially in case of large-scale applications. The empirical example considers the case of energy storage technology selection. The sensitivity of the results obtained by applying MUTIMOORA-IFN2 is checked in two ways. The weighting is adjusted to ascertain whether the changes in the importance of the criteria impact the ranks of the energy storage technologies. Further on, the results obtained by applying MULTIMOORA-IFN2 are compared to those obtained by employing TOPSIS and VIKOR methods. • The MULTIMOORA approach for intuitionistic fuzzy sets is revised. • Intuitionistic fuzzy sets are applied for information fusion. • Energy storage technologies are assessed. • Technical, economic, and environmental criteria are considered. • Different scenarios are assumed for robustness analysis. [ABSTRACT FROM AUTHOR]

The technology deployment in smart e-tourism brings high potential in terms of customer data, events, reservations, and others. It acts as an effective and personalized guide to aid travelers. There is an increasing variety of smart e-tourism apps with multiple categories and criteria, but in terms of decision making, this presents a multicriteria complex problem to determine the best app from a group of available options with high criteria subjectivity. Literature reviews have evaluated and modeled the existing smart e-tourism apps alternatives, but informational uncertainty remains. The fuzzy sets and Multi-Attribute Decision Analysis (MADA) were used to handle the subjectivity issue. However, this process includes levels of uncertainty, which affects the decisions made and still an open issues. Spherical fuzzy rough sets (SFRSs) environment are useful in this situation for resolving fuzziness and ambiguity. This paper proposed a decision modeling approach for smart E-Tourism data management applications based on SFRSs environment. For methodology: firstly, a decision matrix is adopted for 5 different categories of Smart E-tourism's system applications on the basis of the integrated 12 evaluation criteria. Secondly, a new formulation and development formulating a new extension of FWZIC, called a Spherical Fuzzy Rough-Weighted Zero-Inconsistency (SFR-WZIC), for weighting the smart key concept attributes involved in modeling smart e-tourism, whereas a new formulation and development for a new extension of FDOSM, called a Spherical Fuzzy Rough Decision by Opinion Score Method (SFR-DOSM), for modeling the applications of smart e-tourism per each e-tourism category; then, the new developments are integrated. The proposed methods were evaluated using systematic ranking and sensitivity analysis. • A homogeneous SFRS-MCDM framework for benchmarking smart e-tourism applications is developed. • A new formulation of FWZIC and FDOSM based on SFRS are presented. • Systematic ranking and sensitivity analysis assessments were presented to evaluate the robustness of the proposed work. [ABSTRACT FROM AUTHOR]

In recent years, an increasing number of researchers have focused on how to leverage limited information to make more reasonable decisions, thereby reducing the decision-making risk for decision-makers. Three-way decision, as a method for risky decision-making, offers a new approach to address this challenge and has gained significant attention. In view of this, based on the utility theory, this paper proposes a novel three-way utility decision model oriented to attribute fuzzy concept in multi-attribute environments. Firstly, an attribute fuzzy concept is constructed based on the decision-maker's minimum requirements on each attribute. Subsequently, a new method for calculating the relative utility function is proposed, which is used to calculate the corresponding decision thresholds. Additionally, a generalized conditional probability calculation method is presented based on the attribute fuzzy concept and the basic semantics of conditional probability. Finally, a new three-way decision model is established to obtain the classification and ranking results of all objects. Through application analysis, comparative study and experimental analysis, the proposed method's rationality and effectiveness are verified. • A novel calculation method for relative utility function is proposed. • A novel calculation method for conditional probability is proposed. • A three-way utility decision model oriented to attribute fuzzy concept is built. [ABSTRACT FROM AUTHOR]

Prioritising testing activities is essential for new product development to identify the most valuable tests and distinguish the critical ones from the trivial ones. Most research solves this prioritisation problem based on the high risks identified in Failure Mode and Effect Analysis (FMEA). However, an empirical case study on a UK-based engine manufacturer highlights that the aim of testing is not only to detect technical failures but also to validate customer requirements. Based on the findings identified from the empirical study, this paper proposes a systematic approach for testing activity prioritisation, where fuzzy relational decision matrices are established to link customer requirements, technical objectives and testing activities. An XOR Best–Worst Method (BWM) and an extended simple additive weighting (SAW) method are developed to calculate the weights of customer requirements and score the testing activities. The approach also handles two uncertainty scenarios during the prioritisation process, i.e. the subjective judgements of choosing from options and the linguistic vagueness of the decisions. The application in a case from the engine manufacturing company illustrates the methodological improvement in the prioritisation process. The proposed method ranks the testing activities, which suggests an order of effort distribution for the test plan. • Challenges of prioritisation identified in an empirical case study. • A systematic fuzzy hybrid approach to testing prioritisation in new product development. • Both customer requirements and technical risk are critical factors in prioritisation. • The method is applied to an example case from the engine company. • The results show methodological improvement in the prioritisation process. [ABSTRACT FROM AUTHOR]

In resolving multicriteria decision-making (MCDM) problems, it is observed that the solution process is frequently affected by the available vague information together with intra-personal uncertainties associated with expert's opinion. In most of the time fuzzy set or its existing variants fail to capture such uncertainties for making reasonable balance in decision making processes. To reduce the effects of uncertainties, this paper introduces a new variant of fuzzy set, viz., type-2 Pythagorean fuzzy set (T2PFS). The main advantage of using T2PFS in MCDM is that the membership and non-membership values of each argument are presented by Pythagorean fuzzy numbers rather than using fuzzy number or intuitionistic fuzzy number. In model formulation process, at first, T2PFS is explicitly formulated and some basic operations are defined. Subsequently, four aggregation operators are constructed to aggregate the arguments, and their properties are verified. Afterwards, two information measures and a score function are defined. Moreover, two MCDM algorithms based on the developed aggregation operators and TOPSIS are framed. To show the application potentiality of the proposed method, a case study relating to the selection of sustainable transport system in India is considered and solved. In the case study five different fuel-based vehicles, mostly used in India in recent times, are evaluated by the experts as transport alternatives with the consideration of thirteen sustainability related criteria. From the achieved result it is found that in future electric cars would become the most sustainable vehicle in India. Sensitivity analysis of the developed models and comparative study with existing fuzzy and non-fuzzy MCDM methods establish effectiveness of the developed models. This study may facilitate transport authority to select appropriate transport system in connection with sustainable development of a region. • As a new variant of fuzzy set, type-2 PFS is coined for resolving uncertainties associated with MCDM problems. • Terminologies, ranking of type-2 PFSs with aggregation operators are developed by defining arithmetic operations. • Defining distance and entropy measures, type-2 PF entropy weight model is introduced to determine criteria weights. • Two type-2 PF MCDM algorithms based on aggregation operators and TOPSIS are presented. • A case study is performed for the selection of sustainable transport system in India. [ABSTRACT FROM AUTHOR]

It is generally assumed that the rankings provided by Multi-Attribute Decision-Making (MADM) techniques are definitive. Once the ranking is delivered, decision makers (DMs) are expected to choose the first alternative and dismiss the remaining ones, concluding the application of the corresponding model. The MADM literature has incorporated fuzziness and imprecision to its models to deal with evaluation uncertainties but has not accounted for its consequences defined in terms of regrettable choices. That is, MADM models do not consider the possible consequences of having chosen an alternative whose actual characteristics do not correspond to those expected by the DM. This paper aims at designing an integrated MADM framework with interval variables where the DM is allowed to modify the initial alternative chosen after observing the realizations of its characteristics. In order to do so, sequences of alternatives including the initial choice as well as subsequent alternate choices should be ranked in place of single alternatives. We analyze the combinatorial decision environment that arises from defining and evaluating sequences of choices by accounting for the whole set of potential realizations and any subsequent change in the alternatives selected. The TOPSIS method is used to design the integrate evaluation framework producing the final ranking. A case study analyzing the entry decision of a firm within a group of European countries based on their levels of ICT development is presented. We illustrate how the countries selected and their order may differ substantially when accounting for the complementarities existing among them. Moreover, the selection process and any subsequent decision vary with the number of modifications considered relative to the initial country selected. The results obtained are of interest not only to firms facing a similar problem, but also to DMs or managers dealing with strategic selection processes where the wrong choice of alternatives may lead to increasingly complex sequential disruptions. • We analyze a Multi-Attribute Decision-Making setting with interval evaluations. • We formalize the regrettable choices inherent to the uncertain evaluations received. • The resulting choice sequences account for combinations of all potential realizations. • Sequences are determined by the number of modifications in the alternatives selected. • We study the entry decision of firms based on the ICT development level of countries. [ABSTRACT FROM AUTHOR]

In practice, experts sometimes compare alternatives from different perspectives by rankings to express their preferences. However, it is common that the comparing information they give is incomplete, and experts' preferences are changing during the decision-making process. In this situation, it is not easy to depict experts' preferences and making appropriate decisions. Hence, to handle the problems, this paper proposes a multi-attribute group decision-making (MAGDM) method for incomplete linear ordinal ranking (ILOR) information combined with the decision field theory (DFT) from the perspective of process-oriented decision-making. Firstly, the extended preference map and information energy for ILOR are improved. Based on those, the concept of probabilistic utility set (PUS) and some basic operations are proposed to enhance the computability of ILOR, which can convert incomplete ILORs to PUS and depict the experts' preferences effectively. Then, the framework and the detailed steps of the DFT-combined MAGDM method are presented, in which the psychological difference for PUS is established. The method helps depict the distance felt by experts and the variability of the decision-making process. Finally, the illustrations are conducted to show the usage and features of the proposed method. The illustration shows the good interpretability and accuracy of the method. • Proposing a new way to depict incomplete linear ordinal ranking. • Extending the psychological distance for probabilistic utility set. • Combining with decision field theory for multi-attribute group decision making with incomplete linear ordinal ranking. [ABSTRACT FROM AUTHOR]

Abstract: The key issue for an efficient and reliable multi-objective evolutionary algorithm (MOEA) is the ability to converge to the True Pareto Front with the least number of objective function evaluations, while covering it as much as possible. To this purpose, in a previous paper performance comparisons showed that the Genetic Diversity Evolutionary Algorithm (GeDEA) was at the same level of the best state-of-the-art MOEAs due to it intrinsic ability to properly conjugate exploitation of current non-dominated solutions and the exploration of the search space. In this paper, an improved version, namely the GeDEA-II, is proposed which features a novel crossover operator, the Simplex-Crossover (SPX), and a novel mutation operator, the Shrink-Mutation. Genetic Diversity Evaluation Method (GeDEM) operator was left unchanged and completed using the non-dominated-sorting based on crowding distance. The performance of the GeDEA-II was tested against other different state-of-the-art MOEAs, following a well-established procedure already used in other previous works. When compared to the original proposed test problems, the number of decision variables was increased and the number of generations left to the algorithms was intentionally reduced in order to test the convergence performance of the MOEAs. GeDEA-II and competitors were executed 30 times on each proposed test problem. The final approximation set reached by each algorithm was represented in the objective function space, and the performance, measured in terms of hypervolume indicator, reported in dedicated box plots. Finally, authors aimed at putting in evidence the excellent performance of GeDEA-II on the same test problems, by increasing the decision variables up to 100 times the original proposed number. Results clearly indicates that the performance of GeDEA-II is, at least in these cases, superior. [Copyright &y& Elsevier]

Abstract: In this paper, a new approach based on Differential Evolution (DE) for the automatic classification of items in medical databases is proposed. Based on it, a tool called DEREx is presented, which automatically extracts explicit knowledge from the database under the form of IF-THEN rules containing AND-connected clauses on the database variables. Each DE individual codes for a set of rules. For each class more than one rule can be contained in the individual, and these rules can be seen as logically connected in OR. Furthermore, all the classifying rules for all the classes are found all at once in one step. DEREx is thought as a useful support to decision making whenever explanations on why an item is assigned to a given class should be provided, as it is the case for diagnosis in the medical domain. The major contribution of this paper is that DEREx is the first classification tool in literature that is based on DE and automatically extracts sets of IF-THEN rules without the intervention of any other mechanism. In fact, all other classification tools based on DE existing in literature either simply find centroids for the classes rather than extracting rules, or are hybrid systems in which DE simply optimizes some parameters whereas the classification capabilities are provided by other mechanisms. For the experiments eight databases from the medical domain have been considered. First, among ten classical DE variants, the most effective of them in terms of highest classification accuracy in a ten-fold cross-validation has been found. Secondly, the tool has been compared over the same eight databases against a set of fifteen classifiers widely used in literature. The results have proven the effectiveness of the proposed approach, since DEREx turns out to be the best performing tool in terms of highest classification accuracy. Also statistical analysis has confirmed that DEREx is the best classifier. When compared to the other rule-based classification tools here used, DEREx needs the lowest average number of rules to face a problem, and the average number of clauses per rule is not very high. In conclusion, the tool here presented is preferable to the other classifiers because it shows good classification accuracy, automatically extracts knowledge, and provides users with it under an easily comprehensible form. [Copyright &y& Elsevier]

Abstract: This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks. [Copyright &y& Elsevier]

Abstract: The aims of multi-objective optimization are (1) to find pareto-optimal solutions and (2) to analyze the trade-off between conflicting objectives. This paper proposes an interactive method for solving multi-objective optimization problems using the satisficing trade-off method (STOM). In particular, we introduce a trade-off matrix to quantitatively analyze the trade-off between conflicting objectives. Each element of the trade-off matrix consists of a projection matrix of active constraints and gradients of objective functions. In addition, the compromise point and the compromise solution incorporating the trade-off ratio that the decision-maker desires are defined in this paper. The technique to obtain the compromise point is proposed in this paper. Through numerical examples, the validity proposed method is examined. [Copyright &y& Elsevier]