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This paper presents a literature review on models for assessing corporate sustainability. The review encompasses 68 papers that were published in indexed peer-reviewed journals from 1990 to 2019, which were available on Web of ScienceTM in May 2020. The goal of this study is to evaluate how corporate sustainability is being evaluated in different sectors. We concluded that there is still no consensus among researchers from different areas regarding what corporate sustainability means and different indicators have been used to assess it. The biggest problem is that in various cases the sustainability tripod is not considered. Our study recommends a set of relevant indicators to be adopted for assessing corporate sustainability considering a four-dimensional perspective. The recommendation is based on the number of times these indicators were applied in the reviewed models, and it can be useful for different types of organisations and/or sectors. For future work, we suggest improving this review to include other databases and consequently other relevant publications, to analyse the operators used to aggregate the uni-dimensional information, and then to propose a general four-dimensional index for corporate sustainability assessment, based on a non-compensatory aggregation operator, which can be applied to evaluate different types of companies. [ABSTRACT FROM AUTHOR]

The selection and evaluation of solid rocket multi-stage combined schemes with multiple attributes are main issues needed to be resolve. This paper innovatively applied the power generalised compensative weighted averaging (PGCWA) aggregation operator to help multi-stage combined solid rocket select optimal compositional scheme among multiple schemes under multi-attribute environment. The attribute values of each scheme of solid rocket are aggregated to evaluate the scheme, where the weight of attributes is incorporated into the aggregation procedures. Numerical example verifies the proposal method exhibits sound performances in decision support of the aerospace field, the effectiveness and feasibility are further illustrated. [ABSTRACT FROM AUTHOR]

The paper offers an extension and generalization of the definitions of the weighted arithmetic mean WAM, the ordered weighted averaging function OWA and the discrete Choquet integral using not only positive but also negative weights. Negative weights are important in various contexts, including statistics and robust aggregation, and they directly affect the monotonicity of the mentioned functions. The paper offers insights into the extended WAM, OWA and Choquet integral-based averaging functions with respect to their directional monotonicity, and establish the systems of linear inequalities which define the cone of monotonicity of these functions. [ABSTRACT FROM AUTHOR]

Fuzzy game theory has been applied in many decision-making problems. The matrix game with interval-valued intuitionistic fuzzy numbers (IVIFNs) is investigated based on Archimedean t-conorm and t-norm. The existing matrix games with IVIFNs are all based on Algebraic t-conorm and t-norm, which are special cases of Archimedean t-conorm and t-norm. In this paper, the intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm are employed to aggregate the payoffs of players. To derive the solution of the matrix game with IVIFNs, several mathematical programming models are developed based on Archimedean t-conorm and t-norm. The proposed models can be transformed into a pair of primal–dual linear programming models, based on which, the solution of the matrix game with IVIFNs is obtained. It is proved that the theorems being valid in the exiting matrix game with IVIFNs are still true when the general aggregation operator is used in the proposed matrix game with IVIFNs. The proposed method is an extension of the existing ones and can provide more choices for players. An example is given to illustrate the validity and the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

The probabilistic linguistic term set (PLTS) is a useful tool and has been widely applied to deal with uncertain information for decision-making problems. It allows the experts to evaluate the alternatives by linguistic terms with corresponding probability information. In practice, the experts are difficult to provide complete probability information because of the complexity of the decision-making environment and limited cognition of the experts. In such cases, we need to normalize the probability information. To avoid information loss in the normalization process of PLTSs, this paper proposes the concept called limited interval-valued probabilistic linguistic term sets ( l -IVPLTSs) by introducing the membership degree. First, we present the concept of l -IVPLTSs, and provide the basic operation laws and aggregation operators for l -IVPLTSs. Then, the membership degree is determined by the deviation degree based on a programming model. Furthermore, the extended possibility degree and the PROMETHEE II method under the limited interval-valued probabilistic linguistic environment are given, based on which, the whole multi-criteria group decision making (MCGDM) process with l -IVPLTSs is presented. Finally, the proposed method is applied to a case about evaluation of airline service quality. Discussions and analyses about the results are further conducted to verify the rationality of our approach. [ABSTRACT FROM AUTHOR]

As the generalisation of intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy set (PFS), the q-rung orthopair fuzzy set (q-ROFS) is more useful to express fuzzy and ambiguous information. Meanwhile, to consider human's hesitance, the concept of q-rung orthopair hesitant fuzzy set (q-ROHFS) is presented, which can be more valid for handling real multiple attribute decision-making (MADM) problems. To fuse the information in q-ROHFS more effectively, in this article, based on power average (PA) operator and generalised Heronian mean (GHM) operator, some q-rung orthopair hesitant fuzzy power generalised Heronian mean (q-ROHFPGHM) operators which can consider the relationships between being fused arguments are defined and studied. Evidently, the new proposed operators can obtain more exact results than other existing methods. In addition, some precious properties of these operators are discussed. Afterwards, the defined aggregation operators are used to MADM with q-rung orthopair hesitant fuzzy numbers (q-ROHFNs) and the MADM decision-making model is developed. In accordance with the defined operators and built model, the q-rung orthopair hesitant fuzzy weighted power generalised Heronian mean (q-ROHFWPGHM) operator and the q-rung orthopair hesitant fuzzy weighted power generalised geometric Heronian mean (q-ROHFWPGGHM) operator are applied to deal with green supplier selection in supply chain management, and the availability and superiority of the proposed operators are analysed by comparing with some existing approaches. The method presented in this paper can effectually solve the MADM problems which the decision-making information is expressed by q-ROHFNs and the attributes are interactive. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple attribute decision making problems with Pythagorean 2-tuple linguistic information. Then, we utilize power average and power geometric operations to develop some Pythagorean 2-tuple linguistic power aggregation operators: Pythagorean 2-tuple linguistic power weighted average (P2TLPWA) operator, Pythagorean 2-tuple linguistic power weighted geometric (P2TLPWG) operator, Pythagorean 2-tuple linguistic power ordered weighted average (P2TLPOWA) operator, Pythagorean 2-tuple linguistic power ordered weighted geometric (P2TLPOWG) operator, Pythagorean 2-tuple linguistic power hybrid average (P2TLPHA) operator and Pythagorean 2-tuple linguistic power hybrid geometric (P2TLPHG) operator. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the Pythagorean 2-tuple linguistic multiple attribute decision making problems. Finally, a practical example for enterprise resource planning (ERP) system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

In the multi-criteria decision-making (MCDM), multi-objective optimisation by ratio analysis (MOORA) plus the full multiplicative form (MULTIMOORA) are a useful method, which has the flexibility and robustness properties. The aim of this paper is to present a more robust method of MULTIMOORA to solve the MCDM evaluations with interval-valued Pythagorean fuzzy sets (IVPFSs). More specifically, considering the mutual support relationship among the input arguments, we firstly extend the power average (PA) and power geometric (PG) operators to accommodate the interval-valued Pythagorean fuzzy environment. For this, we propose four aggregation operators, including the interval-valued Pythagorean fuzzy power average (IVPFPA), the weighted interval-valued Pythagorean fuzzy power average (WIVPFPA), the interval-valued Pythagorean fuzzy power geometric (IVPFPG) and the weighted interval-valued Pythagorean fuzzy power geometric (WIVPFPG). With the aid of WIVPFPA and WIVPFPG, we further put forward a new robustness of the ranking method, i.e., the interval-valued Pythagorean fuzzy power average-based MULTIMOORA (IVPFPA-MULTIMOORA). Due to the availability and vast diversity among open-source electronic health record (EHR) systems, choosing an appropriate system is a challenge for many health-care institutions under the interval-valued Pythagorean fuzzy environment. Based on these above-mentioned research findings, we successfully utilise the IVPFPA-MULTIMOORA method to solve the selection problem of hospital open-source EHRs systems for MedLab in Ghana. Meanwhile, the effectiveness of the proposed method is verified by some comparative analyses. [ABSTRACT FROM AUTHOR]

The aim of the paper is to develop new aggregation operators using Bonferroni means, ordered weighted averaging (OWA) operators and some distance measures. We introduce the Bonferroni-Hamming weighted distance (BON-HWD), Bonferroni OWA distance (BON-OWAD), Bonferroni OWA adequacy coefficient (BON-OWAAC) and Bonferroni distances with OWA operators and weighted averages (BON-IWOWAD). The main advantages of using these operators are that they allow the consideration of different aggregations contexts to be considered and multiple comparison between each argument and distance measures in the same formulation. An application is developed using these new algorithms in combination with Pichat algorithm to solve a group decision-making problem. Creative personality is taken as an example for forming creative groups. The results show fuzzy dissimilarity relations in order to establish the maximum similarity subrelations and find groups according to each individual's creative personality similarities. [ABSTRACT FROM AUTHOR]

Firms' operation requires teamwork. But it is always a challenge for firms to build teams that consist of suitable employees and meet the requirements of projects. Team candidates' individual capabilities and their abilities in building and maintaining interpersonal relationship are identified as the two key factors affecting team formation. This paper extends previous studies on team formation and proposes a cooperative effect-based team formation model (CE-TFM) for decision-makers in firms to select the most competent and cooperative members. The model aims to build a strong team by selecting the proper members who can help the team improve its performance. Specifically, a genetic algorithm is designed to find solutions for the proposed model. Covering and the positive cooperative effect indicator are applied to describe the interpersonal relationship among team candidates. In addition, the effect aggregation operator is adopted to find solutions for the positive team formation model. Particularly, four methods and the corresponding calculation procedures are provided for solving CE-TFM. Finally, a case study is implemented to test the proposed model. The results indicate that CE-TFM can not only merge decision-making preferences into the process of team formation but also improve team performance. [ABSTRACT FROM AUTHOR]

In this paper, we expand the Muirhead mean (MM) operator and dual MM (DMM) operator with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose the 2-tuple linguistic neutrosophic numbers MM (2TLNNMM) operator, 2-tuple linguistic neutrosophic numbers weighted MM (2TLNNWMM) operator, 2-tuple linguistic neutrosophic numbers DMM (2TLNNDMM) operator and 2-tuple linguistic neutrosophic numbers weighted DMM (2TLNNWDMM) operator. Then, the MADM methods are proposed with these two operators. Finally, an example for green supplier selection is used to show the proposed methods. [ABSTRACT FROM AUTHOR]

Network data envelopment analysis (DEA) considers internal structures of decision-making units. Unlike the standard DEA, network DEA imposes hurdles for measuring scale efficiency due to the fact that (i) overall efficiency aggregated by the stage or divisional technical efficiencies is highly non-linear and only solvable in a heuristic manner, or (ii) the overall efficiency which concerns exclusively inputs and outputs of a system is difficult to be decomposed into divisional efficiencies. In this paper, we establish a mathematical transformation to convert the corresponding non-linear programming problem into second order cone programming programme. The transformation is shown to be versatile in dealing with both constant returns to scale and variable returns to scale models under the two-stage network DEA. Meanwhile, our numerical results reveal that overall scale efficiency in two-stage network DEA is consistent with scale efficiency in conventional DEA. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the decision-making problem under the Pythagorean fuzzy environment by proposing some generalised aggregation operators. For it, we improve the existing aggregation operators by adding the pairs of the hesitation between the membership functions and hence proposed some new operational laws for Pythagorean fuzzy numbers using Einstein norm operations. Based on these laws, some weighted, ordered weighted and hybrid geometric interaction aggregation operators are proposed. The prominent characteristics of these operators are also studied. Further, a group decision-making problem is illustrated and validated through a numerical example. A comparative analysis of the proposed and existing studies is performed to show the validity of the proposed operators. [ABSTRACT FROM AUTHOR]

From last few decades criminal activities and terror attacks have been increased. In a big city or in highly populated city, it is very difficult to keep under surveillance each and every area of that city throughout whole day and night. Since, it will require huge amount of deployment of police duty and such type of deployment can cause heavy work load on the enforcement agencies. It has been seen that, some types of areas are often targeted by terrorist than others, mainly train junctions, bus stoppages, market places, near temples etc. In this paper, a new aggregation operator on generalized triangular fuzzy number has been defined. Through the newly developed aggregation operator, a fuzzy multi criteria decision making approach has been constructed which enables us to determine the area of a city which may be the potentially under threat, during any interval of time on a regular working day. [ABSTRACT FROM AUTHOR]

The theory of intuitionistic fuzzy sets (IFS) is widely used for dealing with vagueness and the Dempster–Shafer (D-S) evidence theory has a widespread use in multiple criteria decision-making problems under uncertain situation. However, there are many methods to aggregate intuitionistic fuzzy numbers (IFNs), but the aggregation operator to fuse basic probability assignment (BPA) is rare. Power average (P-A) operator, as a powerful operator, is useful and important in information fusion. Motivated by the idea of P-A power, in this paper, a new operator based on the IFS and D-S evidence theory is proposed, which is named as intuitionistic fuzzy evidential power average (IFEPA) aggregation operator. First, an IFN is converted into a BPA, and the uncertainty is measured in D-S evidence theory. Second, the difference between BPAs is measured by Jousselme distance and a satisfying support function is proposed to get the support degree between each other effectively. Then the IFEPA operator is used for aggregating the original IFN and make a more reasonable decision. The proposed method is objective and reasonable because it is completely driven by data once some parameters are required. At the same time, it is novel and interesting. Finally, an application of developed models to the ‘One Belt, One road' investment decision-making problems is presented to illustrate the effectiveness and feasibility of the proposed operator. [ABSTRACT FROM AUTHOR]

This paper presents the heavy ordered weighted moving average (HOWMA) operator. It is an aggregation operator that uses the main characteristics of two well-known techniques: the heavy ordered weighted averaging (OWA) and the moving averages. Therefore, this operator provides a parameterized family of aggregation operators from the minimum to the total operator and includes the OWA operator as a special case. It uses a heavy weighting vector in the moving average formulation and it represents the information available and the knowledge of the decision maker about the future scenarios of the phenomenon, according to his attitudinal character. Some of the main properties of this operator are studied, including a wide range of families of HOWMA operators such as the heavy moving average and heavy weighted moving average operators. The HOWMA operator is also extended using generalized and quasi-arithmetic means. An example concerning the foreign exchange rate between US dollars and Mexican pesos is also presented. [ABSTRACT FROM AUTHOR]

The scoring habits of experts are embodied in expert evaluation scale (EES). We show that many inaccurate consensus and ranking results can occur in group decision making (GDM) problems without considering EES. To remove the impacts of EESs, we propose the definition of EES and the uniform EES (UEES) strategy. Then, we prove the influence of the selection of UEES strategy on two consistency measures and two aggregation operators. In real-world GDM problems, a decision maker (DM) usually hopes that the final scores of alternatives are subject to his/her desired distribution. Therefore, a novel ranking method by UEES strategy is proposed to obtain DM's desired final scores. Finally, a numerical example is used to clarify the effectiveness of the proposed method, and a simulation experiment shows that the proposed method is more suitable for large sample cases but the accuracy is still at a high level under small sample cases. [ABSTRACT FROM AUTHOR]

Appropriately capturing the logic or results of a detailed simulation through some kind of aggregation to pass up a model hierarchy is a difficult task. This aggregation task involves two main questions: 'what' to aggregate and 'how' to perform this aggregation. This paper provides a well-defined approach for the 'what' portion of the aggregation process for hierarchical simulation models. We start by characterizing a simulation model in a mathematical format. We then use this mathematical description to identify what portion of a lower-level model can be aggregated, or to capture the logical flow of the full lower-level model. A useful mathematical representation of the simulation structure and logic is through the development of a network representation of the model. This, in turn, can be systematically decomposed into smaller sub-networks by performing model decomposition by means of factor analytic methods, which is the main topic of discussion. [ABSTRACT FROM AUTHOR]

Compared with the large number of research papers dealing with the ordered weighted averaging (OWA) operators, the research on relative quantifiers has been relatively sparse so far. With the quantifier guided aggregation method of OWA operators and the generating function representation of regular increasing monotone (RIM) quantifiers, a parameterized RIM quantifier family with the maximum entropy principle is proposed. This quantifier family can be regarded as the continuous case of the commonly used maximum entropy OWA (MEOWA) operator. Like the MEOWA operator, the RIM quantifier with maximum entropy generating function also possesses some interesting properties. One of them is that the aggregation value for any aggregated set always monotonically increases with the orness level. This can be used to represent the decision maker's preference information, but with free dimension. [ABSTRACT FROM AUTHOR]

The present paper discusses the problems of planning and optimization of maneuver (reorientation) strategies of large-space flexible structures as long booms and tethered satellites subject to conditions for minimization of deviations and vibrations. An approach based on the multibody system methodology for dynamics simulation and forward analysis is proposed. The optimization problem is defined as nonlinear programming problem. Iterative procedure based on directional derivatives and consequential dynamic analysis is applied. Polynomial approximation of the input motion is used, its coefficient being changed using optimization techniques. The principle for minimization of the total energy of the deformable system is applied in an algorithm for planning of controlled motion and computation of driving forces of the actuators. Admissible velocities of the maneuvers are estimated. The process of exaggeration of high order vibrations is analyzed. Examples of maneuver implementation of extremely flexible tethered satellite and suppression of vibrations are presented. [ABSTRACT FROM AUTHOR]

This paper is devoted to the study of a special kind of aggregation operators: commutative, non-decreasing binary operators F on [0,1] with annihilator and such that F (0,0)=0 and F (1,1)=1 . A characterization of this kind of operators is given, including many examples and properties in the general case. Special attention is paid to the associative case, leading to a characterization by means of a median expression. This type of operators can be viewed as a generalization of both uninorms and nullnorms. [ABSTRACT FROM AUTHOR]

This paper investigates the behaviour of Indian aggregate imports during the period 1971-1995. In the empirical analysis of the aggregate import demand function for India, cointegration and error correction modelling approaches have been used. In the aggregate import demand function for India, import volume is found to be cointegrated with relative import price and real GDP. The econometric estimates of the import-demand function for India suggest that import-demand is largely explained by real GDP, and is generally less sensitive to import price changes. Import liberalization is found to have had little impact on import demand. [ABSTRACT FROM AUTHOR]

A pervasive theme in both accounting and statistics is aggregation. However, in contrast to statistics, a customary standard for determining the best aggregation rule in accounting is unavailable or, at least, not explicitly defined. Also, most accounting procedures follow a well-specified recursive algorithm of updating a summarized history number (a beginning balance sheet number) by the current period's activities (changes). In this paper, we present a setting in which the best accounting aggregation rule arises naturally, resembles observed depreciation schedules, and proceeds recursively in a manner analogous to the above outlined stock-flow updating process. Our main results are (1) in every period, the performance of the BLU estimate based on active investments can be replicated by the period's depreciation amount and (2) in every period, the performance of the BLU estimate based on the entire history of investments can be replicated by a recursive procedure that updates the BLU estimate of the previous period with the current period's investment realization. Depreciation successfully satisfies multiple objectives - it serves as a periodic allocation of realized investment amounts and as a statistic for the unknown investment population mean. Depreciation schedules commonly used in practice, straight-line, accelerated and declining balance, are shown to be best in particular settings. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) have been widely used in solving multiobjective optimization problems (MOP). The foremost hindrance limiting strength of GA is the large number of nondominated solutions and the computational complexity involved in selecting a preferential candidate among the set of nondominated solutions. In this paper, we analyze the approach of applying aggregation operator in place of density-based indicator mechanism in cases where Pareto dominance method fails to decide the preferential solution. We also propose a new aggregation function () and compare the results obtained with prevailing aggregation functions suggested in the literature. We demonstrate that the proposed method is computationally less expensive with overall complexity of. To show the efficacy and consistency of the proposed method, we applied it on different, two- and three-objective benchmark functions. Results indicate a good convergence rate along with a near-perfect diverse approximation set. [ABSTRACT FROM AUTHOR]

In this paper, we show that the setof alln-ary aggregation functions on a complete latticeLis a complete lattice and we study some properties of this lattice. IfLis a bounded lattice andnis a natural number such that, we obtain thatforcan be embedded into the lattice. We generate aggregation functions from monotone functions. We introduce the concept of internal product of aggregation functions. We give some examples of aggregation functions on bounded lattices. [ABSTRACT FROM PUBLISHER]

As a variation of fuzzy sets and intuitionistic fuzzy sets, neutrosophic sets have been developed to represent uncertain, imprecise, incomplete and inconsistent information that exists in the real world. Simplified neutrosophic sets (SNSs) have been proposed for the main purpose of addressing issues with a set of specific numbers. However, there are certain problems regarding the existing operations of SNSs, as well as their aggregation operators and the comparison methods. Therefore, this paper defines the novel operations of simplified neutrosophic numbers (SNNs) and develops a comparison method based on the related research of intuitionistic fuzzy numbers. On the basis of these operations and the comparison method, some SNN aggregation operators are proposed. Additionally, an approach for multi-criteria group decision-making (MCGDM) problems is explored by applying these aggregation operators. Finally, an example to illustrate the applicability of the proposed method is provided and a comparison with some other methods is made. [ABSTRACT FROM AUTHOR]

In this paper, we extend the generalized weighted geometric and generalized ordered weighted geometric operators to intuitionistic fuzzy environments, that is, we develop a series of generalized intuitionistic fuzzy geometric operators to aggregate input arguments that are expressed by intuitionistic fuzzy values based on Archimedean t-conorm and t-norm. Then some desired properties of these aggregation operators are investigated. The relations between these operators and some existing intuitionistic fuzzy geometric aggregation operators are discussed in detail. Furthermore, applying these proposed operators, we develop an approach for multi-criteria decision making with intuitionistic fuzzy information. Finally, a practical example is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

With the generalisation of intuitionistic fuzzy sets (IFSs), picture fuzzy sets (PFSs) have been developed based on membership, neutral membership, and non-membership degrees. Compared with IFSs, PFSs can more accurately represent uncertainty in real-world decision-making problems. Recently, the research on decision-making theories and methods under picture fuzzy environments has been rapidly developing. Therefore, this manuscript presents a systematic review of picture fuzzy decision-making theories and methods, including the context in which this field was developed, the current status and advancements of this field, and the main research results obtained with the picture fuzzy information. First, this review introduces the development process of PFSs and the current status of corresponding theories. Next, the basic theories based on PFSs, including operations rules, measures, and aggregation operators are introduced. Afterward, this review summarises the current research on multi-criteria decision-making (MCDM), multi-criteria group decision-making (MCGDM), and large-scale group decision-making (LSGDM) methods with picture fuzzy information. Finally, future research directions of picture fuzzy decision-making theories and methodologies are discussed. [ABSTRACT FROM AUTHOR]

As for many practical evaluation problems, the experts are asked to provide the information not only about the evaluation results of the research objects but also the familiarity with the research topics (belief levels). The goal of this paper is to investigate the intuitionistic fuzzy information aggregation methods under belief levels. First, I briefly recall the basic concepts of intuitionistic fuzzy sets. Then, I propose some generalized belief aggregation operators to aggregate the intuitionistic fuzzy values, such as the generalized belief intuitionistic fuzzy weighted averaging operator and the generalized belief intuitionistic fuzzy ordered weighted averaging operator. Then again, I research the properties and special cases of the proposed operators. Last, a real example about the anonymous review of National Science Foundation of China is employed to show the effectiveness of the proposed method in practical applications. [ABSTRACT FROM AUTHOR]

In this paper, we define various induced intuitionistic fuzzy aggregation operators, including induced intuitionistic fuzzy ordered weighted averaging (OWA) operator, induced intuitionistic fuzzy hybrid averaging (I-IFHA) operator, induced interval-valued intuitionistic fuzzy OWA operator, and induced interval-valued intuitionistic fuzzy hybrid averaging (I-IIFHA) operator. We also establish various properties of these operators. And then, an approach based on I-IFHA operator and intuitionistic fuzzy weighted averaging (WA) operator is developed to solve multi-attribute group decision-making (MAGDM) problems. In such problems, attribute weights and the decision makers' (DMs') weights are real numbers and attribute values provided by the DMs are intuitionistic fuzzy numbers (IFNs), and an approach based on I-IIFHA operator and interval-valued intuitionistic fuzzy WA operator is developed to solve MAGDM problems where the attribute values provided by the DMs are interval-valued IFNs. Furthermore, induced intuitionistic fuzzy hybrid geometric operator and induced interval-valued intuitionistic fuzzy hybrid geometric operator are proposed. Finally, a numerical example is presented to illustrate the developed approaches. [ABSTRACT FROM AUTHOR]

The methodology of aggregation of known nonparametric regression estimators into a single better estimator has received increasing attention in statistical literature. Traditional aggregation means that a linear or convex combination of several estimators is considered. Wavelet regression estimation, due to its multiresolution nature, presents another opportunity for aggregation - using different estimation procedures on different resolution scales. Such an opportunity becomes attractive if known wavelet estimators have desired complementary properties on different frequencies. The difficulty of such an aggregation is that the assignment of scales depends on an underlying regression function and regression errors. This paper proposes a data-driven aggregation of two wavelet estimators - SureBlock of Cai and Zhou [(2009), 'A Data-driven Block Thresholding Approach to Wavelet Estimation', Annals of Statistics, 37, 569-595] and Universal of Efromovich [(1999a,b), Nonparametric Curve Estimation: Methods, Theory and Applications, New York: Springer; 'Quasi-linear Wavelet Estimation', Journal of the American Statistical Association, 94, 189-204] - to achieve a better quality of estimation, better data-compression, and better visualisation of functions with different smoothness characteristics on low and high frequencies. The proposed estimator is motivated by an applied problem of denoising and compression of ultra-fast (UF) functional magnetic resonance imaging (fMRI) - the new magnetic resonance technology that screens the activity of brain voxels every 50 ms with the purpose of understanding human brain activity. The proposed aggregated wavelet estimator is supported by the asymptotic theory, tested via intensive numerical simulations and UF fMRI applications, and it is expected to be useful in similar applications. [ABSTRACT FROM AUTHOR]

Recent research has suggested that intra-day volatility may possess a component structure due to heterogeneous information arrivals. This paper reports evidence for the existence of such components in FTSE-100 stock index futures returns data. Preliminary GARCH model estimates support previous evidence for other markets indicating the breakdown of theoretical GARCH temporal aggregation properties over the intra-day period. However, the fractional integration properties of absolute and squared returns, and FIGARCH conditional volatility model estimates, lend strong support to the contention that volatility dynamics results from multiple sources given the invariance of the fractional difference parameter estimates to the degree of intra-day data temporal aggregation. [ABSTRACT FROM AUTHOR]

In this paper we consider the superposition of independent Coxian arrival streams. We propose a method to efficiently approximate the superposition process, based on state space aggregation. The method is applied to a single server queue and to an inventory control model. Simulation results show that the method accurately estimates performance characteristics and that it significantly outperforms two-moment approximations. [ABSTRACT FROM AUTHOR]

In this paper a model describing dynamics of phytoplankton population with aggregation and competition for resources between individuals is presented. The model is in the form of a coupled system of two nonlinear partial differential equations. The first equation describes population dynamics while the second describes how amount of available nutrient, necessary for sustenance of phytoplankton, changes with time in the environment. Model solution is approximated using a finite difference approximation and convergence of this approximation to a unique weak solution is established. [ABSTRACT FROM AUTHOR]

Supplier selection is an important multiple attribute group decision-making (MAGDM) problem. How to choose a suitable supplier is an evaluation process with different alternatives of multiple attributes, and it also relates to the expression of the evaluation value. Considering Schweizer-Sklar t-conorm and t-norm (SSTT) can make the information aggregation process more flexible than others, and the power average (PA) operator can eliminate effects of unreasonable data from biased decision-makers. So, we extend SSTT to interval-valued intuitionistic fuzzy numbers (IVIFNs) and define Schweizer-Sklar operational rules of IVIFNs. Then, we combine the PA operator with Schweizer-Sklar operations, and propose the interval-valued intuitionistic fuzzy Schweizer-Sklar power average operator, the interval-valued intuitionistic fuzzy Schweizer-Sklar power weighted average (IVIFSSPWA) operator, the interval-valued intuitionistic fuzzy Schweizer-Sklar power geometric operator and the interval-valued intuitionistic fuzzy Schweizer-Sklar power weighted geometric (IVIFSSPWG) operator, respectively. Furthermore, we study some desirable characteristics of them and develop two methods on the basis of IVIFSSPWA and IVIFSSPWG operators. At the same time, we apply the two methods to deal with the MAGDM problems based on supplier selection. Finally, an illustrative example of supplier selection problem is given to testify the availability of the presented operators. [ABSTRACT FROM AUTHOR]

Existing decision-making methods cannot work under the probabilistic uncertain linguistic environment where the decision makers give different uncertain linguistic terms as their assessments and the weights of assessments are different. In this paper, a novel concept called probabilistic uncertain linguistic term set is proposed, which is composed of some possible uncertain linguistic terms associated with the corresponding probabilities. Then, the normalization process, comparison method, basic operations, and aggregation operators are studied for probabilistic uncertain linguistic term sets. After that, an extended technique for order preference by similarity to an ideal solution method and an aggregation-based method are developed to rank the alternatives and then select the best one for multi-attribute group decision-making with probabilistic uncertain linguistic information. Finally, a practical case concerning the selection of Cloud storage services is shown to illustrate the applicability of probabilistic uncertain linguistic term sets. [ABSTRACT FROM AUTHOR]

This study defines interval type basic uncertain information and BUI type basic uncertain information, which are two extensions of basic uncertain information and can model more types of uncertainties and uncertainty involved evaluation problems. Under rules based and linguistic decision making environments, we analyze and provide some classification rules with real values, interval values, BUI granules, interval type basic uncertain information and BUI type basic uncertain information. Using the proposed new concepts and decision making methods, an illustrative example in preference and uncertainty involved multi-source evaluation is presented. [ABSTRACT FROM AUTHOR]

In decision support systems, linguistic fuzzy information played an important role and the linguistic fuzzy aggregation operators (AOs) worked in group decision support systems. Recently, we proposed the linguistic picture fuzzy (LPF) sets, which is the extension of the linguistic intuitionist fuzzy sets, to reflect the ambiguity and vagueness of knowledge in decision-making (DM) problem. The goal of this research work is to define a new family of LPF AOs through the use of Dombi operations and Heronian mean (HM) operator. In addition to fusing individual attribute values, the evolved operators are good ability to handle the common association between the attributes, making them more appropriate to effectively solve difficult multi-attribute DM (MADM) problems. Therefore, we developed an approach for MADM problem based on LPF Dombi HM operators and solved an emergency programme selection problem. The comparison section provides the effectiveness, reliability and practicality. [ABSTRACT FROM AUTHOR]

The objective of this study is to introduce some covariance ordered weighted logarithmic averaging (Cov-OWLA) operators. Covariance measures the conduct of one variable based on the behavior of another; this characteristic makes it key for decision-making processes, especially when applied in uncertain environments. We present some families and particular cases of the introduced operators, including their generalized, induced, and generalized induced formulations. An illustrative example using real-world data on tourism gross domestic product and homeland security indicators is proposed. The results show that there was a positive linear relation between the introduced variables. In this scenario, crime did not affect tourist activity, but tourist activity incited crime. The resulting aggregation with Cov-OWLA operators shows a generally better fit when compared to traditional methods. [ABSTRACT FROM AUTHOR]

Numerous methods based on square rasters have been proposed for polygon generalization. However, these methods ignore the inconsistent distance measurement among neighborhoods of squares, which may result in an imbalanced generalization in different directions. As an alternative raster, a hexagon has consistent connectivity and isotropic neighborhoods. This study proposed a hexagon-based method for polygon generalization using morphological operators. First, we defined three generalization operators: aggregation, elimination, and line simplification, based on hexagonal morphological operations. We then used corrective operations with selection, skeleton, and exaggeration to detect, classify, and correct the unreasonably reduced narrow parts of the polygons. To assess the effectiveness of the proposed method, we conducted experiments comparing the hexagonal raster to square raster and vector data. Unlike vector-based methods in which various algorithms simplified either areal objects or exterior boundaries, the hexagon-based method performed both simplifications simultaneously. Compared to the square-based method, the results of the hexagon-based method were more balanced in all neighborhood directions, matched better with the original polygons, and had smoother simplified boundaries. Moreover, it performed with shorter running time than the square-based method, where the minimal time difference was less than 1 min, and the maximal time difference reached more than 50 mins. [ABSTRACT FROM AUTHOR]

Emergency communication networks play a vital role in disaster monitoring, transmission, and application during disaster emergency response (DER), however, the performance and stability of edge nodes in the emergency communication networks are often weak due to limited communication and computation resources. This weakness directly affects the quality of service (QoS) of the geospatial edge service (GES) chains involved in emergency monitoring. Existing research predominantly addresses service compositions in stable environments, neglecting the aggregation of efficient and robust GES chains in emergency communication networks. This study proposes an evolutionary particle swarm optimization (EPSO)-based emergency monitoring GES chain in an emergency communication network. It includes a GES chain model of emergency environment monitoring for tailing areas, as well as the designs of the particle chromosome encoding method, fitness evaluation model, and particle chromosome swarm update operators of the EPSO-based GES chain. Finally, the study conducts emergency environment monitoring experiments for tailing areas using the proposed method. Experiments results demonstrate that the proposed method significantly enhances the efficiency, stability, and reliability of emergency monitoring GES chains in the emergency communication network. This is crucial to providing fast and reliable services for DER during natural disasters. [ABSTRACT FROM AUTHOR]

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This study aims to study the conception of the complex interval-valued q-rung orthopair 2-tuple linguistic set (CIVQRO2-TLS), which is a new powerful mixture to handle unreliable and vague data in realistic decision concerns. We also explore its fundamental properties as well as important laws. In the occurrence of the above theory, we discover some useful aggregation methods for the CIVQRO2-TLS, including the CIVQRO2-TLWA, CIVQRO2-TLOWA, CIVQRO2-TLHA, CIVQRO2-TLWG, CIVQRO2-TLOWG, and CIVQRO2-TLHG operators. To demonstrate the beneficial features of the invented works, a multi-attribute decision-making (MADM) system is presented and exposed to the supremacy of the presented operators with the help of several examples. In last, we elaborate on the advantages, comparative analysis, and graphical interpretation of the invented approaches. [ABSTRACT FROM AUTHOR]

The recognition of structures is fundamental to map generalization, furnishing structural information that assists in choosing and parameterizing generalization operators. We specifically focus on the process of recognizing groups of small polygons in geological maps as a prerequisite to subsequent aggregation or typification operators. Proximity between polygons represents an essential criterion in identifying neighboring map objects. Here, network-based analysis is used for effective definition and refinement of candidate group members, applying criteria such as the distance between polygons, polygon size, shape, orientation, and feature attributes such as rock type. Starting off from the Delaunay triangulation of the polygon centroids, the global and local long edges, which initially define the network, are removed. The modified network is loaded with additional criteria, and edges are kept or removed based on the local similarities of the polygons they connect. This approach leads to more homogeneous, meaningful groups of polygon features in geological maps. [ABSTRACT FROM AUTHOR]

Group emergency decision-making is an uncertain and dynamic process, in which the decision makers may be bounded rational and have a risk appetite. To depict the vague qualitative assessments, the probabilistic linguistic term sets are employed to express the perceptions of decision makers. First, considering the regret-aversion of the decision makers' psychological characteristic, the value function and the regret-rejoice function in the regret theory are modified to adapt the probabilistic linguistic information. Second, the definition and aggregation operators of the probabilistic linguistic time variable are proposed to describe and aggregate the dynamic decision information. Third, the probabilistic linguistic models based on the dynamic reference point method and the regret theory are studied to maximise the expectation-levels of alternatives at the relative time point. The proposed method is applied to select the optimal response strategy for the outbreak of COVID-19 in China. Finally, the comparative analysis is designed to verify the applicability and reasonability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper deals with pseudometrics aggregation, that is, it studies how to combine a collection of such operators in order to obtain a single one as output. It shows how this problem may be solved in terms of a closely-related one, known as metric preservation, for which different conditions and a characterization are available. The particular case of bounded pseudometrics is also addressed, and, finally, the obtained results are applied to some common aggregation operators. [ABSTRACT FROM AUTHOR]

The evaluation and decision taking methods proposed in this study bypass some final result obtained by performing traditional aggregation operators, and thus they provide some alternative way and choice for decision making and add diversity and flexibility as well. To achieve this purpose, three different types of partitions of input space are introduced, including a usual partition and two ordering determined partitions. Some relations and properties of those partitions are discussed. Several detailed construction methods for those different partitions are also proposed. A detailed decision problem is expressed by two different decision paradigms, with one relating to traditional aggregation operators and the other revolving around some decision functions which are based on partitions. As an extension, we also discuss some methods for generating new aggregation operators from given collections of aggregation operators. [ABSTRACT FROM AUTHOR]

We present a framework based on a higher education application preference list that allows a different type of flexible aggregation and, hence, the analysis and clustering of application data. Preference lists are converted into scores. The proposed approach is demonstrated in the context of higher education applications in Hungary over the period of 2006–2015. Our method reveals that efforts to leverage center-periphery differences do not fulfill expectations. Furthermore, the student's top preference is very hard to influence, and recruiters may build their strategy on information about the first and second choices. [ABSTRACT FROM AUTHOR]

In this article, the VIKOR method is proposed to solve the multiple criteria group decision making (MCGDM) with 2-tuple linguistic neutrosophic numbers (2TLNNs). Firstly, the fundamental concepts, operation formulas and distance calculating method of 2TLNNs are introduced. Then some aggregation operators of 2TLNNs are reviewed. Thereafter, the original VIKOR method is extended to 2TLNNs and the calculating steps of VIKOR method with 2TLNNs are proposed. In the proposed method, it's more reasonable and scientific for considering the conflicting criteria. Furthermore, the VIKOR are extended to interval-valued 2-tuple linguistic neutrosophic numbers (IV2TLNNs). Moreover, a numerical example for green supplier selection has been given to illustrate the new method and some comparisons are also conducted to further illustrate advantages of the new method. [ABSTRACT FROM AUTHOR]

This study proposes some extended aggregation operators to model Extreme Conformity and Bounded Extreme Conformity which were proposed in a recent work. In detail, we propose Interval Judged s-t-A extreme operators and Selective Leveled-Preferences Ordered Weighted Averaging operators, respectively, and analyze some of their properties such as the related degenerations and monotonicities. Both types of operators have more flexibility and adjustability. An application of Interval Judged s-t-A extreme operators in project evaluation and decision-making is also introduced. [ABSTRACT FROM AUTHOR]