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The purpose of this article is to present a novel biobjective biform game framework for optimizing strategy selection of nodes (agents) in bilateral link network formation under dual objectives of profit and link security. First, in the first part of the noncooperative game, we construct various possible strategy combinations (situations) of all players (nodes) in the network, which define the competitive environment in the second part of the cooperative game. Second, in the second part, we derive coalitions’ payoffs under the two objectives of profit and link security and propose the biobjective Banzhaf values to allocate the corresponding cooperative games in all strategy situations. Then, a biobjective noncooperative game is formed on the basis of the obtained biobjective Banzhaf values. Third, we redefine Pareto–Nash equilibriums (in the sense of pure strategies) and obtain the Pareto–Nash equilibriums of the biobjective noncooperative game in the first part. Subsequently, the choice of the obtained Pareto–Nash equilibriums is regarded as a biobjective group decision-making problem, which is solved by employing the redefined Borda choice function method with objective weights. Hence, we can obtain the optimal strategy choices of all the nodes (players) for the entire bilateral link network formation. Finally, numerical examples and comparative analysis are given to verify the validity and applicability of the proposed models and methods. [ABSTRACT FROM AUTHOR]

Then, a series of group consensus cost robust optimization models are constructed under asymmetric unit adjustment cost constraints and the aforesaid four uncertainty sets. Sensitivity analysis shows that the proposed group consensus cost robust optimization models are superior to the existing minimum cost consensus models. Finally, data-driven group consensus cost robust optimization models are developed based on the uncertainty set of asymmetric unit adjustment costs calibrated by using the pricing information of the new product MACUBE 550 in the DEEPCOOL company. [Extracted from the article]

The interval complex single-valued neutrosophic fuzzy set (ICSVNFS) and hesitant fuzzy set (HFS) are two important different generalizations of the fuzzy set (FS) to cope with unreliable and unpredictable information in the real world. The ICSVNHFS is characterized by interval complex-valued membership, interval complex-valued abstinence, and interval complex-valued non-membership grades, whose ranges are restricted to a unit disc in a complex plane instead of real numbers. In this paper, the notions of interval-valued complex single-valued neutrosophic hesitant fuzzy sets (IVCSVNHFSs) are initiated and also their operational laws are described with examples. Further, based on IVCSVNHFSs, we develop the notions of the interval-valued complex single-valued neutrosophic fuzzy generalized weighted (IVCSVNHFGW) operator, interval-valued complex single-valued neutrosophic fuzzy generalized ordered weighted (IVCSVNHFGOW) operator, and interval-valued complex single-valued neutrosophic fuzzy generalized hybrid weighted (IVCSVNHFGHW) operator to cope with decision information and also study their properties. Further, a new multi-attribute group decision making (MAGDM) problem is initiated based on the proposed operators. Finally, we provide some numerical examples to illustrate the reliability and superiority of the proposed methods by comparison with other existing methods. [ABSTRACT FROM AUTHOR]

With the development of modern property service industry, the property perceived service quality (PPSQ) evaluation data is characterized by multiple evaluation subjects, complicated data structure and large scale data. Since the traditional decision-making methods are difficult to solve the above similar problems, this paper proposes a large group decision-making (LGDM) method of generalized multi-attribute and multi-scale (MAMS) based on the linear programming technique for multidimensional analysis of preference (LINMAP). In this method, the large-scale heterogeneous data of expert preference and user evaluation is fused. The decision matrix of generalized MAMS is used to process user evaluation information. The positive ideal solution (PIS) and the attribute weights are determined by the LINMAP model. The comprehensive evaluation values are calculated and hereby the alternatives are ranked order. According to the relation between attribute weights and preset values, a mechanism for identifying invalid data is designed. This paper analyzes a set of survey data of PPSQ for the four public construction projects in the same city. The analysis results show the validity and rationality of the proposed method, and develop the property service evaluation theory. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a method for solving multi-attribute group decision making (MAGDM)problems with intuitionistic uncertain 2-tuple linguistic (IU2TL) variables (IU2TLVs). Firstly, operational laws and similarity degrees of IU2TLVs are introduced. Secondly, a new aggregation operator called IU2TL continuous chi-deviation ordered weighted quasi-averaging (IU2TLC-CDOWQA) is proposed. Then, to deal with heterogeneous relationships, the IU2TL continuous extend Bonferroni mean (IU2TLC-EBM) operator is developed, and hereby a method for MAGDM problems with IU2TLVs is proposed. Finally, an illustrated example and comparing analysis show that the proposed method is practical and effective. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy sets (IFSs) and interval-valued intuitionistic fuzzy sets (IVIFSs) are flexible to deal with the vague and/or imprecise information. Thus, many multi-attribute group decision making (MAGDM) problems are modeled by IFSs or IVIFSs. The comparison of two IVIFSs is still a hot topic, and thereby this paper proposes a new ranking function of IVIFSs, which takes into the amount and the reliability information of an IVIFS and combines the advantages of TOPSIS. Based on the new ranking function, we establish an optimization model to determine the attribute weights when they are unknown and partially known. Moreover, we develop an effective method for solving MAGDM problems in which the attribute values are expressed with IVIFSs. A numerical example of supplier selection problem is examined to demonstrate applicability and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a new Atanassov's intuitionistic fuzzy (A-IF) programming method to solve heterogeneous multiattribute group decision-making problems with A-IF truth degrees in which there are several types of attribute values such as A-IF sets (A-IFSs), trapezoidal fuzzy numbers, intervals, and real numbers. In this method, preference relations in comparisons of alternatives with hesitancy degrees are expressed by A-IFSs. Hereby, A-IF group consistency and inconsistency indices are defined on the basis of preference relations between alternatives. To estimate the fuzzy ideal solution (IS) and weights, a new A-IF programming model is constructed on the concept that the A-IF group inconsistency index should be minimized and must be not larger than the A-IF group consistency index by some fixed A-IFS. An effective method is developed to solve the new derived model. The distances of the alternatives to the fuzzy IS are calculated to determine their ranking order. Moreover, some generalizations or specializations of the derived model are discussed. Applicability of the proposed methodology is illustrated with a real supplier selection example. [ABSTRACT FROM PUBLISHER]

Abstract: Constrained matrix games with payoffs of triangular fuzzy numbers (TFNs) are a type of matrix games with payoffs expressed by TFNs and sets of players’ strategies which are constrained. So far as we know, no study have yet been attempted for constrained matrix games with payoffs of TFNs since there is no effective way to simultaneously incorporate the payoffs’ fuzziness and strategies’ constraints into classical and/or fuzzy matrix game methods. The aim of this paper is to develop an effective methodology for solving constrained matrix games with payoffs of TFNs. In this methodology, we introduce the concepts of Alpha-constrained matrix games for constrained matrix games with payoffs of TFNs and the values. By the duality theorem of linear programming, it is proven that players’ gain-floor and loss-ceiling always have a common interval-type value and hereby any Alpha-constrained matrix game has an interval-type value. Moreover, using the representation theorem for the fuzzy set, it is proven that any constrained matrix game with payoffs of TFNs always has a TFN-type fuzzy value. The auxiliary linear programming models are derived to compute the lower and upper bounds of the interval-type value and optimal strategies of players for any Alpha-constrained matrix game. In particular, the mean and the lower and upper limits of the TFN-type fuzzy value of any constrained matrix game with payoffs of TFNs can be directly obtained through solving the derived three linear programming models with data taken from only 1-cut and 0-cut of payoffs. Hereby the TFN-type fuzzy value of any constrained matrix game with payoffs of TFNs are easily computed. The proposed method in this paper is compared with other methods and its validity and applicability are illustrated with a numerical example. [Copyright &y& Elsevier]

The aim of this paper is to develop a new methodology for solving group decision making problems in which preference comparisons between alternatives are expressed with Atanassov's intuitionistic fuzzy (IF) preference relations. In this methodology, the generalized ordered weighted averaging (GOWA) operator is extended to develop the Atanassov's IF set (IFS) generalized ordered weighted averaging (IFSGOWA) operator, which can aggregate vague or imprecise information expressed with the Atanassov's IFSs. The Atanassov's IFSGOWA operator based methodology is further developed to solve group decision making problems with Atanassov's IF preference relations. A real example of the agroecological region assessment problem is used to illustrate the implementation and applicability of the proposed methodology. It is also shown that the obtained decision results could be affected by the choice of the parameter lambda and the nature of the weights. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a new methodology for solving fuzzy multi-attribute group decision making problems with non-homogeneous information, including multi-granular linguistic term sets, fuzzy numbers, interval values and real numbers. In this methodology, different distances are defined to measure differences between alternatives and the ideal solution as well as the negative ideal solution. A relative closeness method is developed by introducing the multi-attribute ranking index based on the particular measure of closeness to the IS. The proposed method determines a compromise solution for the group, providing a maximum “group utility” for the “majority” and a minimum of an individual regret for the “opponent”. The implementation process, effectiveness and feasibility of the method proposed in this paper are illustrated with a real example of the missile weapon system design project selection. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to study how to solve a type of matrix games with payoffs of triangular fuzzy numbers. In this paper, the value of a matrix game with payoffs of triangular fuzzy numbers has been considered as a variable of the triangular fuzzy number. First, based on two auxiliary linear programming models of a classical matrix game and the operations of triangular fuzzy numbers, fuzzy optimization problems are established for two players. Then, based on the order relation of triangular fuzzy numbers the fuzzy optimization problems for players are decomposed into three-objective linear programming models. Finally, using the lexicographic method maximin and minimax strategies for players and the fuzzy value of the matrix game with payoffs of triangular fuzzy numbers can be obtained through solving two corresponding auxiliary linear programming problems, which are easily computed using the existing Simplex method for the linear programming problem. It has been shown that the models proposed in this paper extend the classical matrix game models. A numerical example is provided to illustrate the methodology. [ABSTRACT FROM AUTHOR]

The current study aims to present a new method called Ordinal Priority Approach (OPA) in Multiple Attribute Decision-Making (MADM). This method can be used in individual or group decision-making (GDM). In the case of GDM, through this method, we first determine the experts and their priorities. The priority of experts may be determined based on their experience and/or knowledge. After prioritization of the experts, the attributes are prioritized by each expert. Meanwhile, each expert ranks the alternatives based on each attribute, and the sub-attributes if any. Ultimately, by solving the presented linear programming model of this method, the weights of the attributes, alternatives, experts, and sub-attributes would be obtained simultaneously. A significant advantage of the proposed method is that it does not make use of pairwise comparison matrix, decision-making matrix (no need for numerical input), normalization methods, averaging methods for aggregating the opinions of experts (in GDM) and linguistic variables. Another advantage of this method is the possibility for experts to only comment on the attributes and alternatives for which they have sufficient knowledge and experience. The validity of the proposed model has been evaluated using several group and individual instances. Finally, the proposed method has been compared with other methods such as AHP, BWM, TOPSIS, VIKOR, PROMETHEE and QUALIFLEX. Based on comparisons among the weights and ranks using Spearman and Pearson correlation coefficients, the proposed method has an applicable performance compared with other methods. • The study proposes a new Ordinal Priority Approach (OPA) to solve Multiple Attribute Decision-Making (MADM) problems. • The proposed method can calculate the weights and the ranks of the experts, alternatives, and attributes simultaneously using simple steps. • The proposed method supports group decision making without using averaging methods. • The proposed method needs a simple comparison of experts, alternatives, and attributes and supports decision making with incomplete input data. [ABSTRACT FROM AUTHOR]