In this paper, we present the study of fuzzy parameterized bipolar fuzzy soft expert sets and define some of its basic operations. We validated the basic properties and essential laws related to this methodology. Lastly, we presented some possible applications in decision-making problems using a generalized algorithm.
All existing theories on uncertainty have their own flaws as pointed out by Molodtsov [1]. Molodtsov [1] was the pioneer who introduced the idea of soft sets. In [2], the authors extended the work of Molodtsov [1] and established the theory of fuzzy soft sets. Meanwhile, Alkhazaleh et al. [3] established the notion of fuzzy parameterized interval-valued fuzzy soft set and discussed its application in a decision-making problem. Cagman and his colleagues [4, 5] introduced the concept of fuzzy parameterized soft sets and fuzzy parameterized fuzzy soft sets and discussed the associated properties. Bashir and Salleh [6] pioneered the concept of fuzzy parameterized soft expert sets. Hazaymeh et al. [7] then extended the work of Bashir and Salleh [6] and discussed the theory of fuzzy parameterized fuzzy soft expert sets. The idea of fuzzy parameterized intuitionistic fuzzy soft expert sets and its application in decision making was first explored by Selvachandranwe and Salleh [8]. The idea of bipolar valued fuzzy sets was first established by Lee [9].
In this paper, we extend the concept of fuzzy parameterized fuzzy soft expert set to the bipolar-valued fuzzy set introduced by Lee [10], and we introduce the fuzzy parameterized bipolar fuzzy soft expert set (FPBFSES). We investigated the basic properties of this set such as De Morgan’s law and applied it to a decision-making problem, i.e., the home buying process.
Many researchers have studied the notion of soft expert sets and fuzzy soft expert sets. There continues to be growing research interest in this field. In this paper, we extend the work of Hazaymeh et al. [7] and introduce the notion of FPBFSES.
The paper is divided into different sections to discuss the various topics. In Section 2, the basic concepts of soft sets, bipolar fuzzy sets, bipolar fuzzy soft sets, soft expert sets, fuzzy parameterized fuzzy soft sets, and fuzzy parameterized fuzzy soft expert sets are provided. The definition and basic concepts of FPBFSES are discussed in Section 3. In Section 4, the fundamental operations on FPBFSES such as the union, intersection, complement, AND operator, OR operator and its properties are discussed. In Section 5, we design a generalized algorithm in FPBFSES to solve a decision-making problem, i.e., the home buying process. In the last section, we present the conclusion of the study.
Basic information on soft sets, bipolar fuzzy sets, bipolar fuzzy soft sets, soft expert sets, fuzzy parameterized fuzzy soft sets, and fuzzy parameterized fuzzy soft expert sets is provided.
Let F be the mapping given by : U → P(U) where P(U) is a power set of U. The pair (U, F) is called a soft set over U.
A bipolar-valued fuzzy set A defined over U is a mapping of the form
where
With A and B as bipolar fuzzy sets defined over the universal set U, they can be expressed as follows:
A bipolar fuzzy set A is a subset of a bipolar fuzzy set B if
The complement, union, and intersection of two bipolar fuzzy sets A and B are defined as follows:
Consider a set of universe U and a set of parameter E. Suppose P(U) represents the set of all bipolar fuzzy sets on U. Let F be a function given by F : A → P(U). Then, the pair (F, A) is called a bipolar fuzzy soft set (BFSS) over U where A ⊆ E.
A BFSS (F, A) is called a null BFSS if F(e) = ∅︀ for all e ∈ A.
A BFSS (F, A) is called an absolute BFSS if F(e) = B^{U} ∀e ∈ A, where B^{U} is the collection of all bipolar fuzzy subset of U.
Let (G, X) and (H, Y) be two BFSSs of U. Then, the union of (G, X) and (H, Y), represented by (G, X)∪(H, Y) is a BFSS classified as (G, X)∪(H, Y) = (I, Z), where Z = X ∪ Y and t ∈ Z,
Let (G, X) and (H, Y) be two BFSSs of U. Then, the intersection of (G, X) and (H, Y), represented by (G, X)∩(H, Y) is a BFSS classified as (G, X)∩(H, Y) = (I, Z) where Z = X ∪ Y and t ∈ Z,
Let F be a function given by F : A → P(U). Then, the pair (F, A) is said to be a soft expert set over U, where A ⊆ Z = E × X × O.
Consider a set of universe U, a set of parameter E, a set of experts X and a set of options O. Let Z = E × X × O and A ⊆ Z. Suppose F : A → B^{X} is a function defined by F(x) = F̂(x)(u_{i}) ∀u_{i} ∈ U, where B^{X} is the collection of all bipolar fuzzy sets. Then, F(x) is called a bipolar fuzzy soft expert set.
A pair (F, A)_{D} is called a fuzzy parameterized soft expert set over U, where F is a mapping given by F_{D} : A → P(U), while D is the fuzzy subset of the set of parameters E and P(U) denotes the power set of U.
We discussed the notion of FPBFSES and established relevant properties in this section.
Let U = {u_{1}, u_{2}, u_{3}, …, u_{n}} be set of universe and E = {e_{1}, e_{2}, e_{3}, …, e_{m}} be set of parameters, let A = {x_{1}, x_{2}, x_{3}, …, x_{i}} be a set of experts (agents), and let O = {1 =agree, 0 =disagree } be a set of opinions. Let Z = D×A×O, where D a fuzzy subset of E then Z = D×A×O is defined as
For the sake of simplicity in this paper, it is supposed that the set of opinions only consists of two values, namely, agree and disagree. However, it is possible to include other options in the set of opinions, including more specific opinions.
Let G be a mapping given by G_{D} : X → B^{U}, where B^{U} denotes the set of all bipolar fuzzy set on U. Then the pair (G, X)_{D} is said to be FPBFSES over U.
Let U = {u_{1}, u_{2}, u_{3}} be the set of three cars under consideration. Let e =beautiful, e =cheap and e =good conditionare the parameters for decision, E = {e_{1}, e_{2}, e_{3}}, A = {x_{1}, x_{2}} be the set of experts and D = {0.9/e_{1}, 0.6/e_{2}, 0.2/e_{3}}. The following information was obtained after getting the opinion of two experts:
Thus, (G, X)_{D} is FPBFSES.
A (G, X)_{D} is said to be FPBFSE subset of (H, Y)_{K}, denoted by (G, X)_{D} ⊑ (H, Y)_{K} if
1) X ⊆ Y,
2) For all ɛ ∈ X, G_{D}(ɛ) is bipolar fuzzy subset of H_{K}(ɛ).
They are equal if (G, X)_{D} ⊑ (H, Y)_{K} and (H, Y)_{K} ⊑ (G, X)_{D}.
An agree-FPBFSES denoted by (G, X)_{D}_{1} is defined as
A disagree-FPBFSES denoted by (G, X)_{D}_{0} is defined as
Some basic operations such as complement, union, intersection, AND operator and OR operator of FPBFSES are discussed in this segment.
Let (F, X)_{D} be a FPBFSES over U. Then, the complement of (F, X)_{D}, denoted by
where c is the bipolar fuzzy complement and ~ X ⊂ D^{c} × A × O.
Let (F, X)_{D} be a FPBFSES as defined in Example 3.3. Then, using the complement of bipolar fuzzy set, we have
Let (F, X)_{D} be a FPBFSES then we have
Let (F, X)_{D} be a FPBFSES. Then by definition of complement we have a mapping
The union of two FPBFSESs (G, X)_{D} and (H, Y)_{K} over U, denoted by (G, X)_{D} ⋓ (H, Y)_{K}, is the FPBFSES (I, Z)_{R} such that Z = {R × A × O}, where R = D ∪ K and ∀ɛ ∈ Z. Then I_{R}(ɛ) can be defined as follows:
where ∪ is bipolar fuzzy soft union.
Let U = {u_{1}, u_{2}, u_{3}} be set of universe, Z = {e_{1}, e_{2}, e_{3}} be set of decision parameter and A = {x_{1}, x_{2}} be set of experts such that X = {e_{1}, e_{2}} and Y = {e_{1}, e_{3}}. Suppose that (G, X)_{D} and (H, Y)_{K} are two FPBFSESs over a soft universe U such that
and
Using FPBFSES union, we have (G, X)_{D}⋓(H, Y)_{K} = (I, Z)_{R} where
Let (G, X)_{D}, (H, Y)_{K} and (I, Z)_{R} are FPBFSESs over a universe U. Then the following conditions hold:
1) (G, X)_{D} ⋓ (H, Y)_{K} = (H, Y)_{K} ⋓ (G, X)_{D},
2) ((G, X)_{D}⋓(H, Y)_{K})⋓(I, Z)_{R} = (G, X)_{D}⋓((H, Y)_{K}⋓ (I, Z)_{R}),
3) (G, X)_{D} ⋓ (G, X)_{D} = (G, X)_{D}.
Let (G, X)_{D} ⋓ (H, Y)_{K} = (I, Z)_{R}. Then by Definition 4.4, for all ɛ ∈ Z, we have R = D ∈ K and I_{R}(ɛ) = G_{D}(ɛ)∪H_{K}(ɛ). As we know that the union of fuzzy sets and BF sets are commutative then R = K ∈ D and I_{R}(ɛ) = H_{K}(ɛ)∪G_{D}(ɛ). Therefore (I, Z)_{R} = (H, Y)_{K} ⋓ (G, X)_{D}. Hence (G, X)_{D} ⋓ (H, Y)_{K} = (H, Y)_{K} ⋓ (G, X)_{D}.
The proof of the remaining parts is straightforward and is therefore omitted.
The intersection of two FPBFSESs (G, X)_{D} and (H, Y)_{K} over U, denoted by (G, X)_{D} ⋒ (H, Y)_{K}, is the FPBFSES (I, Z)_{R} such that Z = {R × A × O}, where R = D ∩ K and ∀ɛ ∈ Z. Then, I_{R}(ɛ) can be defined as
where ∩ is bipolar fuzzy soft intersection.
Consider Example 5.5. Then, (G, X)_{D} ⋒ (H, Y)_{K} = (I, Z)_{R}, where
Let (G, X)D, (H, Y)K, and (I, Z)R be FPBFSESs over a universe U. Then, the following conditions hold:
1) (G, X)_{D} ⋒ (H, Y)_{K} = (H, Y)_{K} ⋒ (G, X)_{D},
2) ((G, X)_{D}⋓(H, Y)_{K})⋒(I, Z)_{R} = (G, X)_{D}⋒((H, Y)_{K}⋒ (I, Z)_{R}),
3) (G, X)_{D} ⋒ (G, X)_{D} = (G, X)_{D}.
Its proof is straightforward by Definition 4.7 and is therefore omitted.
Let (G, X)D, (H, Y)K, and (I, Z)R be FPBFSESs over a universe U. Then, the following conditions hold:
1) (G, X)_{D}⋒((H, Y)_{K}⋓(I, Z)_{R}) = ((G, X)_{D}⋒(H, Y)_{K})⋓ ((G, X)_{D} ⋒ (I, Z)_{R}),
2) (G, X)_{D}⋓((H, Y)_{K}⋒(I, Z)_{R}) = ((G, X)_{D}⋓(H, Y)_{K})⋒ ((G, X)_{D} ⋓ (I, Z)_{R}).
The proof is simple and is therefore omitted.
Let (G, X)_{D} and (H, Y)_{K} are FPBFSESs over a universe U. Then the De Morgan’s laws holds true:
1)
2)
Let (G, X)_{D} and (H, Y)_{K} are FPBFSESs over a universe U, we have
ii) The proof is simple and is therefore eliminated.
Let (G, X)_{D} and (H, Y)_{K} are FPBFSESs over a universe U. Then “(G, X)_{D} AND (H, Y)_{K}”, denoted by (G, X)_{D}⋀̂(H, Y)_{K} = (I, X × Y)_{R} is a FPBFSES such that I_{R}(α, β) = (G_{D}(α)∩H_{K}(β)) for all (α, β) ∈ X × Y and R = D × K.
Let (G, X)_{D} and (H, Y)_{K} are FPBFSESs over a universe U. Then “(G, X)_{D} OR (H, Y)_{K}”, denoted by (G, X)_{D}⋁̂(H, Y)_{K} = (I, X × Y)_{R} is a FPBFSES such that I_{R}(α, β) = (G_{D}(α)∪H_{K}(β)) for all (α, β) ∈ X × Y and R = D × K.
Let (G, X)_{D} and (H, Y)_{K} are FPBFSESs over a universe U. Then the De Morgan’s laws holds true:
1)
2)
1) Let (G, X)_{D} and (H, Y)_{K} are FPBFSESs over a universe U. Then by Definition 5.10, we have
ii) The proof is simple and is therefore eliminated.
To solve hypothetical decision-making problems, we established a generalized algorithm, which will be applied to a FPBFSES model in this section.
Suppose that U = {h1, h2, h3} is the set of three houses making the set of universe. Suppose that Mr. X wants to buy a house while considering four decision parameters E = {e1, e2, e3, e4}, where ei (1, 2, 3, 4) represents the aesthetics, build material, environment, and commute convenience, respectively. Mr. X, his wife, and son have their own opinions, which are represented by the set of experts X = {p, q, r}. After detailed observation, the family constructs the fuzzy sets
and this is subsequently used to form the following FPBFSES.
Next, the generalized algorithm is applied to find the solution to a decision-making problem. The algorithm is used to select the best house among the three different houses. The generalized algorithm is defined as follows:
(1) Input the FPBFSES (F, A)_{D}.
(2) Compute
(3) Compute the largest numerical grade for agree and disagree FPBFSES.
(4) Find the score of every u_{i} ∈ U by taking the sum of the products of the numerical grade of each element with member function of fuzzy set D for agree and disagree FPBFSES.
(5) Find r_{j} where r_{j} = A_{j} − B_{j} for every u_{i} ∈ U.
(6) Find s, where s = max_{ui∈U}r_{j}. Then choose u_{i} as the best solution to the problem.
The largest numerical grade for the element in agree and disagree PBFSES are given in Tables 2 and 3, respectively.
Let A_{j} and B_{j} denote the score for every numerical grade of the agree and disagree FPBFSES, respectively.
These values are shown in Table 4. From the computations in Tables 1
In this paper, the notion of FPBFSES was introduced. The basic operations on FPBFSES were described. We verified De Morgan’s laws and other relevant laws by using the properties of these operations. We presented a decision-making generalized algorithm using this methodology and finally applied it to solve a decision-making problem.
No potential conflict of interest relevant to this article was reported.
Value of
(0.9/ | 0.8 | 0.9 | 1.3 |
(0.4/ | 0.9 | 1.2 | 1 |
(0.8/ | 1.1 | 0.8 | 1.2 |
(0.5/ | 1.3 | 1.4 | 0.8 |
(0.9/ | 1.3 | 0.3 | 1.4 |
(0.4/ | 1.05 | 1 | 1.3 |
(0.8/ | 0.7 | 0.8 | 1.2 |
(0.5/ | 1.3 | 1 | 1.4 |
(0.9/ | 1.2 | 1 | 0.8 |
(0.4/ | 1.58 | 1.2 | 0.96 |
(0.5/ | 0.2 | 1.2 | 1.38 |
(0.9/ | 1.1 | 1.1 | 1.2 |
(0.4/ | 1 | 0.55 | 1.27 |
(0.8/ | 1.6 | 1 | 0.9 |
(0.9/ | 1.5 | 1 | 0.9 |
(0.4/ | 1.6 | 1.1 | 1.2 |
(0.5/ | 1.1 | 1.02 | 0.8 |
(0.9/ | 1.2 | 1 | 1.4 |
(0.8/ | 0.8 | 1 | 1.3 |
(0.5/ | 1.1 | 1.3 | 1.1 |
Numerical grade for agree-FPBFSES
(0.9/ | 0.8 | 0.9 | 1.3 |
(0.4/ | 0.9 | 1.2 | 1 |
(0.8/ | 1.1 | 0.8 | 1.2 |
(0.5/ | 1.3 | 1.4 | 0.8 |
(0.9/ | 1.3 | 0.3 | 1.4 |
(0.4/ | 1.05 | 1 | 1.3 |
(0.8/ | 0.7 | 0.8 | 1.2 |
(0.5/ | 1.3 | 1 | 1.4 |
(0.9/ | 1.2 | 1 | 0.8 |
(0.4/ | 1.58 | 1.2 | 0.96 |
(0.5/ | 0.2 | 1.2 | 1.38 |
Numerical grade for disagree-FPBFSES
(0.9/ | 1.1 | 1.1 | 1.2 |
(0.4/ | 1 | 0.55 | 1.27 |
(0.8/ | 1.6 | 1 | 0.9 |
(0.9/ | 1.5 | 1 | 0.9 |
(0.4/ | 1.6 | 1.1 | 1.2 |
(0.5/ | 1.1 | 1.02 | 0.8 |
(0.9/ | 1.2 | 1 | 1.4 |
(0.8/ | 0.8 | 1 | 1.3 |
(0.5/ | 1.1 | 1.3 | 1.1 |
The score
score | score | −0.258 |
score | score | 0.210 |
score | score | 1.316 |
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