1. Introduction
In the recent era of decision making, there is often incomplete, indeterminate, and inconsistent information. Zadeh introduced the notion of fuzzy set [
1], which deals with uncertainty and can be applied in many fields. However, it has a shortcoming, i.e., it only expresses membership value and is unable to express non-membership value. At that point, Atanassov [
2] introduced the idea of intuitionistic fuzzy set (IFS) to address issues with the fuzzy set. Every component in IFS is shown by a structured pair, and every pair is portrayed by a membership value (truth-membership)
and a non-membership value (falsity-membership)
that satisfy the conditions
and
. IFSs can deal with incomplete data but cannot deal with the indeterminate and inconsistent data. Smarandache [
3] developed the neutrosophic set (NS) by including an indeterminacy membership value
, which is a generality of IFS. NS can deal with information very effectively, i.e., incomplete, indeterminate, and inconsistent. When
, it shows that this information is indeterminate and when
, it is inconsistent information.
Single valued neutrosophic set (SVNS), as suggested by Wang et al. [
4], was applied to decision making with the conditions
and
. Ye [
5] introduced the correlation coefficient and also proposed the comparison method for SVNSs. Wang et al. [
6] proposed the interval valued SVNSs to extend the truth, indeterminacy, and false membership to interval values. Ye [
7] defined the similarity measures between interval valued neutrosophic sets on the basis of the Hamming and Euclidean distances and also proposed a multiple attribute decision-making method.
Aggregation operators are the important research areas, claiming the attention of today’s researchers. Since the proposed theory of IFS, many scientists [
8,
9,
10,
11,
12,
13,
14,
15] have made essential contributions to the advancement of IFS theory. XU and Yager [
12] developed the notion of aggregation operators based on IFS. They also applied these aggregation operators to decision making. Wang and Liu [
16], proposed the idea of Einstein aggregation operators. Zhao and Wei [
17] built up some of the Einstein hybrid aggregation operators. Fahmi et al. [
18,
19,
20,
21] developed aggregation operators based on triangular and trapezoidal cubic fuzzy numbers with applications to decision making. Rahman et al. [
22,
23,
24,
25] proposed aggregation operators on different extension of fuzzy numbers. The bipolar fuzzy set (BFS) [
26,
27,
28] uses a substitute method to deal with uncertainty in decision making. The bipolar fuzzy set consists of a positive as well as negative membership degree. The membership degree of the bipolar fuzzy set ranges from −1 to 1. BFSs have been useful in various research domains and set theory decision analysis and organizational modeling [
29], quantum computing [
30], physics and philosophy [
31], and graph theory [
32]. Bipolar averaging and geometric fuzzy aggregations operators were defined by Gul [
33]. Irfan et al. [
34] presented the bipolar neutrosophic set with basic operations. They also proposed the comparison method for bipolar neutrosophic sets. Fan et al. [
35] developed Heronian mean operators in a bipolar neutrosophic environment. Irfan et al. [
36] presented the interval valued bipolar neutrosophic set with applications to pattern recognition. Irfan et al. [
37] proposed the interval valued neutrosophic soft set with applications to decision making. Zhan et al. [
38] proposed Schweizer-Sklar Muirhead mean aggregation operators based on single-valued neutrosophic set. Ashraf et al. introduced some logarithmic aggregation operators on neutrosophic sets [
39].
Hamacher t-norm and t-conorm [
40], which are the generalization of algebraic and Einstein t-norm and t-conorm, are more general and flexible. There are many researchers who extended the Hamacher operations to solve multiple attribute decision-making problems combined with other fuzzy environments, such as intuitionistic [
41], interval valued intuitionistic [
42], hesitant fuzzy [
43], hesitant Pythagorean fuzzy [
44], bipolar fuzzy numbers [
45] and neutrosophic numbers [
46,
47]. Since the development of this field, there has been no significant research on Hamacher operations and its applicability to bipolar neutrosophic numbers. Here, we extended the Hamacher operations to bipolar neutrosophic numbers to develop bipolar neutrosophic Hamacher aggregation operators for multiple attribute decision-making problems.
Decision making plays a key role in present day management. Necessarily, sound decision making is a basic part of administration. Consciously or unconsciously, a manager makes a decision, or decisions, as it is his or her responsibility as a manager. Decision making has a consequential role as organizational and managerial activities are linked with that decision. A decision is explained as a sequence of actions, intentionally taken from a set of alternates, to accomplish managerial or organizational targets. The decision-making process is an incessant and obligatory part of organization management or activities that are carried out in business. Decisions are made to address the events of all activities related to business and organizations. Decision and economic theories are interconnected with the assumption that field experts make a decision to make the best use out of their personal interest and reasonableness. This, though, doesn’t take into consideration the probabilities of intervening factors that make decision making dependent upon the situation. The aforementioned factors play a key role in normalizing decision making for the manager to achieve optimal targets.
Although there is much literature available regarding the present study, the points given below, connected to the bipolar neutrosophic set and its aggregation operator, motivated the researcher to construct a detail and deep inquiry in the present study. Our main rationalizations and are as follows:
- (1)
Single valued neutrosophic sets (SVNSs) help in dealing with uncertain information in a more reliable way. It is a generalization of classical set, fuzzy set, and intuitionistic fuzzy set etc., and adaptable to the framework in comparison with pre-existing fuzzy sets and its versions.
- (2)
To deal with uncertain real-life problems, bipolar fuzzy sets are of great value and prove to be helpful in dealing with the positive as well as the negative membership values.
- (3)
We tried to merge these ideas with the Hamacher aggregation operator and strive to develop a more effective tool to deal with uncertainty in the form of bipolar neutrosophic Haymaker averaging aggregation operators.
- (4)
The objective of the study was to propose bipolar neutrosophic Hamacher operators and also study its properties. Furthermore, we proposed three aggregation operators, namely bipolar neutrosophic Hamacher weighted averaging operators (BNHWA), and bipolar neutrosophic Hamacher ordered weighted averaging operators (BNHOWA) and bipolar neutrosophic Hamacher hybrid averaging operators (BNHHA). Multi-attribute decision making (MADM) program approach is established based on bipolar neutrosophic numbers
- (5)
So as to affirm the effectiveness of the proposed method, we applied bipolar neutrosophic numbers to the decision-making problem.
- (6)
The initial decision matrices were composed of bipolar neutrosophic numbers and transformed into a collective bipolar neutrosophic decision matrix.
- (7)
The proposed operators probably completely elaborate the vagueness of bipolar neutrosophic Hamacher aggregation operator.
The rest of the study is organized as follows:
Section 2 comprises the fundamental definitions and their related properties, which are required later in paper.
In
Section 3 we introduce the BNHWA operator, BNHWOA operator and BNHHA operator.
In
Section 4, the new aggregation operators are applied to group decision making and we propose a numerical problem.
In
Section 5, there is a comparison of our method in relation to other methods and concluding remarks are also given.
2. Preliminaries
Fundamental definitions of neutrosophic set are provided in the current section for bipolar fuzzy set, bipolar neutrosophic set, score function, accuracy function, certainty function and Hamacher operations.
Definition 1. [3]
Let P be any fixed set. Then a neutrosophic set (NS) is as follows:where the truth-membership is , the indeterminacy-membership is , and the falsity-membership is , where . and are real standard or non-standards subsets of , which was proposed by Abraham Robinson in 1966 [48]. There is no restriction on the sum of and so . As it is difficult to apply NS in real scientific and engineering areas, Wang et al. [
4] proposed the concept of a single valued neutrosophic set (SVNS), which follows.
Definition 2. [4]
Let P be a non-empty set, with an element in P denoted by p, and then the single valued neutrosophic set (SVNS) of A in H is as follows:where the truth-membership is , the indeterminacy-membership is , and the falsity-membership is where . There is one condition, i.e., The basic operations for two SVNSs are:
and are given as follows:
The subset
if, and only if,
if, and only if,
the complement
is
the intersection is defined by
Definition 3. [49] Let and be two single-value neutrosophic numbers (SVNNs). Then, the operations for the SVNNs are as follows: and
where
Definition 4. [49] Let be a SVNN. Then, the score function is as follows: Definition 5. [49] Let be a SVNN. Then, the accuracy function a is as follows: Definition 6. [49] Let be a SVNN. Then, the certainty function is as follows: Definition 7. [49] Let and be two SVNNs. Then, the comparison is as follows: if then is greater than , denoted by ,
if and aa, then is greater than , denoted by ,
if , aa and then is greater than , denoted by and
if , aa and then is equal to , denoted by .
Definition 8. [28] Let P be a fixed set, and the bipolar fuzzy set is as follows:where the positive degree of membership is and the negative degree of membership is where and Definition 9. [34] A bipolar neutrosophic set (BNS), A in P, is as follows: Let and where, is the positive degree of truth, the indeterminate and false membership of and is the negative degree of truth, the indeterminate and false membership of . Then and where and There are conditions where
Example 1. Let , thenis a bipolar neutrosophic subset of P. Basic operations [
34], for two bipolar neutrosophic sets (BNSs), are as follows:
if, and only if,
and
if, and only if,
and
The intersection is defined as:
Let
and be a BNS. Then the complement
is defined as:
and
Definition 10. [34] Let and be two bipolar neutrosophic numbers (BNNs). Then, the operations for the BNNs are as follows:
where
Definition 11. [34] Let be a bipolar neutrosophic number (BNN), then the score function of is as follows: Definition 12. [34] Let be a BNN, then the accuracy function of is as follows: Definition 13. [34] Let be a bipolar neutrosophic value, then the certainty function of is as follows: Definition 14. [34] Let and be two BNNs, then the comparison method is as follows: - (i)
If then is greater than , denoted by
- (ii)
If and then is superior to , denoted by
- (iii)
If and then is greater than , denoted by and
- (iv)
If and then is equal to , denoted by
Hamacher [
40] proposed a more generalized t-norm and t-conorm. The Hamacher product,
, is a t-norm and the Hamacher sum,
, is a t-conorm, where:
when
, the Hamacher t-norm and t-conorm will be reduced to algebraic t-norm and
t-conorm, respectively:
when
, the Hamacher t-norm and t-conorm will be reduced to the Einstein t-norm and t-conorm, respectively [
16]:
The following definitions introduce the Hamacher operations of bipolar neutrosophic set, as the notion of the bipolar neutrosophic Hamacher sum, product, scalar multiple and exponential operations are defined.
Definition 15. Let , and be three BNNs values, and be any real number, then we define basic Hamacher operators with . 3. Bipolar Neutrosophic Hamacher Aggregation Operators
We propose some properties of the Hamacher aggregation operators in this part of the paper for bipolar neutrosophic Hamacher weighted averaging (BNHWA), bipolar neutrosophic Hamacher ordered weighted averaging (BNHOWA) and bipolar neutrosophic Hamacher hybrid averaging (BNHHA).
Let be a family of BNNs, where and .
3.1. Bipolar Neutrosophic HamacherWeighted Averaging Aggregation Operator
Definition 16. The bipolar neutrosophic Hamacher weighted averaging (BNHWA) operator can be defined as follows:
where
is the weighted vector of
, such that
and
Theorem 1. The (BNHWA) operator gives a bipolar neutrosophic value when:where is the weighted vector of , such that and Proof.
This theorem can be proved by mathematical induction as follows:
When
and for
So it is proved for
.
Now when
in Equation (14), then
This proves that it is true for
Thus, Equation (14) is true for , which proves Theorem 1. □
Theorem 2. (Idempotency) Let , where and be a collection of BNNs are equal, i.e., for all , then: Theorem 3. (Boundedness) Let , then: Theorem 4. (Monotonicity) Let , where and , and where and are two BNNs. If , for all , then: A discussion of two cases of BNHWA operator follows.
If
, then the BNHWA is converted to the bipolar neutrosophic weighted average (BNWA):
If
, then the BNHWA is converted to the bipolar neutrosophic Einstein weighted average (BNEWA):
3.2. Bipolar Neutrosophic Hamacher OrderedWeighted Averaging Aggregation Operator
Definition 17. The bipolar neutrosophic Hamacher ordered weighted averaging (BNHOWA) operator can be defined as follows:where is a permutation with , , , and is the weighted vector of such that and Theorem 5. The (BNHOWA) operator gives a bipolar neutrosophic value:where
is a permutation with , , and is the weighted vector of such that and Proof.
The theorem is straightforward. □
Theorem 6. (Idempotency) Let , where and are a collection of equal BNNs, i.e., for all , then: Theorem 7. (Boundedness) Let , then: Theorem 8. (Monotonicity) Let , where and and , where and are two BNNs. If , for all , then: Now, we discuss two cases of the BNHOWA operator:
If
, the BNHOWA is converted to the bipolar neutrosophic ordered weighted average (BNOWA):
If
, the BNHOWA is converted to the bipolar neutrosophic Einstein ordered weighted average (BNEOWA):
3.3. Bipolar Neutrosophic Hamacher HybridAveraging Aggregation Operator
Definition 18. The bipolar neutrosophic Hamacher hybrid averaging (BNHHA) operator can be defined as follows:where is a weighting vector of , , such that and is the -th largest element of the bipolar neutrosophic argument, and also are weighting vectors of bipolar neutrosophic arguments , , such that , where n is the balancing coefficient. Note that BNHHA reduces to BNHWA if and BNHOWA operator if: Theorem 9. The (BNHHA) operator returns a bipolar neutrosophic value when:where is the weighting vector of , , such that and is the
-th largest element of the bipolar neutrosophic arguments, and also are the weighting vector of bipolar neutrosophic arguments , , such that , where n is the balancing coefficient, Proof.
The theorem is straightforward. □
Now, we discuss two cases of BNHOWA:
If
, the BNHHA is converted to the bipolar neutrosophic hybrid averaging (BNHA):
If
, the BNHHA is converted to the bipolar neutrosophic Einstein hybrid averaging (BNEHA):