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Maritime connectivity is becoming increasingly important due to its inextricable relation to maritime trade, especially in the global economy. This paper presents a robust framework to rank countries on their maritime connectivity. Typically, in most index creation models, the arithmetic mean is used to build a composite index. In this paper, we propose a more reliable ranking approach, by applying the method of stochastic multicriteria acceptability analysis (SMAA). In this, we take into account the variability in the weights assigned to the connectivity factors, thus producing a probabilistic ranking for each country (i.e., the probability to achieve a certain rank). UNCTAD's ranking, based simply on the arithmetic mean, where all the weights are equal, becomes a special case of our approach. Our results show that China achieves the highest level of connectivity, followed by Singapore. [ABSTRACT FROM AUTHOR]

In group decision making, each expert's background and the level of knowledge and ability differ, which makes the expert's information inputs to the decision-making process heterogeneous. Such heterogeneity in the information can affect the outcome of the selection of the decision alternatives. This paper therefore attempts to partition the heterogeneous information into homogeneous groups to elicit similar (related) and dissimilar (unrelated) data using a clustering algorithm. We then develop an aggregation approach to gather the collective opinions from the homogeneous clusters to accurately model the decision problem in a group setting. The proposed aggregation approach, labeled as the generalized partitioned Bonferroni mean (GPBM), is studied to investigate the characteristics of the aggregation operator. Further, we extend the GPBM concept to an interval-valued fuzzy set context using the additive generators of the strict t-conorms and we develop two other new aggregation operators: the interval-valued GPBM (IVGPBM) and the weighted IVGPBM (WIVGPBM). We analyze the aggregation of fuzzy numbers by the IVGPBM operator using interval arithmetic involving α -cuts and the α -cut-based decomposition principle of fuzzy numbers. Two practical examples are presented to illustrate the applicability of these operators, and a comparison is conducted to highlight the effects of the confidence level and the sensitivity of the parameters chosen, analyzing the results with the parameterized strict t-conorm. Finally, we compare the experimental results of the proposed method with existing methods. [ABSTRACT FROM AUTHOR]

The qualitative flexible multiple criteria method (QUALIFLEX) is a convenient outranking technique to handle multicriteria decision‐making (MCDM) problems due to its less complexity and high applicability, while the multiple criteria hierarchy process (MCHP) allows decision makers to deal with the hierarchical structure of criteria set where decision makers can even estimate results for a particular subcriterion at some intermediate level of the hierarchy. The main focus of our study is the amalgamation of the MCHP and QUALIFLEX methodology with special emphasis on modeling interaction among the criteria using the concept of bipolar Choquet integral. To give the decision makers more freedom for expressing their cognition about membership and nonmembership grades, the q‐rung orthopair fuzzy (q‐ROF) environment is adopted to expresses the criteria measurement. To facilitate this, it is first proposed a revised closeness index for q‐ROF to identify the appropriate ordering. Further, it is aimed to establish a new framework by implementing stochastic multiobjective acceptability analysis in our proposed extended QUALIFLEX method to take into account a variety of parameters compatible with the descriptive information regarding the relative importance and interaction of different criteria provided by the decision maker. Finally, a numerical example based on the supplier selection problem is presented to illustrate the proposed methodology in the decision problem. [ABSTRACT FROM AUTHOR]

Inconel 690 is one of the most comprehensively used heat-resistive superalloys, exclusively applied in aerospace or aircraft engineering. Due to its implausible strength and rigidity, it possesses dull machinability. Hence, the machinability of Inconel alloys has turned out to be an extremely significant topic for study. Minimum quantity lubrication–vegetable oil synergy already made a reliable venture into the challenging facets of Inconel machining. However, for the effective controlling of end milling parameters, it is an imperative idea to imply Pareto-based hybrid multi-objective optimization strategy in machining domain. Thus, for the first time, a three-stage computational approach combining the theory of gene expression programming (GEP), non-dominated sorting genetic algorithm-II (NSGA-II) and technique for order preference by similarity to ideal solution model (TOPSIS) were utilized. Here, GEP-generated explicit equations are applied in NSGA-II to search the different solutions, and TOPSIS method is applied to choose the best compromise solution from non-dominated Pareto optimal solutions. Furthermore, a comparative study showed that the average error obtained between the experimental and predicted response is 3.13%, which determines the modesty of the proposed optimization model. So, the results of this study enlighten the possibility of adopting Pareto-based hybrid algorithms in the domains of the metal cutting operation. [ABSTRACT FROM AUTHOR]

This article addresses orness measures to reflect the or‐like degree of the Bonferroni mean (BM) and its variants. Some properties of these operators associated with their orness measures are portrayed analytically. However, the general orness measure involves the multiple integrals with the integral fold number being the number of the aggregated elements and as a result, the computation becomes complicated when the number of the aggregated elements is large. Furthermore, the analytical formula of the orness measure often cannot be obtained. For this reason, this study concentrates on Monte Carlo simulation to validate the result. We estimate the two parameters of the BM for a predefined orness value and a fixed length of the input vector. Besides the theoretical study of orness measure related to BM and its variants, the article also explores the simulation‐based results. To support this, we provide four numerical examples. [ABSTRACT FROM AUTHOR]

Service quality acts as an antecedent to customer satisfaction (CS). Evaluation of service quality in an enterprise is vital to improve productivity and increase CS. Usually, it is difficult to rate service quality due to the presence of vagueness in the available information as well as impreciseness in the physical nature of the problem. The comprehensive intention of this paper is to present a robust modified SERVQUAL based multi-criteria decision making (MCDM) method to evaluate the quality of service and its interaction with the dynamics of CS. The proposed evaluation model is a hybrid model, which integrates three popular tools of decision making at the different stage. At first stage, service quality assessment SERVQUAL with the statistical tool has employed to identify appropriate factors affecting the service quality of the telecommunication network. In the next stage, to find the appropriate weight of the different factors in the evaluation process, fuzzy Rasch method is utilized. The final stage involves the selection of the service provider with the most contented customer based on fuzzy MCDM method. Moreover, a new risk minimizing evaluative model is proposed for the study. The strength of the proposed approach is its practical applicability and ability to provide solution under partial or lack of quantitative information. The proposed model is applied for evaluating the service quality of the telecommunication industry of Jorhat, Assam with respect to 256 participants on 15 criteria. Finally, sensitivity analysis is conducted to evaluate the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, it is proven that the natural extensions of a submodular coherent upper conditional probability, defined a class S properly contained in the power set of Ω, coincide on the class of all bounded and upper S-measurable random variables. Moreover, it is proven that a coherent upper conditional prevision can be represented as the Choquet integral, the pan-integral and the concave integral with respect to its associated Hausdorff outer measure hs and, denoted by S the σ-field of all hs-measurable sets, all these integral representations agree with the Lebesgue integral on the class of all S-measurable random variables. [ABSTRACT FROM AUTHOR]

Capturing specific interrelationship among input arguments has great importance in the process of aggregation as they may change the aggregation result significantly, which can lead viable changes in the overall decision outcome. In this study, we attempt to aggregate a set of inputs with certain heterogeneous interrelationship pattern among them. To do this, we introduce a new aggregation operator, which we call the extended geometric Bonferroni mean. We investigate its properties and develop an algorithm to learn its associated parameters based on decision maker's perceived view toward the aggregation process. Moreover, to learn such heterogeneous relationship among the inputs from the data set, we provide a learning algorithm. Examples are given to illustrate the realization of algorithm and to show certain advantages over the existing aggregation operators. [ABSTRACT FROM AUTHOR]

The objective of the present study is to develop/establish a web-based medical diagnostic support system (MDSS) by which health care support can be provided for people living in rural areas of a country. In this respect, this research provides a novel approach for medical diagnosis driven by integrating fuzzy and intuitionistic fuzzy (IF) frameworks. Subsequently, based on the proposed approach a web-based MDSS is developed. The proposed MDSS comprises of a knowledge base (KB) and intuitionistic fuzzy inference system (IFIS). Based on the observation that medical data cannot be described with both precision and certainty, a medical KB is constructed in the form of a set of if-then decision rules by employing both fuzzy and IF logics. After constructing the medical KB, a new set of patients is considered for diagnosing the diseases. For each patient, linguistic values of the patients' symptoms are considered as inputs of the proposed IFIS and modeled by using the generalized triangular membership functions. Subsequently, integrated fuzzy and IF rule-based inference system is used to find a valid conclusion for the new set of patients. In a nutshell, in this paper fuzzy rule-based and IFS based inference systems are combined for better and more realistic representation of uncertainty of the medical diagnosis problem and for more accurate diagnostic result. The method is composed of following four steps: (1) the modeling of antecedent part of the rules, which consist of linguistic assessments of the patients' symptoms provided by the doctors/medical experts with their corresponding confidence levels, by using generalized fuzzy numbers; (2) the modeling of consequent part, which reveals the degree of association and the degree of non-association of diseases into the patient, by using IFSs; (3) the use of IF aggregation operator in inference process; (4) the application of relative closeness function to find the final crisp output for a given diagnosis. Finally, the applicability of the proposed approach is illustrated with a suitable case study. This article has also justified the proposed approach by using similarity measurement. [ABSTRACT FROM AUTHOR]

The aim of this study is to propose an objective method for determining weights of criteria (also called attributes) based on a new measure of intuitionistic fuzzy information, called knowledge measure, in a real-world multi-criteria decision-making problem under intuitionistic fuzzy and interval-valued intuitionistic fuzzy environment. To address this issue, we first analyze the existing entropy measures and show that their use in objective weight determination process may lead us to produce unreliable weights of criteria by citing appropriate examples. Then we analyze important properties of knowledge measure of intuitionistic fuzzy set (IFS) and also define knowledge measure for interval-valued intuitionistic fuzzy set. Then a new method to determine the weights of criteria is developed on the basis of knowledge measure where information about criteria weights is completely unknown and partly known. A real-life example is presented to illustrate the proposed weight determination method and a comparative analysis is carried out to indicate the practicality and effectiveness of knowledge-based weight-generation method under both intuitionistic fuzzy and interval-valued intuitionistic fuzzy environment. Finally, we formulate the axioms for knowledge measure associated with IFSs and we also propose families (classes) of knowledge measures. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy multi-objective programming problem is considered where functional relationships between decision variables and objective functions are not completely known to us. Due to uncertainty in real decision situations sometimes it is difficult to find the exact functional relationship between objectives and decision variables. It is assumed that information source from where some knowledge may be obtained about the objective functions consists of a block of fuzzy if-then rules. In such situations, the decision making is difficult and the presence of multiple objectives gives rise to multi-objective optimization problem under fuzzy rule constraints. In order to tackle the problem, appropriate fuzzy reasoning schemes are used to determine crisp functional relationship between the objective functions and the decision variables. Thus a multi-objective optimization problem is formulated from the original fuzzy rule-based multi-objective optimization model. In order to solve the resultant problem, a deterministic single-objective non-linear optimization problem is reformulated with the help of fuzzy optimization technique. Finally, PSO (Particle Swarm Optimization) algorithm is employed to solve the resultant single-objective non-linear optimization model and the computation procedure is illustrated by means of numerical examples. [ABSTRACT FROM AUTHOR]

Nowadays, decision making problems have increased their complexity and a single decision maker cannot handle these problems, with a more diverse and comprehensive view of them being necessary, which results in group decision making (GDM) schemes. The complexity of GDM problems is often due to their inherent uncertainty that is not solved just by using a group. Consequently, different methodologies has been proposed to handle it, in which, the use of the fuzzy linguistic approach stands out. Among the multiple fuzzy linguistic modeling approaches, Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) information has been recently introduced, which enhances classical linguistic modeling that is based on single terms by providing linguistic expressions in a continuous linguistic domain. Its application to decision making is quite promising, but it is necessary to develop enough operators to accomplish aggregation processes in the decision solving scheme. So far, just a small number of aggregation operators have been defined for ELICIT information. Hence, this paper aims at providing new aggregation operators for ELICIT information by developing novel OWA based operators, such as the Induced OWA (IOWA) operator in order to avoid the OWA operator needs of reordering its arguments, because ELICIT information does not have an inherent order due to its fuzzy representation. Our proposal not only consists of extending the definition of an IOWA operator for ELICIT information with crisp weights, but it is also proposed a type-1 IOWA operator for ELICIT information in which both weights and arguments are fuzzy as well as the use of ELICIT information constructing the order inducing variable to reorder the arguments. Additionally, the use of ELICIT information in GDM demands the ability to manage majority based decisions that are better represented in the IOWA operator by linguistic quantifiers. Hence, a majority-driven GDM process for ELICIT information is proposed, which it is the first proposal for fulfilling the majority solving process for GDM while using ELICIT information. Eventually, an illustrative example and a brief comparative analysis are presented in order to show the performance of the proposal and its feasibility. [ABSTRACT FROM AUTHOR]