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The purpose of this paper is to build a novel data envelopment analysis (DEA) model to evaluate the efficiencies of decision making units (DMUs).

Using the Choquet integrals as aggregating tool, the authors give a novel DEA model to evaluate the efficiencies of DMUs.

It extends DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form. At last, the authors use the numerical examples to illustrate the performance of the proposed model.

The proposed DEA model can be used to evaluate the efficiency of the DMUs with multiple interactive inputs and outputs.

This paper introduce a new DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form.

The purpose of this paper is to propose novel attitudinal prioritization and correlated aggregating methods for multiple attribute group decision making (MAGDM) with triangular intuitionistic fuzzy Choquet integral.

Based on the continuous ordered weighted average (COWA) operator, the triangular fuzzy COWA (TF-COWA) operator is defined, and then a novel attitudinal expected score function for triangular intuitionistic fuzzy numbers (TIFNs) is investigated. The novelty of this function is that it allows the prioritization of TIFNs by taking account of the expert’s attitudinal character. When the ranking order of TIFNs is determined, the triangular intuitionistic fuzzy correlated geometric (TIFCG) operator and the induced TIFCG (I-TIFCG) operator are developed.

Their use is twofold: first, the TIFCG operator is used to aggregate the correlative attribute value; and second, the I-TIFCG operator is designed to aggregate the preferences of experts with some degree of inter-dependent. Then, a TIFCG and I-TIFCG operators-based approach is presented for correlative MAGDM problems. Finally, the propose method is applied to select investment projects.

Based on the TIFCG and I-TIFCG operators, this paper proposes a novel correlated aggregating methods for MAGDM with triangular intuitionistic fuzzy Choquet integral. This method helps to solve the correlated attribute (criteria) relationship. Furthermore, by the attitudinal expected score functions of TIFNs, the propose method can reflect decision maker’s risk attitude in the final decision result.

This paper aims to solve the problem of public resource allocation among vulnerable groups by proposing a new method called uncertain α-coordination value based on uncertain cooperative game.

First, explicit forms of uncertain Shapley value with Chouqet integral form and uncertain centre-of-gravity of imputation-set (CIS) value are defined separately on the basis of uncertainty theory and cooperative game. Then, a convex combination of the two values above called the uncertain α-coordination value is used as the best solution. This study proves that the proposed methods meet the basic properties of cooperative game.

The uncertain α-coordination value is used to solve a public medical resource allocation problem in fuzzy coalitions and uncertain payoffs. Compared with other methods, the α-coordination value can solve such problem effectively because it balances the worries of vulnerable group’s further development and group fairness.

In this paper, an extension of classical cooperative game called uncertain cooperative game is proposed, in which players choose any level of participation in a game and relate uncertainty with the value of the game. A new function called uncertain α-Coordination value is proposed to allocate public resources amongst vulnerable groups in an uncertain environment, a topic that has not been explored yet. The definitions of uncertain Shapley value with Choquet integral form and uncertain CIS value are proposed separately to establish uncertain α-Coordination value.

The focus in ICT development has shifted from performance to maximization of tangibility. In particular, the interests in emotion-based services increase and more attention is paid to improving the quality of service and maximizing tangibility. Hence, it is necessary to model human emotions and utilize inferred emotions for services in order to enhance the quality of emotion-based personalized services. To this end, this study aims to model personalized emotional space by means of V-A emotion model and to suggest a fuzzy-based emotion inference method.

This study takes advantage of V-A emotion model, AHP and fuzzy integral technique to implement an emotion inference based on the quantitative evaluation and systematic expression of human emotions. By means of AHP, the personalized V-A emotion model was established. Fuzzy measure and Choquet integral methods were applied for emotion inference based on the inputs of valence and arousal (V-A) values, with the results evaluated in an experiment.

Thayer's emotion model structures 12 human emotions in reference to V-A values. This structure, however, may differ depending on individuals, and thus a method to measure and express it appropriately is necessary. This study evaluates the relative weight of 12 emotions based on AHP and regularizes them to compose the personalized V-A emotional space by drafting the V-A model. In addition, fuzzy integral-based emotion inference has been tested in reference to the V-A values on the established V-A emotion space.

Fuzzy integral-based emotion inference has been tested in reference to the V-A values on the established personalized emotion space.

Quality cost is usually considered as a means to measure the quality level in a quality system. Since the interrelationship among quality cost components is complex, a general quantitative model for describing their relationship is not easy to construct for improving the quality. In the assessments of quality cost, some hidden quality costs, such as the goodwill loss due to lost customers’ reliability, are often neglected in the existing analysis methods. This may lead to reaching a sub‐optimal decision. In addition, the assessments of quantitative quality cost items are usually approximated, and therefore are imprecise in nature. Based on these considerations, we propose fuzzy approaches to evaluate quality improvement alternatives because of its fuzzy nature. An evidence fusion technique, namely Choquet fuzzy integral, is employed to aggregate the quality cost information. A composite index is determined to find the best quality improvement alternative. Finally, a numerical example is used to demonstrate the applicability of the approach.

Many problems in science and engineering fields involve decision making. Usually these decision‐making processes are based on several criteria that represent various experts' knowledge. Stakeholder prioritization is useful for assisting in decision‐making situations where various stakeholders have competing interests, resources are limited, and stakeholder requirements must be appropriately balanced. When these conflicts arise it is important to the success of the organization that it has prioritized each stakeholder according to the situation. To date, few researchers tried to resolve this question, mostly are based on intuitive and very simple reasoning methods which are error prone. The purpose of this paper is to propose a multi‐criteria decision analysis process to help decision makers when evaluating and prioritizing stakeholders.

In this process, Mitchell et al.'s model is used for identifying criteria on which stakeholders will be evaluated and the fuzzy Choquet integral as an aggregation operator. This research also tested and discussed the proposal using a case study from Toulouse city subway.

The results show the applicability of this process and the effectiveness of using the fuzzy Choquet integral than a traditional multi‐criteria evaluation method for human subjective evaluation, or when criteria are not mutually independent.

The highly subjective nature of criteria weights and rapid elicitation can lead to questions of validity. Also, results are not always widely accepted.

The paper is original in considering the stakeholder prioritization problem as a multi‐criteria decision analysis problem; using a simple and well‐known model to classify stakeholders, i.e Mitchell et al.'s model; and in using Choquet integral as an aggregation operator which allows considering interaction between criteria.

The purpose of this paper is to develop a probabilistic uncertain linguistic (PUL) TODIM method based on the generalized Choquet integral, with respect to the interdependencies between criteria, for the selection of the best alternate in the context of multiple criteria group decision-making (MCGDM).

Owing to decision makers (DMs) do not always show completely rational and may have the preference of bounded rational behavior, this may affect the result of the MCGDM. At the same time, criteria interaction is a focused issue in MCGDM. Hence, a novel TODIM method based on the generalized Choquet integral selects the best alternate using PUL evaluation, where the generalized Choquet integral is used to calculate the weight of criterion. The generalized PUL distance measure between two probabilistic uncertain linguistic elements (PULEs) is calculated and the perceived dominance degree matrices for each alternate relative to other alternates are obtained. Furthermore, the comprehensive perceived dominance degree of each alternate can be calculated to get the ranking.

Potential application of the PUL-TODIM method is demonstrated through an evaluation example with sensitivity and comparative analysis.

As per author's concern, there are no TODIM methods with probabilistic uncertain linguistic sets (PULTSs) to solve MCGDM problems under uncertainty. Compared with the result of existing methods, the final judgment value of alternates using the extended TODIM methodology is highly corroborated, which proves its potential in solving MCGDM problems under qualitative and quantitative environments.

The multiple factors of intelligence measurement are critical in intelligent science. The intelligence measurement is typically built as a model based on multiple factors. The different agent is generally difficult to measure because of the uncertainty between multiple factors. The purpose of this paper is to solve the problem of uncertainty between multiple factors and propose an effective method for universal intelligence measurement for the different agents.

In this paper, the authors propose a universal intelligence measurement method based on meta-analysis for crowd network. First, the authors get study data through keywords in the database and delete the low-quality data. Second, they compute the effect value by odds ratio, relative risk and risk difference. Then, they test the homogeneity by Q-test and analyze the bias by funnel plots. Third, they select the fixed effect and random effect as a statistical model. Finally, through the meta-analysis of time, complexity and reward, the weight of each factor in the intelligence measurement is obtained and then the meta measurement model is constructed.

This paper studies the relationship among time, complexity and reward through meta-analysis and effectively combines the measurement of heterogeneous agents such as human, machine, enterprise, government and institution.

This paper provides a universal intelligence measurement model for crowd network. And it can provide a theoretical basis for the research of crowd science.

The purpose of this paper is to present the adapted model per phases of the creative problem solving (CPS) process, where multi‐criteria decision making (MCDM) methods are used in the decision‐making phase. Also, to adapt and complete the steps of the six‐question technique, in order to establish the criteria's importance.

The framework procedure of MCDM, together with the Dialectical Systems Theory's guidelines when solving complex problems has already been introduced. The procedure was well‐verified in practice, but lacked the support of creative qualitative techniques in defining problems, and in generating and choosing alternatives. To eliminate this deficiency, in terms of prescriptive approach, the authors adapted the phases of the CPS process, where MCDM methods are used when choosing alternatives, and completed the steps of the six‐question technique to establish the criteria weights. The discrete Choquet integral was used to consider interactions among criteria.

The article shows that creative approaches are not limited to merely problem definitions and problem structuring. They can also be used in typically analytical steps in the framework procedure.

The completed and adapted phases of the CPS process can allow the mutual assistance of creative and decision‐making methods when solving problems – a step forward to holism.

This article develops and introduces the use of the six‐question technique, in the establishment of criteria weights.

The innovative aspect of this article is that it adapts and completes the CPS process so that MCDM methods can be used when choosing alternatives. It extends the use of creative approaches to typically analytical steps of MCDM, where synergies and redundancies among criteria are considered.

The purpose of this paper is to propose a model for a target searching problem in a two-dimensional region with time constraints. The proposed model facilitates the search operation by minimizing the mission time and fuel usage, and the search operation is performed by a set of agents divided into a number of groups.

The authors have applied optimization techniques, Cartesian product, inclusion–exclusion principle, cooperative strategy, Shapley value, fuzzy Shapley function and Choquet integral to model the problem.

The proposed technique optimizes the placement of base stations that minimizes the sortie length of the agents. The results show that the cooperative strategy outperforms the non-cooperative strategy. The Shapley values quantify the rewards of each group based on their contributions to the search operation, whereas the fuzzy Shapley values determine the rewards of each group based on their contributions and level of cooperation in the search operation.

The proposed model can be applied to model many real-time problems such as patrolling in international borders, urban areas, forests and managing rescue operations after natural calamities, etc. Therefore, defence organizations, police departments and other operation management sectors will be benefitted by applying the proposed approach.

To the best of the authors’ knowledge, determining the optimal locations of base stations in a region is not explored in the existing works on target searching problems with fuel constraints. The proposed approach to cooperatively search the targets in a region is new. Introducing the Shapley function and fuzzy Shapley function is a novel idea to quantify the rewards of each group based on their contributions and level of cooperation in the search operation. This paper addresses these unexplored areas.