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The 2-dimensional uncertain linguistic variable (2DULV) can depict decision-makers’ subjective assessments on the reliability of given evaluation results, which is a valid and practical tool to express decision information. In this study, we develop an improved MABAC method with 2DULVs to handle multiattribute group decision-making (MAGDM) problems where the weight information of attributes is unknown. First, some related theories of 2DULVs and the basic procedure of the MABAC method are briefly reviewed. Then, the maximum comprehensive evaluation value method is extended to 2DULVs to obtain combination weights of attributes, in which the subjective weights are determined according to the best–worst method (BWM) and the objective weights are calculated by the maximum deviation method. Besides, the generalized weighted average operator for 2DULVs (2DULGWA) is utilized to aggregate the evaluation information given by all experts. Finally, an improved MABAC for 2DULVs (2DUL-MABAC) is proposed, and an example is carried out to explain the validity of the proposed approach.

In this paper, the idea of the bipolar Pythagorean fuzzy sets (BPFSs) and its activities, which is a generalization of fuzzy sets, bipolar fuzzy sets (BFSs), intuitionistic fuzzy sets and bipolar intuitionistic fuzzy sets is proposed, with the goal that it can deal with dubious data all the more flexibly during the process of decision making. The key objective of this research paper has presented another variant of the Pythagorean fuzzy sets so called BPFSs. In bipolar Pythagorean fuzzy sets, membership degrees are satisfying the condition $$0 \le \left( {\mu_{p}^{ + } \left( x \right)} \right)^{2}$$ + $$\left( {v_{p}^{ + } \left( x \right)} \right)^{2} \le 1$$ and $$0 \le \left( {\mu_{p}^{ - } \left( x \right)} \right)^{2}$$ + $$\left( {v_{p}^{ - } \left( x \right)} \right)^{2} \le 1$$ instead of $$0 \le \left( {\mu_{p} \left( x \right)} \right)^{2}$$ + $$\left( {v_{p} \left( x \right)} \right)^{2} \le 1$$ as is in Pythagorean fuzzy sets and $$0 \le \mu_{p} \left( x \right)$$ + $$v_{p} \left( x \right) \le 1$$ as is in the intuitionistic fuzzy sets. Here, negative membership degree means the certain counter-property comparing to a bipolar Pythagorean fuzzy set. Also, the BPFSs weighted average operator and the BPFSs weighted geometric operator to aggregate the BPFSs is developed here. Further a multi attribute decision making technique is developed and the proposed aggregation operators are used. Finally, a numerical methodology for execution of the proposed system is introduced.

The objective of this paper is to develop Pythagorean fuzzy (PF) aggregation operators, utilizing the concept of power aggregation operators through Schweizer and Sklar (SS) operations. A series of aggregation operators, viz., PF SS power average operator, PF SS power weighted average operator, PF SS power geometric operator, and PF SS power weighted geometric operator under PF environment is proposed in this paper. The developed operators possess the capacity to make information aggregation technique more flexible than other existing operators due to the presence of SS t-norms and t-conorms in PF environment. Also, for the appearance of power aggregation operator, the developed operators contain the capability to eliminate effects of unreasonable data from biased decision makers by considering interrelationships among the fused arguments. Several properties of the proposed operators are studied and a method for solving multi-attribute decision-making problems under PF context is developed. To illustrate the proposed method and to show its efficiency, an example, studied previously, is solved and compared with existing methods.

In this paper, we define aggregation operators for trapezoidal cubic linguistic fuzzy sets which includes generalized cubic linguistic fuzzy averaging (geometric) operator, generalized trapezoidal cubic linguistic fuzzy weighted averaging (GTrCLFWA) operator, generalized trapezoidal cubic linguistic fuzzy weighted geometric (GTrCFWG) operator, generalized trapezoidal cubic linguistic fuzzy ordered weighted average (GTrCLFOWA) operator, generalized trapezoidal cubic linguistic fuzzy ordered weighted geometric (GTrCLFOWG) operator, generalized trapezoidal cubic linguistic fuzzy hybrid averaging (GTrCLFHA) operator and generalized trapezoidal cubic linguistic fuzzy hybrid geometric (GTrCLFHG) operator. Furthermore, we relate these aggregation operators to develop an approach to multiple attribute group decision-making with trapezoidal cubic linguistic fuzzy information. Finally, a numerical example is providing to demonstrate the submission of the established approach.

With the current high-tech development, the management of enterprises has gradually realized intelligent management. Especially for the management of enterprise employees, it is very necessary to realize artificial intelligence. There are many mistakes in the traditional employee management, which results in a waste of human resources. The artificial intelligence technology has exerted unprecedented advantages in employee management, but also has many challenges. The purpose of this article is to study the reconstruction of enterprise employee management paradigm in the era of artificial intelligence. This article first briefly describes the meaning of employee management paradigm, and then studies the impact of artificial intelligence technology on employee management paradigm. Then this article uses the fuzzy comprehensive evaluation method to establish a model for evaluating the basic qualities of business managers. The experimental part introduces the experimental process in combination with the quality evaluation index system. By evaluating the basic quality factors of the director of a state-owned enterprise, the quality factors of the factory director are calculated by using the weighted average operator. 0, the evaluation result is that the basic quality is relatively good.

Aiming at solving problem of multi-attribute decision making of q-rung orthopair fuzzy, the method based on Hamacher norm and improved MULTIMOORA is proposed. Firstly, q-rung orthopair fuzzy Hamacher weighted average operator and q-rung orthopair fuzzy Hamacher weighted geometric average operator are proposed, and their properties are studied. Secondly, the reference point method in the MULTIMOORA method only calculates the distance between each scheme and the positive ideal scheme, ignores the distance between each scheme and the negative ideal scheme, so an improved MULTIMOORA method was put forward, and then alternative from multiple perspectives is chosen and compared by using the improved MULTIMOORA method and two operators. Finally, through an example, this method is compared with other q-ROF decision-making methods, and the sensitivity of the algorithm is analyzed.

Hesitant fuzzy linguistic term set is defined in hesitant fuzzy linguistic decision frameworks in which all decision makers are agreed on the primary linguistic scale and its membership functions; however, this situation does not always happen, because words mean different things to different people or there are some linguistic term sets with different semantics for different people. Hence, normalization of linguistic information is necessary. Inspired by normalizing linguistic information based on 2-tuple linguistic model, in this paper, the concept of generalized hesitant fuzzy linguistic term set is proposed when decision makers are not agreed on the primary linguistic scale and its membership functions, which is an extension of hesitant fuzzy linguistic term set. Then, t-norms and t-conorms are utilized to define new operations on generalized hesitant fuzzy linguistic term sets and their properties are discussed. Based on these new operations, the likelihood-based comparison relation of generalized hesitant fuzzy linguistic term sets is presented, which is an extension of the likelihood-based comparison relation of hesitant fuzzy linguistic term sets. Accordingly, the generalized hesitant fuzzy linguistic weighted average operator, generalized hesitant fuzzy linguistic weighted geometric operator, generalized hesitant fuzzy linguistic ordered weighted average operator and generalized hesitant fuzzy linguistic ordered weighted geometric operator are provided to fuse generalized hesitant fuzzy linguistic term sets in linguistic decision making. A case study is used to illustrate the practicality of the method based on generalized hesitant fuzzy linguistic term sets and compare with Lee and Chen’s method, Rodriguez’s method and Wei’s method. It seems that the method based on generalized hesitant fuzzy linguistic term sets is flexible and useful method for hesitant fuzzy linguistic decision making.

In this paper, we introduce certain aggregation operators, namely, the m-polar fuzzy (mF) Hamacher weighted average operator, mF Hamacher ordered weighted average (mFHOWA) operator, mF Hamacher hybrid average (mFHHA) operator, mF Hamacher weighted geometric (mFHWG) operator, mF Hamacher weighted ordered geometric operator, and mF Hamacher hybrid geometric (mFHHG) operator. We discuss some properties of these operators, inclusive of their ability to implement both symmetric and asymmetric treatments of the items. We develop an algorithmic model to solve multi-attribute decision-making (MADM) problems in mF environment using mF Hamacher weighted average operator (mFHWA) and mFHWG operators. They can compensate for the possible asymmetric roles of the attributes that describe the problem. In the end, to prove the validity and feasibility of the proposed work, we give applications for selecting the most affected country regarding human trafficking, selecting health care waste treatment methods and selecting the best company for investment. We also solve practical MADM problems by using ELECTRE-I method, and give a comparative analysis.

In this paper, we utilize power aggregation operators to develop some Pythagorean fuzzy power aggregation operators: Pythagorean fuzzy power average operator, Pythagorean fuzzy power geometric operator, Pythagorean fuzzy power weighted average operator, Pythagorean fuzzy power weighted geometric operator, Pythagorean fuzzy power ordered weighted average operator, Pythagorean fuzzy power ordered weighted geometric operator, Pythagorean fuzzy power hybrid average operator, and Pythagorean fuzzy power hybrid geometric operator. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the Pythagorean fuzzy multiple attribute decision-making problems. Finally, a practical example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

In this paper, we have investigated the multiple attribute decision-making problems with bipolar fuzzy information. Motivated by the Hamacher operations, we have proposed bipolar fuzzy Hamacher weighted average operator, bipolar fuzzy Hamacher ordered weighted average operator, bipolar fuzzy Hamacher hybrid average operator, bipolar fuzzy Hamacher weighted geometric operator, bipolar fuzzy Hamacher ordered weighted geometric operator, bipolar fuzzy Hamacher hybrid geometric operator. We investigate the characteristics and special cases of these operators. Then, we have utilized these operators to develop some approaches to solve the bipolar fuzzy multiple attribute decision-making problems. Finally, a practical example for enterprise resource planning system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.

In this paper, we investigate the multiple attribute decision making problems based on the power aggregation operators with hesitant fuzzy uncertain linguistic information. Then, we have developed some power aggregation operators for aggregating hesitant fuzzy uncertain linguistic information: hesitant fuzzy uncertain linguistic power weighted average operator and hesitant fuzzy uncertain linguistic power weighted geometric operator. Then, we have utilized these operators to develop some approaches to solve the hesitant fuzzy uncertain linguistic multiple attribute decision making problems. Finally, a practical example for evaluating the teaching quality in higher education is given to verify the developed approach and to demonstrate its practicality and effectiveness.

This paper firstly analyzes the language fuzzy power weighted average operator based on the theory of language fuzzy sets and power average operators, and analyzes the drawbacks of LIFWPA operators under the influence of non-membership degree. Secondly proposes an improved continuous interval linguistic fuzzy weighted average operator according to the continuous interval Power average operator and the interval language ambiguity operation rules, and proves that the improved C-ILIFWPA operator is also idempotent, exchange invariant and boundedness. Then presents a new multi-attribute decision-making method using the C-ILIFWPA operator, Hamming distance and other related theories. Finally this paper proves the multi-attribute decision-making method is effectiveness and feasibility through examples and comparative analysis.

© 2019 by the authors. In this paper, we consider multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of intuitionistic uncertain linguistic variables. Based on Hamacher operations, we developed several Hamacher aggregation operators, which generalize the arithmetic aggregation operators and geometric aggregation operators, and extend the algebraic aggregation operators and Einstein aggregation operators. A number of special cases for the two operators with respect to the parameters are discussed in detail. Also, we developed an intuitionistic uncertain linguistic generalized Hamacher hybrid weighted average operator to reflect the importance degrees of both the given intuitionistic uncertain linguistic variables and their ordered positions. Based on the generalized Hamacher aggregation operator, we propose a method for MAGDM for intuitionistic uncertain linguistic sets. Finally, a numerical example and comparative analysis with related decision making methods are provided to illustrate the practicality and feasibility of the proposed method.

To solve the problem of strong subjectivity and less objectivity of decision-making in distribution network planning scheme caused by qualitative evaluation indices, a model was proposed which combines parabolic ordered weighted average operator (P-OWA) with entropy weight and grey relational analysis (EW-GRA). Firstly, the expert scoring of qualitative indices were reasonably assembled by a P-OWA operator. The whole indices using an entropy weight method. The correlation matrix was obtained according to a grey relation analysis. The grey correlation degree was obtained by weighting. Finally, the optimal scheme was obtained according to the result of ranking. By applying this method to an actual case, its effectiveness was verified by comparing with other methods.

Neutrosophic cubic sets (NCSs) can express complex multi-attribute decision-making (MADM) problems with its interval and single-valued neutrosophic numbers simultaneously. The weighted arithmetic average (WAA) and geometric average (WGA) operators are common aggregation operators for handling MADM problems. However, the neutrosophic cubic weighted arithmetic average (NCWAA) and neutrosophic cubic geometric weighted average (NCWGA) operators may result in some unreasonable aggregated values in some cases. In order to overcome the drawbacks of the NCWAA and NCWGA, this paper developed a new neutrosophic cubic hybrid weighted arithmetic and geometric aggregation (NCHWAGA) operator and investigates its suitability and effectiveness. Then, we established a MADM method based on the NCHWAGA operator. Finally, a MADM problem with neutrosophic cubic information was provided to illustrate the application and effectiveness of the proposed method.

Simplified neutrosophic sets (SNSs) can effectively solve the uncertainty problems, especially those involving the indeterminate and inconsistent information. Considering the advantages of SNSs, a new approach for multi-criteria decision-making (MCDM) problems is developed under the simplified neutrosophic environment. First, the prioritized weighted average operator and prioritized weighted geometric operator for simplified neutrosophic numbers (SNNs) are defined, and the related theorems are also proved. Then two novel effective cross-entropy measures for SNSs are proposed, and their properties are proved as well. Furthermore, based on the proposed prioritized aggregation operators and cross-entropy measures, the ranking methods for SNSs are established in order to solve MCDM problems. Finally, a practical MCDM example for coping with supplier selection of an automotive company is used to demonstrate the effectiveness of the developed methods. Moreover, the same example-based comparison analysis of between the proposed methods and other existing methods is carried out.

With respect to dynamic multi-attribute group decision-making (DMAGDM) problems, where attribute values take the form of intuitionistic fuzzy values (IFVs) and the weights (including expert, attribute and time weights) are unknown, the dynamic intuitionistic fuzzy power geometric weighted average (DIFPGWA) operator and the improved IFVs’ GM(1,1) prediction model (IFVs-GM(1,1)-PM) are proposed. First, the concept of IFVs, the operational rules, the distance between IFVs, and the comparing method of IFVs are defined. Second, the DIFPGWA operator and the improved IFVs-GM(1,1)-PM are defined in detail. Third, corresponding decision-making (D-M) steps are proposed. Three kinds of weights are given by the proposed determination method. Finally, an example is given to prove the effectiveness and superiority of the proposed decision-making method.

In recent decades, innovation has been recognized as one of the main sources of competitive advantage for organizations at an international level. Recently, numerous studies have been developed, mainly focused on the analyses of innovation processes in large companies, however, little has been done in the identification of innovative capabilities that allow small and medium-sized companies to compete in highly uncertain environments. This study presents the interpreted results of an innovation diagnosis utilizing the Experton method and the application of the generalized ordered weighted average operator (GOWA) for the treatment of information. The focus relies on seven organizational areas of small and medium-sized manufacturing companies. The applied questionnaire presents 32 exploration and 5 control questions applied to a total of 217 small and medium-sized companies in the Latin American city of Morelia, Mexico. The present work seeks to shed light in the diagnosis of innovation capabilities in manufacturing companies with the aim of fostering synergies and effectively allocate resources for the promotion of innovation management as a source of competitiveness and economic growth.

The Multiple Criteria Decision Aiding (MCDA) has been a fast growing area of operations research and management science during the last decades, while the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) is one of the well-known MCDA methods with ranking the alternatives from the best to the worst. In this paper, we propose a new outranking method called Prioritized PROMETHEE (P-PROMETHEE) method, which is used to deal with the ranking problem of Prioritized Multiple Criteria Decision Making (PMCDM). First, we use the idea of entropy to get the information of each criterion in the same prioritized level, which is used to get the weight of each criterion. Then, we use the weighted average operator to aggregate all the criteria in the same prioritized level. Moreover, we apply Yager's prioritized measure-guided aggregation operator to the classical PROMETHEE method to get the P-PROMETHEE method. Finally, we give a practical example to illustrate the effectiveness of the P-PROMETHEE method.

With respect to the multiple attribute decision making problems in which the attribute values take the form of the 2-dimension uncertain linguistic information, a new method based on the generalized hybrid operators is proposed. Firstly, the definition, properties, expectations and ranking method of 2-dimension uncertain linguistic information are introduced, and the operational laws the 2-dimension uncertain linguistic information are defined. Then some aggregation operators, including 2-dimension uncertain linguistic generalized weighted average operator, 2-dimension uncertain linguistic generalized ordered weighted average operator, and 2-dimension uncertain linguistic generalized hybrid weighted average operator, are developed, and some properties and special cases of them are also discussed. Finally, these operators are applied to multi-criteria decision making and an illustrative example is given to verify the developed approach and to demonstrate its effectiveness.

In this paper, we study the group decision making (GDM) problem with two-tuple linguistic information .we propose a way to calculate the comprehensive assessment information. Then average preference degree of one alternative over all the other alternatives is calculated. Thus, the ranking of alternatives is obtained by the comparison of linguistic two-tuple information.

In this paper, we develop a new linguistic aggregation operator based on the cloud model for solving linguistic group decision-making problem. First, an improved generating cloud method is proposed so as to transform linguistic variables into clouds, which modifies the limitation of the classical generating cloud method. We then address some new cloud algorithms, such as cloud possibility degree and cloud support degree which can be respectively used to compare clouds and determine the weights. Combining the cloud support degree with power aggregation operator, we develop a new cloud aggregation operator dubbed the cloud generalized power ordered weighted average (CGPOWA) operator. We study the properties of the CGPOWA operator and investigate its family including a wide range of aggregation operators such as the CGPA operator, CPOWA operator, CPOWGA operator, CPWQA operator, CWAA and CWGA operator. Furthermore, a new approach for linguistic group decision-making is presented on the basis of the improved generating cloud method and CGPOWA operator. Finally, an illustrative example is provided to examine the effectiveness and validity of the proposed approach.

In order to achieve the quantitative health diagnosis of operational tunnel, established the health diagnosis system and the evaluating criterion based on the mechanism of tunnel disease, used the weighted average operator to obtain comprehensive evaluation results, applied the asymmetric proximity to decide the grade of tunnel structure health, thus build a complete fuzzy comprehensive evaluation model. Finally, by taking the example of Lilong tunnel located at LiuHe Express way, and made analysis and evaluation to its structure health status. The results show that the improved grey relevancy method to determine the index weight can avoid the subjective arbitrariness and improve the reliability of the evaluation model, making a decision and analysis by asymmetric proximity insures the diagnosis results more correspond with the objective reality.

The ranking of interval-valued intuitionistic fuzzy sets (IVIFSs) is very important for the interval-valued intuitionistic fuzzy decision making. From the probability viewpoint, the possibility degree of comparison between two interval-valued intuitionistic fuzzy numbers (IVIFNs) is defined by using the notion of 2-dimensional random vector, and a new method is then developed to rank IVIFNs. Hereby the ordered weighted average operator and hybrid weighted average operator for IVIFNs are defined based on the Karnik-Mendel algorithms and employed to solve multi-attribute group decision making problems with IVIFNs. The individual overall attribute values of alternatives are obtained by using the weighted average operator for IVIFNs. By using the hybrid weighted average operator for IVIFNs, we can obtain the collective overall attribute values of alternatives, which are used to rank the alternatives. A numerical example is examined to illustrate the effectiveness and flexibility of the proposed method in this paper.

We present the induced intuitionistic fuzzy ordered weighted averaging-weighted average (I-IFOWAWA) operator. It is a new aggregation operator that uses the intuitionistic fuzzy weighted average (IFWA) and the induced intuitionistic fuzzy ordered weighted averaging (I-IFOWA) operator in the same formulation. We study some of its main properties and we have seen that it has a lot of particular cases such as the IFWA and the intuitionistic fuzzy ordered weighted averaging (IFOWA) operator. We also study its applicability in a decision-making problem concerning strategic selection of investments. We see that depending on the particular type of I-IFOWAWA operator used, the results may lead to different decisions.

Linguistic neutrosophic numbers (LNNs) include single-value neutrosophic numbers and linguistic variable numbers, which have been proposed by Fang and Ye. In this paper, we define the linguistic neutrosophic number Einstein sum, linguistic neutrosophic number Einstein product, and linguistic neutrosophic number Einstein exponentiation operations based on the Einstein operation. Then, we analyze some of the relationships between these operations. For LNN aggregation problems, we put forward two kinds of LNN aggregation operators, one is the LNN Einstein weighted average operator and the other is the LNN Einstein geometry (LNNEWG) operator. Then we present a method for solving decision-making problems based on LNNEWA and LNNEWG operators in the linguistic neutrosophic environment. Finally, we apply an example to verify the feasibility of these two methods.

This study presents an innovative approach to develop a course specification that considers the effect of the covered topics in a course on the entire program learning outcomes. This study presents a practical methodology enabling instructors to design an efficient course specification based on the national academic accreditation and assessment standards of the Kingdom of Saudi Arabia. Fuzzy linguistic variables are aggregated using the induced unbalanced linguistic ordered weighted average operator.

Despite the growing trend in intelligent transportation systems applications, there are still many problems waiting for an accurate solution such as traffic flow forecasting. In this paper, based on real-time data provided by dual loop speed traps detectors at given slot of time, we propose a cloud-based data collection method which is aimed to improve prediction accuracy. To reach this level of accuracy, two traffic parameters were introduced, the average speed and the foreseen arrival time between two vehicles. By adopting Choquet integral operator, these parameters can subsequently aggregate to busiest traffic parameters. Afterwards, a simple linear regression is applied for a dual purpose, the first to predict the traffic flow, then prove that there is a relationship between derived busiest arrival time and the traffic flow. Moreover, the simulation charts demonstrates that the forecasts by the Choquet operator ensure an accurate results to the real-time data. In contrast, the forecasts using weighted average operator lead to low accuracy compared with real-time data.

In this paper, we introduce concept of bipolar neutrosophic set and its some operations. Also, we propose score, certainty and accuracy functions to compare the bipolar neutrosophic sets. Then, we develop the bipolar neutrosophic weighted average operator and bipolar neutrosophic weighted geometric operat\"or to aggregate the bipolar neutrosophic information. Furthermore, based on the neutrosophic weighted geometric(aritmetic) operat\"or and the score, certainty and accuracy functions, we develop a bipolar neutrosophic multiple criteria decision-making approach, in which the evaluation values of alternatives on the attributes take the form of bipolar neutrosophic numbers to select the most desirable one(s). Finally, a numerical example of the method was given to demonstrate the application and effectiveness of the developed method., Comment: Thanks

The hesitant fuzzy linguistic term set (HFLTS) was originally presented to tackle the situation where the experts are hesitant between several linguistic terms to assess a linguistic variable. One basic problem in multiple attribute decision making (MADM) with HFLTSs is the aggregation of HFLTSs. This paper aims at providing a possibility distribution based approach to carry out the aggregation process. The concept of possibility distribution for an HFLTS is first introduced which is a natural way to deal with the possible information contained in an HFLTS. Two kinds of aggregation operators are presented which are extensions of the weighted average operator and the ordered weighted average operator. A procedure to solve MADM with HFLTSs is proposed by use of the presented aggregation operators. A numerical example is given to verify the proposed approach.

When using the ordered weighted average operator, it can happen that one wants to optimize the variability (measured by the entropy (maximal) or by the variance (minimal)) of the weights while keeping the orness of this operator at a fixed level. This has been considered by several authors. Dually, there might be some contexts where one wishes to maximize the orness while guaranteeing some fixed variability. In this paper, we present two algorithms for finding such weights, when the variability is captured by the entropy and by the variance.

In this paper, a hesitant fuzzy multiple attribute group decision making problem where there exists a prioritization relationship over the attributes and decision makers is studied. First, the generalized hesitant fuzzy prioritized Einstein weighted average operator is proposed. Then, some special cases and their desirable properties are investigated. Furthermore, the procedure of group decision making based on the proposed operators is given under hesitant fuzzy environment. Finally, a practical example is provided to illustrate the developed approach.

The ordered weighted average (OWA) is an aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper studies the use of the OWA operator with norms. Several extensions and generalizations are suggested including the use of the induced OWA operator and the OWA weighted average. This approach represents a general framework of the aggregation operators when dealing with distance and similarity measures. Some key particular cases are studied including the addition OWA and the subtraction OWA operator.

At present, people are in the pursuit of household intelligent control, at the same time, they start to focus on the comfort and energy saving in household environment. This paper based on the FIPA ontology standard, a kind of intelligent household energy saving model is established, the system of various kinds of agent module are analyzed, and a concrete plan to design energy control is provided. By introducing ordered weighted average operator (OWA), the problem that the system can maximize the customer comfort with minimum power consumption is solved.

Recently research on the evaluation of pipeline leak detection systems has been developing due to the widespread of natural gas supply through pipeline transportation in urban areas. This paper presents an improved fuzzy comprehensive evaluation method for assessing the performance of leak detection systems. In this method, the weights of different indexes are calculated by combining entropy weight with AHP weight. Weighted average operator is introduced to replace the fuzzy operator in traditional evaluation models. And interval evaluation according to comment rank is also adopted for final evaluation results. Simulation experiments verify the effectiveness of new approach. Compared with other common evaluation methods, new approach could effectively enhance evaluation resolution, satisfying the requirement of pipeline leak detection application. © 2012 IEEE.

The combination forecast method based on inducted ordered weighted average operator is used widely in the recent years. The difference between this model and the traditional combination forecast method is that the empowerment coefficient of the combination forecast has nothing to do with the monomial prediction model, but it is closely related to forecast precision's size of the monomial prediction model at each time-point, it is an invariable empowerment method. First of all, this paper adopts the time series tendency forecast model and the gray system GM (1, 1) forecast model in order to analysis the tax revenue of the Hebei Province, secondly, it introduces the tax revenue combination forecast model based on the IOWA operator, Empirical analysis indicates that this model can enhance the forecast precision of the combination and it is an effective combination forecast method.

We propose a complete characterization of a certain class of aggregation-invariant dissimilarities between row (or column) profiles. This class (for which row and column dispersions coincide) contains the chi-square, ratio, Kullback-Leibler, Hellinger, Cressie-Read dissimilarities, as well as a presumably new “type s” class of dissimilarities. Distinguishing between two forms of Huygens’ principle from Classical Mechanics, we show “type s” dissimilarities to satisfy the weak Huygens’ principle; the strong Huygens’ principle however holds for a single member of the class, namely the chi-square dissimilarity. Extending the concept of dissimilarity to “type s” divergences restores the strong principle.

Energy is the essential material for human survival, and it is the important resource for economic development and social progress. Making a reasonable and reliable development plan by forecasting the energy consumption scientifically and effectively can ensure economic healthily and orderly developing. The combination forecast method based on inducted ordered weighted average operator is used widely in these years. The difference between this model and the traditional combination forecast method is that the empowerment coefficient of the combination forecast has nothing to do with the monomial prediction model, but it is closely related to forecast precision's size of the monomial prediction model at each time-point, it is an invariable empowerment method. In this paper we adopt the time series tendency forecast model and the gray system GM (1, 1) forecast model in order to analysis the energy consumption of the Hebei Province; then, it introduces the energy consumption combination forecast model based on the IOWA operator. Empirical analysis indicates that this model can enhance the forecast precision of the combination and it is an effective combination forecast method.