Abstract
The aim of this paper is to investigate information aggregation methods under Pythagorean trapezoidal fuzzy environment. Some Einstein operational laws on Pythagorean trapezoidal fuzzy numbers are defined based on Einstein sum and Einstein product. Based on Einstein operations we define Pythagorean trapezoidal fuzzy Einstein weighted geometric (PTFEWG) operator, Pythagorean trapezoidal fuzzy Einstein ordered weighted geometric (PTFEOWG) operator and Pythagorean trapezoidal fuzzy Einstein hybrid geometric (PTFEHG) operator. Furthermore, we apply the proposed aggregation operators to deal with multiple attribute group decision making. In the last we construct a numerical example for multiple attribute group decision making problem and compare the result with existing method.
Keywords
Introduction
The idea of fuzzy set theory was developed by L.A. Zadeh [2] in 1965. In fuzzy set theory the degree of membership function was discussed. Fuzzy set theory has been studied in various fields such that, medical diagnosis, computer science, fuzzy algebra and decision making problems. Later on the great idea of intuitionistic fuzzy set (IFS) theory was developed by Atanassov [1] in 1986, who discussed the degree of membership and non-membership function. IFS [2] is the generalization of fuzzy set theory. There are many advantage of IFS theory such as, usage in engineering, management science and computer science. As for aggregation methods for IFSs information, Xu [4] Xu and Yager [3], Wei [9] and Liang [8] developed different IFSs aggregation operators, respectively, such as intuitionistic fuzzy weighted averaging (IFWA) operator, intuitionistic fuzzy ordered weighted averaging (IFOWA) operator, intuitionistic fuzzy hybrid aggregation (IFHA) operator, intuitionistic fuzzy weighted geometric (IFWG) operator, intuitionistic fuzzy ordered weighted geometric (IFOWG) operator, intuitionistic fuzzy hybrid geometric (IFHG) operator. Atanassov and Gargov [10] further extended the concept of intuitionistic fuzzy sets to introduced interval-valued intuitionistic fuzzy sets (IVIFSs), which enhances greatly the representation ability of uncertainty than IFSs. However, the domain of intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets which are used to indicate the certain criterion does or does not belong to some fuzzy concepts [11]. Ban et al [29, 30] developed the idea of trapezoidal fuzzy numbers and interval-valued trapezoidal fuzzy numbers. Later on Wang and Zhang [12] developed the definition of intuitionistic trapezoidal fuzzy numbers (ITFNs) and interval-valued intuitionistic trapezoidal fuzzy numbers (IVITFNs). As compared to intuitionistic fuzzy numbers, the intuitionistic trapezoidal fuzzy numbers make their membership and non-membership degrees no longer relative to a fuzzy concept Excellent or Good, but relative to the trapezoidal fuzzy number; thus can express decision information in different dimensions and avoid losing decision preference information [11]. Correspondingly on aggregation methods, Wang and Zhang [12] first proposed the definition and Hamming distance formula for intuitionistic trapezoidal fuzzy numbers. Moreover, they developed the intuitionistic trapezoidal fuzzy weighted averaging (ITFWA) operator and the multi-criteria decision making method with incomplete certain information. Blanco et al. [16] discussed a bibliometric approach which show citations and the h-index. Moreover, it also uses the VOS viewer software in order to map the main trends in this area. The work considers the leading journals, articles, authors and institutions. Merigo et al. [17] presented a general overview of research in the fuzzy sciences using bibliometric indicators. The main advantage is that these indicators provide a general picture, identifying some of the most influential research in this area. The analysis is divided into key sections focused on relevant journals, papers, authors, institutions and countries. Zhen et al. [34] proposed a novel computational model based on the use of extended linguistic hierarchies, which not only can be used to operate with multigranular linguistic distribution assessments but also can provide interpretable linguistic results to decision makers. Jian et al. [35] developed the isomorphic multiplicative transitivity for IFPRs and IVFPRs, which builds the substantial relationship between hesitation and uncertainty in MCDM. Jian et al. [36] developed three levels of consensus degree with distributed linguistic trust functions are calculated. Then, a novel feedback mechanism is activated to generate recommendation advices for the inconsistent users to increase the group consensus degree. Its novelty is that it produces the boundary feedback parameter based on the minimum adjustment cost optimisation model. Jain et al. [7] developed three levels of consensus degree are defined and used to identify the inconsistent experts. A trust based recommendation mechanism is developed to generate advices according to individual trust relationship, making recommendations more likeable to be implemented by the inconsistent experts to achieve higher levels of consensus. Yujia Liu et al. [37] discussed the inconsistency problem in group decision making caused by disparate opinions of multiple experts. To do so, a trust induced recommendation mechanism is investigated to generate personalised advices for the inconsistent experts to reach higher consensus level. Zaiwu Gong, et al. [38] explored the case when an individual opinion is interval preference in consensus decision making. And constructed two multi-objective optimization models: one based on the minimum cost from the perspective of the moderator, the other the maximum return from the perspective of the individuals. On the basis of multi-objective programming theories, these multi objective programming models are then transformed into two single-objective linear programming models.
Wan and Dong [13] defined the expectation and expectant score of intuitionistic trapezoidal fuzzy numbers from the geometric angle. Wu [14] further developed the intuitionistic trapezoidal fuzzy weighted geometric (ITFWG) operator, the intuitionistic trapezoidal fuzzy ordered weighted geometric (ITFOWG) operator, the induced intuitionistic trapezoidal fuzzy ordered weighted geometric (I-ITFOWG) operator and the intuitionistic trapezoidal fuzzy hybrid geometric (ITFHG) operator. But it must be noticed that the above aggregation operators are all based on the most commonly used algebraic product and algebraic sum of ITFNs for carrying the combination process, which are not the only operational laws that can be chosen to model the intersection and union on ITFNs, and it is well known that Einstein t-norms and Einstein t-conorms are two ideal examples of the class of strict Archimedean t-norms and t-conorms [15]. Moreover, in literatures, there is still little research on aggregation operators using the Einstein operations for aggregating a collection of IFVs. Such as, Wang and Liu [16, 17] brought forward the intuitionistic fuzzy Einstein weighted geometric (IFEWG) operator, the intuitionistic fuzzy Einstein ordered weighted geometric (IFEOWG) operator, the intuitionistic fuzzy Einstein weighted averaging (IFEWA) operator and the intuitionistic fuzzy Einstein ordered weighted averaging (IFEOWA) operator successively. Zhao and Wei [18] developed the intuitionistic fuzzy Einstein hybrid averaging (IFEHA) operator and intuitionistic fuzzy Einstein hybrid geometric (IFEHG) operator. Zhang and Yu [19] proposed the Einstein based intuitionistic fuzzy Choquet geometric (EIFCG) operator and Einstein based interval-valued intuitionistic fuzzy choquet geometric (EIVIFCG) operator. Therefore, in the light of references [16, 17], the aim of this paper is to generalized and advanced the tool of intuitionistic trapezoidal fuzzy theory by investigating information aggregation methods utilizing Einstein t-conorm and t-norm.
In 2013, Yager [21,22, 21,22] developed Pythagorean fuzzy set (PFS) characterized by a membership degree and non-membership degree, which satisfies the condition that the square sum of its membership degree and non-membership degree is less than or equal to 1. Yager [32] gave an example to state this situation, a DM gives his support for membership of an alternative as
Shakeel et al. [26] developed some aggregation operators to use the decision information represented by PTFNS, including the Pythagorean trapezoidal fuzzy weighted averaging (PTFWA) operator, Pythagorean trapezoidal fuzzy ordered weighted averaging (PTFOWA) operator and Pythagorean trapezoidal fuzzy hybrid averaging (PTFHA) operator. Shakeel et al. [27] extended the work of aggregation operators into interval-valued Pythagorean trapezoidal fuzzy weighted averaging (IVPTFWA) operator, ordered weighted, hybrid averaging operators such as, IVPTFWA, IVPTFOWA, IVPTFHA operator. Shakeel et al. [28] also introduced the idea of induced interval-valued Pythagorean trapezoidal fuzzy Einstein ordered weighted geometric (I-IVPTFEOWG) operator and induced interval-valued Pythagorean trapezoidal fuzzy Einstein hybrid geometric (I-IVPTFEHG) operator. Shakeel et al. [4] extended the idea of interval-valued Pythagorean trapezoidal fuzzy aggregation operators into interval-valued Pythagorean trapezoidal fuzzy Einstein weighted geometric, ordered weighted, and hybrid geometric operator such as, IVPTFEWG, IVPTFEOWG, IVPTFEHG operator respectively. The main advantages of the proposed operators are that, it is decided that these aggregation operators provided more accurate and precious result as compare to the above mention operators. Thus keeping an advantages of the above mention aggregation operators. In this paper we, develop a series of Pythagorean trapezoidal fuzzy Einstein aggregation operators, including the Pythagorean trapezoidal fuzzy Einstein weighted geometric (PTFEWG) operator, Pythagorean trapezoidal fuzzy Einstein ordered weighted geometric (PTFEOWG) operator and Pythagorean trapezoidal fuzzy Einstein hybrid geometric (PTFEHG) operator.
In this paper we develope the following sections; In section 2, we briefly review some basic concepts of intuitionistic fuzzy, Pythagorean fuzzy numbers, trapezoidal fuzzy numbers, and Einstein operations on Pythagorean trapezoidal fuzzy numbers. In section 3, we propose some Pythagorean trapezoidal fuzzy aggregation operators based on Einstein operations, such as Pythagorean trapezoidal fuzzy Einstein weighted geometric (PTFEWG) operator, the Pythagorean trapezoidal fuzzy Einstein ordered weighted geometric (PTFEOWG) operator and Pythagorean trapezoidal fuzzy Einstein hybrid geometric (PTFEHG) operator, to aggregate the PTFNs, whose desirable properties are also studied in this section. In section 4, we develop a multiple attribute group decision making method based on the proposed operators under Pythagorean trapezoidal fuzzy environment. In section 5, we give numerical example. In section 6, we compare the result with existing method. Concluding remarks are made in section 7.
Preliminaries
For each IFS, U in L
For each PFS, U in L,
To determine the degree of an accuracy of the interval Pythagorean trapezoidal fuzzy numbers
The theory of aggregation operators has an important role since in the beginning of fuzzy set theory. Einstein operations is a kind of various t-norms and t-conorms families [22, 23] can be used to accomplish the corresponding intersections and unions of IFSs, Pythagorean Einstein operations includes the Pythagorean Einstein product and the Pythagorean Einstein sum, respectively, defined as follows;
In the following, we define some operational laws of Pythagorean trapezoidal fuzzy numbers, based on Einstein operations.
(3) Let n be any positive integer and
By using mathematical induction we prove that Equation (8) holds for all positive integers n. First, we show that Equation (8) holds for n = 2. Since
Hence Equation (8) holds for n = k + 1, therefore Equation (8) holds for all n.
(4) Let m be any positive integer and
Then, we have
(1)
Pythagorean trapezoidal fuzzy Einstein aggregation operators Based on the above Einstein operational laws of PTFNs. We shall investigate the Pythagorean trapezoidal fuzzy information aggregation operators and give the definition of some aggregation operators with the Pythagorean trapezoidal fuzzy numbers based on Einstein operational laws as follows. Let Ωbe the set of Pythagorean trapezoidal fuzzy numbers.
Pythagorean Trapezoidal Fuzzy Einstein Geometric Aggregation Operators
(1) We prove that Equation (11) holds for n = 2. Since
(2) If Equation (11) holds for n = k, that is
When n = k + 1, such that,
(1) (Idempotency) If all
R(1): Decision matrix of expert-1
R(2): Decision matrix of expert-2
R(3): Decision matrix of expert-3
R(4): Decision matrix of expert-4
In this section, we apply the proposed aggregation operators to develop an approach for dealing with multiple attribute group decision making problems under the Pythagorean trapezoidal fuzzy environment.
In this section, we are going to present an illustrative example of the new approach in a decision-making problem. We analyze a company that operates in Europe and North America that wants to invest some money in a new market. They consider four possible alternatives such as A1 invest in the Asian market, A2 invest in the South American market, A3 invest in the African market. A4 Invest in all three markets.
To evaluate these alternatives, the investor has brought together a group of three alternatives. After analyzing the information, this group considers that the key factor is the economic situation of the world economy for the next period. They consider four main possible states of nature that could happen in the future. Consider there are four attributes C1 Bad economic situation, C2 Regular economic situation, C3 Good economic situation, C4 Very good economic situation. Let w = (0.40, 0.30, 0.20, 0.10) is the weighting vector of the attributes.
The experts of the government evaluate and offer their own opinions regarding the results obtained with each alternative. The weights of experts of is given as λ = (0.10, 0.20, 0.30, 0.40). As the environment is very uncertain, the group of experts needs to assess the available information by using PTFNs. The expected results given in the form of PTFNs depending on the characteristic C
j
and the alternative A
i
are shown Tables 1–4. Such that,
In ordered to show the validity and effectiveness of the proposed methods, we utilize Pythagorean fuzzy numbers to solve the same problem described above, we apply the proposed aggregation operators developed in this paper. After simplification we got the ranking result A1 > A2 > A3 > A4, A1 is best alternative. In above example, if we use Pythagorean fuzzy numbers to express the decision makers evaluations, then decision matrix R(1), R(2), R(3), R(4) can be written as decision matrix R/(1), R/(2), R/(3), R/(4) through deleting the corresponding trapezoidal fuzzy numbers from Pythagorean trapezoidal fuzzy numbers. Yager in [21] developed Pythagorean fuzzy numbers, such that sum of the square of membership and non-membership is less than or equal to 1. In [27] proposed PFEWG operator to deal with multiple attribute decision making with Pythagorean fuzzy information such that;
We further explain to find the best alternative in Pythagorean fuzzy set, after computation process, the overall preference values p
i
(i = 1, 2, 3, 4) as follows;
Now we find the ranking A2 > A1 > A4 > A3. In this case the A2 is best alternative.
It is noted that the ranking orders of obtained by this paper and by [27] are very different. This is because that all the trapezoidal fuzzy numbers are deleted from Pythagorean trapezoidal fuzzy numbers, which weakness the ability of information representation PFNs. Therefore, Pythagorean trapezoidal fuzzy numbers reflect may better the decision information than PFNs, by adding trapezoidal fuzzy numbers under the real decision making problem. Hence our proposed approach is more better than Pythagorean fuzzy numbers.
In this paper, we have investigated the multiple attribute group decision making problems in which attribute values are in the form ofPTFNs. We defined some operations of PTFNs based on Einstein operations, and some corresponding operational laws. Further, we have proposed some new Einstein aggregation operators for PTFNs, including Pythagorean trapezoidal fuzzy Einstein weighted geometric (PTFEWG) operator, Pythagorean trapezoidal fuzzy Einstein ordered weighted geometric (PTFEOWG) operator and Pythagorean trapezoidal fuzzy Einstein hybrid geometric (PTFEHG) operator, And desirable properties of the operators have also been analyzed. Then, numerical application based on these operators has been constructed to deal with multiple attribute group decision making problems under Pythagorean trapezoidal fuzzy environment and we compare the proposed method with existing method.In future we will extend Pythagorean trapezoidal fuzzy numbers to real life decision making. We will study TODIM-TOPSIS method based on Pythagorean trapezoidal fuzzy numbers.
