The main motivation behind this paper is to study some structural properties of a non-associative structure Abel Grassmann’s groupoid (AG-groupoid) in terms of double-framed soft sets (DFS sets) as it hasn’t attracted much attention compared to associative structures. An AG-groupoid can be referred to as a non-associative semigroup, as the main difference between a semigroup and an AG-groupoid is the switching of an associative law. In this paper, we introduce the concept of (M, N)-double-framed soft ideals (briefly, (M, N)-DFS ideal) of AG-groupoids and investigate some properties of these notions. We have shown that every (M, N)-DFS ideal is (M, N)-DFS AG-groupoid but the converse is not true. This is shown with the help of an example. We also discuss the properties of (M, N)-DFS ideals in regular AG-groupoids. Moreover a decision making algorithm based on DFS-sets is given.
In many real world problems, there arises uncertainty in the data. For example, the set of regions with cold weather. Two regions with temperature -2 degree Celsius and -10 degree Celsius have different degree of belongness to this set. To deal with such uncertainty, in 1965, Zadeh [34] introduced the concept of a fuzzy set. This concept helped to deal with vagueness and the representation of imperfect knowledge which had troubled the minds of many philosophers, logicians and mathematicians for a long time and became a crucial issue for computer scientists, particularly in the area of artificial intelligence. Since then there have been many extensions of the fuzzy set theory. A few extensions of fuzzy sets are intuitionistic fuzzy sets, rough sets, theory vague sets, bipolar fuzzy sets, picture fuzzy sets. But there are inherent difficulties associated with each of these theories pointed out by Molodtsov [25]. All these techniques lack in the parameterization tool and hence could not be successfully useful in tackling problems especially in areas like economics, environmental sciences and social sciences. Russian researcher Molodtsov conceptualized a framework known as soft set [25] in 1999. Since then, theory of soft sets has undergone rapid growth and has been applied to complex topics like optimization, decision making, data analysis, forecasting etc. (see [5, 49]. Soft set theory has also been applied to different algebraic structures. We refer the reader to the papers [1–4, 33].
Commutative law is given by abc = cba in ternary operations. By putting brackets on the left two elements of both hand sides of this equation, i.e. (ab) c = (cb) a, in 1972, Kazim and Naseeruddin [11] introduced a new algebraic structure which they named a left almost semigroup (briefly as LA-semigroup). Some other names have also been used in literature for left almost semigroups. Cho et al. [6] studied this structure under the name of right modular groupoid. Holgate [7] studied it as left invertive groupoid. Similarly, Stevanovic and Protic [28] called this structure an Abel-Grassmann groupoid (or simply AG-groupoid), which is the primary name under which this structure is known nowadays. There are many important applications of AG-groupoids in the theory of flocks [27]. For more study of AG-groupoids, the reader is suggested to read [15–17, 31].
Recently, Jun et al. [8] introduced double-framed soft sets (breifly DFS-sets) and developed ideal theory of BCK/BCI-algebra in terms of DFS-sets. Khan et al. [14] applied the idea of DFS-sets to AG-groupoids.
The present paper is an extension of our previous work [14]. Motivated by the idea of Zhan et al. [20, 35], we introduce (M, N)-DFS ideals of AG-groupoids. The organization of the paper is as follows:
In Section 2 basic concepts of AG-groupoids are given. In Section 3, the concept of soft set and DFS-set and its operations are reviewed. In Section 4, we define (M, N)-DFS AG-groupoids, (M, N)-DFS ideals and discuss some properties of these ideals. In Section 5, we study these notions in regular AG-groupoids. In the last section, we give a decision making algorithm based on DFS-sets and apply it to a real world problem.
Preliminaries
A groupoid (S, ·) is called an AG-groupoid if it satisfies the left invertive law, that is, (ab) c = (cb) a for all a, b, c ∈ S.
Every AG-groupoid S satisfies the medial law, that is, (ab) (cd) = (ac) (bd) for all a, b, c, d ∈ S.
It is basically a non-associative algebraic structure in between a groupoid and a commutative semigroup. It is important to mention here that if an AG-groupoid contains identity or even right identity, then it becomes a commutative monoid. An AG-groupoid may or may not contain left identity. If there exists left identity in an AG-groupoid then it is unique [26].
Every AG-groupoid S with left identity satisfies the paramedial law, that is, (ab) (cd) = (db) (ca) for all a, b, c, d ∈ S.
In an AG-groupoid S with left identity, using the paramedial law, it is easy to prove that
Moreover, in an AG-groupoid S with left identity, we have
Throughout this paper, S will represent an AG-groupoid unless otherwise stated.
For non-empty subsets A and B of S, we have AB : = {ab|a ∈ A and b ∈ B}. If A = {a} then we write aB instead of {a} B.
A nonempty subset A of an AG-groupoid S is called sub AG-groupoid of S if A2 ⊆ A.
A nonempty subset A of an AG-groupoid S is called left (resp. right) ideal of S if SA ⊆ A (resp. AS ⊆ A).
If A is both a left and a right ideal of S then it is called a two-sided ideal or simply an ideal of S.
Soft set (basic operations)
In [2], Atagun and Sezgin introduced some new operations on soft set theory and defined soft sets in the following way: Let U be an initial universe, E a set of parameters, P (U) the power set of U and A ⊆ E. Then soft set fA over U is a function defined by fA : E ⟶ P (U) such that fA (x) =∅ if x ∉ A. Here fA is called an approximate function. A soft set over U can be represented by the set of ordered pairs fA : = { (x, fA (x)) : x ∈ E, fA (x) ∈ P (U) }.
It is clear that a soft set is a parameterized family of subsets of U. The set of all soft sets over U is denoted by S (U).
Definition 3.1. [2] Let fA, fB ∈ S (U). Then fA is a soft subset of fB, denoted by if fA (x) ⊆ fB (x) for all x ∈ E. Two soft sets fA, fB are said to be equal soft sets if and and is denoted by
Definition 3.2. [2] Let fA, fB ∈ S (U). Then the union of fA and fB, denoted by , is defined by where fA∪B (x) = fA (x) ∪ fB (x), for all x ∈ E.
Definition 3.3. [2] Let fA, fB ∈ S (U). Then the intersection of fA and fB, denoted by , is defined by where fA∩B (x) = fA (x) ∩ fB (x), for all x ∈ E.
Definition 3.4. [2] Let fA, fB ∈ S (U). Then the soft product of fA and fB, denoted by is defined by
Throughout this paper, let E = S, where S is an AG-groupoid and A, B, C, ⋯ are sub AG-groupoids, unless otherwise stated.
Definition 3.5. [8] A double-framed soft pair 〈 (αA, βA); A〉 is called a double-framed soft set of A over U (briefly, DFS-set), where αA and βA are mappings from A to P (U).
The set of all DFS-sets of S over U will be denoted by DFS (U).
We define the order relation ⊑[M,N] on DFS (U) as follows:
For any 〈 (fS, gS); S〉 and 〈 (pS, qS); S〉, ∅ ⊆ M ⊂ N ⊆ U, we define;〈 (fS, gS); S〉 ⊑ [M,N] 〈 (pS, qS); S 〉 ⇔ (fS (x) ∩ N) ∪ M ⊆ (pS (x) ∩ N) ∪ M and (gS (x) ∪ M) ∩ N ⊇ (qS (x) ∪ M) ∩ N for all x ∈ S.
If in case 〈 (fS, gS); S〉 ⊑ [M,N] 〈 (pS, qS); S 〉 and 〈 (pS, qS); B〉 ⊑ [M,N] 〈 (fS, gS); S 〉 then we say 〈 (fS, gS); A〉 = [M,N] 〈 (pS, qS); B 〉.
Let 〈 (αS, βS); S〉 and 〈 (fS, gS); S〉 be two double-framed soft sets of S over U. Then the int-uni soft product [12] is denoted by 〈 (αS, βS); S〉 ◊ 〈 (fS, gS); S 〉 and is defined as the double framed soft set over U, in which and are soft mappings from S to P (U), given as follows:
One can easily see that the operation “◊” is well-defined.
For a DFS-set 〈 (αS, βS); S〉 of S and two subsets γ and δ of U, the γ-inclusive set and the δ-exclusive set of 〈 (αS, βS); S〉, denoted by iS (αS; γ) and eS (βS; δ), respectively, are defined as:
and eS (βS; δ) ={ x ∈ S|βS (x) ⊆ δ } respectively.
The double framed soft including set [8] of the DFS-set 〈 (αS, βS); S〉 is defined as:
It is clear that DFS (αS, βS) (γ,δ) : = iS (αS; γ) ∩ eS (βS; δ).
For any two DFS sets 〈 (αS, βS); S〉 and 〈 (fS, gS); S〉 of S over U, the DFS intersection [8] of 〈 (αS, βS); S〉 and 〈 (fS, gS); S 〉, is defined to be the DFS set where and are mappings given by and for all x ∈ S. It is denoted by . For any two DFS sets 〈 (αS, βS); S〉 and 〈 (fS, gS); S〉 of S over U, the DFS union of 〈 (αS, βS); S〉 and 〈 (fS, gS); S 〉, is defined the be the DFS set where and are mappings given by and for all x ∈ S. It is denoted by . We have the following lemmas.
Lemma 3.6.The set (DFS (U), ◊) forms an AG-groupoid.
Proof. Obviously, the operation ”◊” is well-defined.Let 〈 (fS, gS); S 〉, 〈 (hS, kS); S 〉, and 〈 (pS, qS); S 〉 ∈ DFS (U) and take an element x of S. If there does not exist any elements in S such that x is their product then clearly, and ∪ M ) ∩ N. Let x can be expressed as the product of two elements y, z of S. Then, we have
Similarly we can prove that Now
Similarly we can prove that . Hence (DFS (U), ◊) is an AG-groupoid. □
The proof of the following lemmas are based on Lemma 3.6.
Lemma 3.7.If S is an AG-Groupoid then the medial law holds in DFS (U). i.e. for 〈 (αS, βS); S 〉, 〈 (fS, gS); S 〉, 〈 (hS, kS); S〉 and 〈 (pS, qS); S 〉 ∈ DFS (U), we have ( 〈 (αS, βS); S 〉 ◊ 〈 ( fS, gS); S 〉) ◊ (〈 (hS, kS); S 〉 ◊ 〈 (pS, qS); S 〉 ) = [M,N] (〈 (αS, βS); S 〉 ◊ 〈 (hS, kS); S 〉) ◊ (〈 (fS, gS); S 〉 ◊ 〈 (pS, qS); S 〉).
Lemma 3.8.If S is an AG-groupoid with left identity then the paramedial law holds in DFS (U). That is for all 〈 (αS, βS); S 〉, 〈 (fS, gS); S 〉, 〈 (hS, kS); S〉 and 〈 (pS, qS); S 〉 ∈ DFS (U), (〈 (αS, βS); S 〉 ◊ 〈 (fS, gS); S 〉) ◊ (〈 (hS, kS); S 〉 ◊ 〈 (pS, qS); S 〉) = [M,N] (〈 (pS, qS); S 〉 ◊ 〈 (fS, gS); S 〉) ◊ (〈 (hS, kS); S 〉 ◊ 〈 (αS, βS); S 〉).
Lemma 3.9.If S is an AG-groupoid with left identity then for all 〈 (fS, gS); S 〉, 〈 (hS, kS); S〉 and 〈 (pS, qS); S 〉 ∈ DFS (U), we have〈 (fS, gS); S 〉 ◊ (〈 (hS, kS); S 〉 ◊ 〈 (pS, qS); S 〉) = [M,N] 〈 (hS, kS); S 〉 ◊ (〈 (fS, gS); S 〉 ◊ 〈 (pS, qS); S 〉).
Lemma 3.10.If S is an AG-groupoid and 〈 (αS, βS); S〉, 〈 (fS, gS); S〉,〈 (hS, kS); S〉 and 〈 (pS, qS); S 〉 ∈ DFS (U), then the following hold. (i) 〈 (αS, βS); S 〉 ◊ (〈 (fS, gS); S 〉 ⊓ 〈 (hS, kS); S 〉) = [M,N] 〈 (αS, βS); S 〉 ◊ 〈 (fS, gS); S 〉 ⊓ 〈 (αS, βS); S 〉 ◊ 〈 (hS, kS); S 〉). (ii) 〈 (αS, βS); S 〉 ◊ (〈 (fS, gS); S 〉 ⊔ 〈 (hS, kS); S 〉) = [M,N] 〈 (αS, βS); S 〉 ◊ 〈 (fS, gS); S 〉 ⊔ 〈 (αS, βS); S 〉 ◊ 〈 (hS, kS); S 〉). (iii) If 〈 (αS, βS); S〉 ⊑ [M,N] 〈 (fS, gS); S 〉 then 〈 (αS, βS); S 〉 ◊ 〈 (hS, kS); S 〉 ⊑ [M,N] 〈 (fS, gS); S 〉 ◊ 〈 (hS, kS); S 〉. (iv) If 〈 (αS, βS); S〉 ⊑ [M,N] 〈 (fS, gS); S 〉 and 〈 (hS, kS); S〉 ⊑ [M,N] 〈 (pS, qS); S 〉 then 〈 (αS, βS); S 〉 ◊ 〈 (hS, kS); S 〉 ⊑ [M,N] 〈 (fS, gS); S 〉 ◊ 〈 (pS, qS); S 〉.
Definition 3.11. [12] Let S be an AG-groupoid and 〈 (fS, gS); S〉 be a DFS-set of S over U. Then 〈 (fS, gS); S〉 is called double-framed soft AG-groupoid (briefly, DFS AG-groupoid) of S over U if fS (xy) ⊇ fS (x) ∩ fS (y) and gS (xy) ⊆ gS (x) ∪ gS (y) for all x, y ∈ S.
(M, N)-double framed soft ideals
In this section, we give concepts of (M, N)-DFS AG-groupoid, (M, N)-DFS ideal of an AG-groupoid and discuss their properties. From now on ∅⊆ M ⊂ N ⊆ U.
Definition 4.1. Let S be an AG-groupoid and 〈 (fA, gA); A〉 be a DFS-set of S over U. Then 〈 (fA, gA); A〉 is called (M, N)-double-framed soft AG-groupoid (briefly, (M, N)-DFS AG-groupoid) of S over U if fA (xy) ∪ M ⊇ fA (x) ∩ fA (y) ∩ N and fA (xy) ∪ M ⊇ fA (x) ∩ fA (y) ∩ N and gA (xy) ∩ N ⊆ gA (x) ∪ gA (y) ∪ M for all x, y ∈ A.
Example 4.2. Consider an AG-groupoid S = {a, b, c, d} with the following multiplication table:
Let , , and a double-framed soft 〈 (fS, gS); S〉 of S over defined by and
By routine checking it is easy to verify that 〈 (fS, gS); S〉 is an (M, N)-DFS AG-groupoid of S over U.
Remark 4.3. If 〈 (fS, gS); S〉 is a DFS AG-groupoid of S over U, then 〈 (fS, gS); S〉 is an (∅, U)-DFS AG-groupoid of S over U. Therefore,every DFS AG-groupoid of S is an (M, N)-DFS AG-groupoid, but the converse is not true.
Example 4.4. Reconsider Example 4.2, 〈 (fS, gS); S〉 is (M, N)-DFS AG-groupoid which is not DFS AG-groupoid because fS (d) nsupseteqfS (b) ∩ fS (b) and gS (d) ⊈ gS (c) ∪ gS (d).
For any DFS-set 〈 (fS, gS); S〉 of S, let be a DFS-set of S defined by
where γ, δ, η and ρ are subsets of U with η ⊊ fS (x) and ρsupsetneqgS (x).
Theorem 4.5.If 〈 (fA, gA); A〉 is an (M, N)-DFS AG-groupoid of A over U then so is .
Proof. Suppose that 〈 (fA, gA); A〉 is an (M, N)-DFS AG-groupoid of A over U then non-empty γ-inclusive set and δ-exclusive set of 〈 (fA, gS); A〉 are sub AG-groupoids of S for any subsets γ and δ of U with M ⊆ γ ⊆ N, M ⊆ δ ⊆ N. Let x, y ∈ A. Case (i). If x, y ∈ iA (fA; γ) then xy ∈ iA (fA; γ) and hence Case (ii). If x ∉ iA (fA; γ) and y ∈ iA (fA; γ) then and . Here two subcases arise.
Case (ii-a) If xy ∈ iA (fA; γ) then
Case (ii-b) If xy ∉ iA (fA; γ) then .
Case (iii) If x ∉ iA (fA; γ) and y ∉ iA (fA; γ) then two subcases arise.
Case (iii-a) If xy ∈ iA (fA; γ) then
Case (iii-b) If xy ∉ iA (fA; γ) then
Now let x, y ∈ A. We discuss similar cases for the eA (gA; δ).
Case (i) If x, y ∈ eA (gA; δ) then xy ∈ eA (gA; δ) and hence Case (ii) If x ∉ eA (gA; δ) and y ∈ eA (gA; δ) then two subcases arise.
Case (ii-a) If xy ∈ eA (gA; δ) then .
Case (ii-b) If xy ∉ eA (gA; δ) then
Case (iii) If x ∉ eA (gA; δ) and y ∉ eA (gA; δ) then two subcases arise.
Case (iii-a) If xy ∈ eA (gA; δ) then .
Case (iii-b) If xy ∉ eA (gA; δ) then Therefore is an (M, N)-DFS AG-groupoid of A.
The converse of this theorem is not true in general.
Example 4.6. Suppose the initial universe U given by , the non-negative integers modulo 10. Let S = {a, b, c, d} be the set with the following multiplication table
Let M = {1, 2}, N = {1, 2, 3, 5} and be the non-negative integers modulo 10. Define a DFS set 〈 (fS, gS); S〉 by fS (a) = {1, 3, 5}, fS (b) = {2, 3, 5}, fS (c) = {1, 2, 3, 4}, fS (d) = {1, 2, 3} and gS (a) = {1, 2}, gS (b) = {2, 3}, gS (c) = {2}, gS (d) = {1, 3} then iS (fS; γ) = {c, d} for γ = {1, 2} and eS (gS; δ) = {c} for δ = {2}.
According to the definition, we have is defined as; and . By routine checking, we have is (M, N)-DFS AG-groupoid. But 〈 (fS, gS); S〉 is not (M, N)-DFS AG-groupoid because fS (c) ∪ M = {1, 2, 3, 4} nsupseteq {3, 5} = fS (a) ∩ fS (b) ∩ N or gS (d) ∩ N = {1, 2, 3} ⊈ {1, 2} = gS (a) ∪ gS (a) ∪ M.
Theorem 4.7.If 〈 (fS, gS); S〉 and 〈 (hS, kS); S〉 are (M, N)-DFS AG groupoids then their intersection 〈 (fS, gS); S〉 ⊓ 〈 (hS, kS); S 〉 is (M, N)-DFS AG groupoid.
Proof. Let x, y ∈ S. Since 〈 (fS, gS); S〉 and 〈 (hS, kS); S〉 are (M, N)-DFS AG groupoids then
and
Hence 〈 (fS, gS); S〉 ⊓ 〈 (hS, kS); S 〉 is (M, N)-DFS AG groupoid.
It is important to note that the union of two (M, N)-DFS AG groupoids may or may not be an (M, N)-DFS AG groupoid.
Example 4.8. Suppose the initial universe U given by , the non-negative integers modulo 10. Let S = {a, b, c, d} be the set with the following multiplication table
Let M = {1, 4}, N = {1, 2, 3, 4, 5} and be the non-negative integers modulo 10. Define a DFS set 〈 (fS, gS); S〉 by fS (a) = {1, 4}, fS (b) = {4, 5}, fS (c) = {1, 4, 7}, fS (d) = {1, 3, 5} and gS (a) = {1, 2, 3, 5, 7}, gS (b) = {1, 3, 4, 5}, gS (c) = {1, 2, 3, 5}, gS (d) = {1, 3, 5, 7}.
One can easily check that 〈 (fS, gS); S〉 and 〈 (hS, kS); S〉 are (M, N)-DFS AG groupoids of S over U. But their union 〈 (pS, qS); S〉 given by pS (a) = {1, 4}, pS (b) = {4, 5, 7}, pS (c) = {1, 4, 7, 9}, pS (d) ={ 1, 3, 5, 7 } and qS (a) = {2, 3, 5}, qS (b) = {1, 3, 4, 5}, qS (c) = {1, 2, 3}, qS (d) = {3, 5, 7} is not (M, N)-DFS AG groupoid since pS (c) ∪ M = {1, 4, 7, 9} nsupseteq {5} = pS (b) ∩ pS (d) ∩ N and/or qS (c) ∩ N = {1, 2, 3} ⊈ {1, 3, 4, 5, 7} = qS (b) ∪ qS (d) ∪ M.
Theorem 4.9.A DFS-set 〈 (fS, gS); S〉 of an AG groupoid S over U is an (M, N)-DFS AG groupoid if and only if
Proof. Assume that the DFS-set 〈 (fS, gS); S〉 of an AG groupoid S over U is (M, N)-DFS AG groupoid and take x ∈ S. If there are no y, z ∈ S such that x = yz then
Suppose there exist y, z ∈ S such that x = yz then
and
Conversely, let x, y ∈ S then by hypothesis, fS (xy) .
Also gS (xy) ∩ N ⊆ (gS (xy) ∩ N) ∪ M ⊆ (gS (xy) Hence 〈 (fS, gS); S〉 is (M, N)-DFS AG groupoid.
Definition 4.10. Let S be an AG-groupoid and 〈 (fA, gA); A〉 be a DFS-set of S over U. Then 〈 (fA, gA); A〉 is called (M, N)-double-framed soft left (resp. right) ideal (briefly, (M, N)-DFS left (resp. right)) ideal over U if fA (ab) ∪ M ⊇ fA (b) ∩ N (resp. fA (ab) ∪ M ⊇ fA (a) ∩ N) and gA (ab) ∩ N ⊆ gA (b) ∪ M (resp. gA (ab) ∩ N ⊆ gA (a) ∪ M) for all a, b ∈ A. A DFS-set 〈 (fA, gA); A〉 of S over U is called a (M, N)-double-framed soft two sided ideal (briefly, (M, N)-DFS two-sided ideal) of S over U if it is both a (M, N)-DFS left and a (M, N)-DFS right ideal of S over U.
Example 4.11. Consider the set S = {1, 2, 3, 4} with the following multiplication table
Consider the DFS-set 〈 (fS, gS); S〉 of S over with defined by and is (M, N)-DFS right ideal of S.
Theorem 4.12.Every (M, N)-DFS left (resp. right) ideal is (M, N)-DFS AG groupoid.
Proof. Straightforward.
Theorem 4.13.Let A be a nonempty subset of an AG-groupoid S. Then A is a left (resp. right) ideal of S if and only if the DFS-set is an (M, N)-DFS left (resp. right) ideal of S over U.
Proof. Straightforward.
Theorem 4.14.LA DFS set 〈 (fS, gS); S〉 of an AG-groupoid S over U is (M, N)-DFS left ideal of S if and only if XS◊ 〈 (fS, gS); S 〉 ⊑ [M,N] 〈 (fS, gS); S 〉.
Proof. Let 〈 (fS, gS); S〉 be an (M, N)-DFS left ideal of S. Let a ∈ S. If a is not product of any x, y ∈ S then and
If there exist x, y ∈ S such that a = xy then
and
Hence XS ◊ 〈 (fS, gS); S 〉 ⊑ [M,N] 〈 (fS, gS); S 〉.
Conversely, let x, y ∈ S and a = xy. Then
and
Hence 〈 (fS, gS); S〉 is (M, N)-DFS left ideal of S.
Theorem 4.15.RA DFS set 〈 (fS, gS); S〉 of an AG-groupoid S over U is (M, N)-DFS right ideal of S if and only if 〈 (fS, gS); S〉 ◊ XS ⊑ [M,N] 〈 (fS, gS); S 〉.
Proof. Same as Theorem 4.14.
If an AG groupoid contains left identity then every (M, N)-DFS right ideal of it becomes (M, N)-DFS left ideal. We prove it.
Theorem 4.16.If S is an AG-groupoid with left identity e then every (M, N)-DFS right ideal of S is (M, N)-DFS left ideal.
Proof. Let S be an AG-groupoid with left identity e and 〈 (fS, gS); S〉 is (M, N)-DFS right ideal of S. Then
and
Hence 〈 (fS, gS); S〉 is (M, N)-DFS left ideal.
Similar to Theorem 4.7, one can easily prove the following result.
Proposition 4.17.If 〈 (fS, gS); S〉 and 〈 (hS, kS); S〉 are (M, N)-DFS left (resp. right) ideals then their intersection 〈 (fS, gS); S〉 ⊓ 〈 (hS, kS); S 〉 is (M, N)-DFS left (resp. right) ideals.
Lemma 4.18.If S is an AG-groupoid with left identity e, then XS ◊ XS = [M,N]XS.
Proof. Let x be any element of S then x = ex, where e is the left identity of S, then we have
and
Hence, The other inclusion is obvious. Therefore
Proposition 4.19.If S is an AG-groupoid with left identity e, then for (M, N)-DFS right ideal 〈 (fS, gS); S〉 we have 〈 (fS, gS); S 〉 ◊ XS = [M,N] 〈 (fS, gS); S 〉.
Proof. Let a is any element of S then a = ea = (ee) a = (ae) e, where e is the left identity of S, we have
and
Therefore 〈 (fS, gS); S〉 ⊑ [M,N] 〈 (fS, gS); S 〉 ◊ XS. By Theorem 4.15, we have the required result.
It is proved earlier in Lemma 3.6. that the set of all double framed soft sets of S over U denoted by DFS (U) is (M, N)-DFS AG groupoid. An element 〈 (fS, gS); S〉 of DFS (U) is said to be idempotent if 〈 (fS, gS); S 〉 ◊ 〈 (fS, gS); S 〉 = [M,N] 〈 (fS, gS); S 〉.
Proposition 4.20.An idempotent (M, N)-DFS left ideal of an AG groupoid S is (M, N)-DFS right ideal.
Proof. Let 〈 (fS, gS); S〉 be a (M, N)-DFS left ideal of S over U which is idempotent. Since 〈 (fS, gS); S〉 is idempotent then we can write
and
then by Lemma 3.9 and Theorem 4.14,
and
Therefore, 〈 (fS, gS); S〉 is (M, N)-DFS right ideal of S over U and hence an (M , N)-DFS ideal.
Regular AG-groupoids
In this section, we discuss properties of (M, N)-DFS ideals in regular AG-groupoids and characterize regular AG-groupoids in terms of these ideals.
An AG-groupoid is said to be regular if for each a ∈ S, there exist an element x ∈ S such that a = (ax) a.
Proposition 5.1.An (M, N)-DFS right ideal of a regular AG-groupoid S over U is an (M, N)-DFS left ideal of S.
Proof. Let 〈 (fS, gS); S〉 be an (M, N)-DFS right ideal of a regular AG-groupoid S. Then for any a ∈ S, there exist an element x ∈ S such that a = (ax) a. Then
and
Hence 〈 (fS, gS); S〉 is (M, N)-DFS left ideal of S.
Proposition 5.2.An (M, N)-DFS right ideal of a regular AG-groupoid S over U is idempotent.
Proof. Let S be regular AG-groupoid and 〈 (fS, gS); S〉 be an (M, N)-DFS right ideal of S. Then for any a ∈ S, we have
and
Hence 〈 (fS, gS); S〉 ◊ 〈 (fS, gS) ; S 〉 ⊑ [M,N] 〈 (fS, gS) ; S〉. Next we prove the other inclusion. Since S is regular AG-groupoid so for any a ∈ S, there exist x ∈ S such that a = (ax) a, then
and
Hence 〈 (fS, gS); S〉 ⊑ [M,N] 〈 (fS, gS); S 〉 ◊ 〈 (fS, gS); S〉 and thus 〈 (fS, gS); S〉 is idempotent.
Proposition 5.3.If 〈 (fS, gS); S〉 is an (M, N)-DFS right ideal of a regular AG-groupoid S with left identity e then 〈 (fS, gS); S 〉 (ab) = [M,N] 〈 (fS, gS); S 〉 (ba) for all a, b ∈ S.
Proof. Let 〈 (fS, gS); S〉 be an (M, N)-DFS right ideal of a regular AG-groupoid S with left identity e. Let a, b ∈ S. Due to the regularity of S, there exist elements x, y ∈ S such that a = (ax) a and b = (by) b. Then ab = ((ax) a) ((by) b) = (ba) ((by) (ax)). Since 〈 (fS, gS); S〉 is (M, N)-DFS right ideal of S, thus
and
Hence 〈 (fS, gS); S〉 (ba) ⊑ [M,N] 〈 (fS, gS); S 〉 (ab). Similarly the inclusion 〈 (fS, gS); S 〉 (ab) ⊑[M,N] 〈 (fS, gS); S 〉 (ba) can be proved. Thus 〈 (fS, gS); S 〉 (ab) = [M,N] 〈 (fS, gS); S 〉 (ba).
Theorem 5.4.If S a regular AG-groupoid then for every (M, N)-DFS right ideal 〈 (fS, gS); S〉 and every (M, N)-DFS left ideal 〈 (hS, kS); S〉, the relation 〈 (fS, gS); S〉 ◊ 〈 (hS, kS); S 〉 = [M,N] 〈 (fS, gS); S 〉 ⊓ 〈 (hS, kS); S 〉 holds.
Proof. Since 〈 (fS, gS); S〉 is (M, N)-DFS right ideal and 〈 (hS, kS); S〉 is (M, N)-DFS left ideal of S over U then for any a ∈ S,
and
Similarly we can show that and Hence it is established that
〈 (fS, gS); S 〉 ◊ 〈 (hS, kS); S 〉 ⊑ [M,N] 〈 (fS, gS); S 〉 ⊓ 〈 (hS, kS); S 〉. Next, since S is regular so for a ∈ S, there exist x ∈ S such that a = (ax) a. Thus,
and
Thus 〈 (fS, gS); S〉 ⊓ 〈 (hS, kS); S 〉 ⊑[M,N]〈 (fS, gS); S 〉 ◊ 〈 (hS, kS); S 〉 and hence the required result follows.
A decision making scheme based on DFS-sets
Suppose a set of objects is under consideration. The properties of the objects are often called parameters. There could be situations where the parameters may be multi-valued. For the sake of argument, lets take a collection of books titled, ” Calculus & Analytic Geometry”. The properties of these books are the calculus contents and the geometry contents. The parameter, the calculus contents, is intended to be two-valued. i.e. Calculus contents means considering single variable calculus and several-variable calculus. Other parameter, the geometry contents of the book mean 2-dimensional geometry and 3-dimensional geometry. Situations, as exemplified above, where the parameters involved are two-valued, the concept of DFS-sets may be useful.
The concept of DFS-sets can be naturally extended to n-framed soft sets where the parameters are n-valued. We present a decision making scheme based on DFS-sets in the following.
According to the definition of a DFS-set, we can construct tabular form for every DFS-set for a finite universal set U and finite set of parameters E. Therefore let U = {u1, u2,…, um} and E = {e1, e2,…, en} and 〈 (αA, βA); A〉 be a DFS-set of A over U, then its tabular representation is given in Table 1 where,
〈 (αA, βA); A〉
(αA (e1), βA (e1))
(αA (e2), βA (e2))
⋯
(αA (en), βA (en))
u1
(a11, b11)
(a12, b12)
⋯
(a1n, b1n)
u2
(a21, b21)
(a22, b22)
⋯
(a2n, b2n)
⋮
⋮
⋮
⋱
⋮
um
(am1, bm1)
(am2, bm2)
⋯
(amn, bmn)
and
Example 6.1. Assume that U = {n1. n2, n3, n4, n5} is the set of five newspapers under consideration and let A = {e1, e2, e3} = {coverage, publication, quality} is the set of parameters. The parameters involved here are two-valued. i.e. e1 stands for coverage which means national news and international news, e2 stands for publication which means online version and print version, e3 stands for quality which means unbiased news and true news. Suppose Mr. X wants to subscribe monthly package of a newspaper. According to the data collected, the DFS-set 〈 (αA, βA); A〉 can be viewed as the collection of the following approximations:
The tabular representation of the DFS-set 〈 (αA, βA); A〉 is given in Table 2.
〈 (αA, βA); A〉
(αA (e1), βA (e1))
(αA (e2), βA (e2))
(αA (e3), βA (e3))
n1
(1, 1)
(1, 0)
(1, 1)
n2
(1, 1)
(1, 0)
(0, 1)
n3
(1, 1)
(1, 1)
(0, 0)
n4
(0, 0)
(0, 1)
(0, 0)
n5
(1, 0)
(0, 1)
(1, 0)
Choice values of an object
The choice values of an object ui ∈ U are and given by,
where aij and bij are the entries in the tabular form of a DFS-set as given in Table 1.
DFS-set based decision making
In this section, we will present an algorithm for decision making problems based on DFS-sets.
The following algorithm may be followed by Mr. X to subscribe a monthly package of the newspaper.
Algorithm.
1. Input the DFS-set.
2. Input the set A of choice parameters of Mr. X which is a subset of E.
3. Compute the choice values and for each ui ∈ U and find
4. Find k for which ck = max ci.
Then uk is the optimal choice object. If there the more than one values of k then any of them could be chosen by Mr. X whichever he likes.
Now we use the algorithm to solve a real-world problem.
Example 6.2. Reconsider Example 6.1 and let us apply the algorithm, we get Table 3 in which choice values are shown.
〈 (αA, βA); A〉
(αA (e1), βA (e1))
(αA (e2), βA (e2))
(αA (e3), βA (e3))
n1
(1, 1)
(1, 0)
(1, 1)
3
2
n2
(1, 1)
(1, 0)
(0, 1)
2
2
n3
(1, 1)
(1, 1)
(0, 0)
2
2
n4
(0, 0)
(0, 1)
(0, 0)
0
1
n5
(1, 0)
(0, 1)
(1, 0)
2
1
Here c1 = 3 +2 = 5, c2 = 2 +2 = 4, c3 = 2 +2 = 4, c4 = 0 +1 = 1, c5 = 2 +1 = 3. Hence c1 = max ci and so Mr. X should subscribe newspaper n1.
Weighted table of a DFS-set
Extending the idea of weighted soft sets given by Liu [19], we define weighted DFS-sets. We define a two-valued weight denoted by on each parameter ei with the property that . The entries of the weighted table of DFS-set 〈 (αA, βA); A〉 are defined as
where (aij, bij) are the entries in the tabular presentation of the DFS-set 〈 (αA, βA); A 〉.
Weighted choice values of an object
The weighted choice values of an object ui ∈ U are and given by,
where and and (aij, bij) are the entries in the tabular presentation of the DFS-set 〈 (αA, βA); A〉.
Weighted DFS set based decision making
In this section, we will present an algorithm for decision making problems based on DFS-sets.
After imposing weights on each parameter, Mr. X should follow the following algorithm to subscribe a monthly package of the newspaper.
1. Input the DFS-set.
2. Input the set A of choice parameters of Mr. X which is a subset of E.
3. Construct the weighted DFS-set.
4. Compute the choice values and for each ui ∈ U and find
5. Find k for which ck = max ci.
Then uk is the optimal choice object. If there the more than one values of k then any of them could be chosen by Mr. X whichever he likes.
Now we use the algorithm to solve a real-world problem.
Example 6.3. Reconsider Example 6.1 with the tabular representation given in Table 4. Suppose Mr. X puts weights w1, w2 and w3 on each of the parameters e1, e2 and e3 respectively. The weights are given below;
〈 (αA, βA); A〉
(αA (e1), βA (e1))
(αA (e2), βA (e2))
(αA (e3), βA (e3))
n1
(1, 1)
(1, 0)
(1, 1)
n2
(1, 1)
(1, 0)
(0, 1)
n3
(1, 1)
(1, 1)
(0, 0)
n4
(0, 0)
(0, 1)
(0, 0)
n5
(1, 0)
(0, 1)
(1, 0)
The tabular presentation of weighted DFS-set is given in Table 5. Hence c1 = 1.2 + 1.2 = 2.4, c2 = 0.7 + 1.2 = 1.9, c3 = 0.7 + 1.3 = 2, c4 = 0 +0.6 = 0.6, c5 = 0.8 + 0.6 = 1.4 and so Mr. X chooses the newspaper n1.
〈 (αA, βA); A〉
(αA (e1), βA (e1))
(αA (e2), βA (e2))
(αA (e3), βA (e3))
n1
(0.3, 0.7)
(0.4, 0)
(0.5, 0.5)
1.2
1.2
n2
(0.3, 0.7)
(0.4, 0)
(0, 0.5)
0.7
1.2
n3
(0.3, 0.7)
(0.4, 0.6)
(0, 0)
0.7
1.3
n4
(0, 0)
(0, 0.6)
(0, 0)
0
0.6
n5
(0.3, 0)
(0, 0.6)
(0.5, 0)
0.8
0.6
Before putting weights, we have c1 > c2 = c3 > c5 > c4. Suppose that Mr. X cannot subscribe n1 due to some reason. Then his next options are to choose between news papers n2, n3. But since c2 = c3, he is in confusion which one to subscribe. So to decide which newspaper suits him, he puts weights on each parameter and after putting weights, c1 > c3 > c2 > c5 > c4. It is now very clear that Mr. X should subscribe n3. Hence after assigning weights which means that by taking into account the importance of each parameter, the algorithm results in more suitable decision.
Conclusion
In this paper we developed theory of (M, N)-DFS ideals of AG-groupoids and discussed some of their properties in regular AG-groupoids. Some future work is given in the following points.
1. We shall extend the results of this study define (M, N)-DFS bi-ideals, (M, N)-DFS interior ideals and (M, N)-DFS quasi ideals of AG-groupoids, pseudo-BCI algebras (see [43, 44]) and neutrosophic triplet groups (see [45, 46]).
2. We shall combine DFS-set with fuzzy set and rough set to form some hybrid soft sets and develop some decision making methods like some developed in [21, 48].
3. Importantly, we will generalize DFS-set to n-framed soft set and develop decision making schemes.
Footnotes
Acknowledgements
The authors are highly grateful to the referees for their valuable comments and suggestions which were helpful in improving this paper, and to the Assoc. Editor of the journal Professor Xueling Ma for editing and communicating the paper.
References
1.
U.Acar, F.Koyuncu and B.Tanay, Soft sets and soft rings, Comput Math Appl59 (2010), 3458–3463.
2.
A.O.Atagun and A.Sezgin, Soft substructures of rings, fields and modules, Comput Math Appl61 (2011), 592–601.
3.
S.M.Anvariyeh, S.Mirvakili, O.Kazanci and B.Davvaz, Algebraic hyperstructures of soft sets associated to semi-hypergroups, Southeast Asian Bull of Math35 (2011), 911–925.
4.
N.Cagman and S.Enginoglu, FP-soft set theory and its applications, Ann Fuzzy Math Inform2 (2011), 219–226.
5.
N.Cagman and S.Enginoglu, Soft matrix theory and its decision making, Comput Math Appl59 (2010), 3308–3314.
A.Khan, Y.B.Jun and T.Mehmood, Generalized fuzzy interior ideals in Abel Grassmann's groupoids, Intl J Math Mathematical Sci (2010), Art. ID. 838392.
16.
M.Khan, F.Smarandache and S.Anis, Theory of abel grassmann's groupoids, ISBN 978-1-59973-347-0, Educational Publisher Columbus (2015).
17.
M.Khan, F.Smarandache and T.Aziz, Fuzzy abel grass-mann's groupoids, ISBN 978-1-59973-340-1,Educational Publisher Columbus (2015).
18.
D.V.Kovkov, V.M.Kolbanov and D.A.Molodtsov, Soft sets theory based optimization, J Computers and Sys Sciences Int46(6) (2007), 872–880.
19.
T.Y.Lin, A set theory for soft computing, a unified view of fuzzy sets via neighbourhoods, Proceedings of 1996 IEEE Intl Conf of Fuzzzy Systems, New Orleans,1996, pp. 1140–1146.
20.
X.Ma and J.Zhan, Applications of a new soft set to h-hemiregularhemirings via(M, N)-SI-h-ideals, J Intell Fuzzzy Syst26 (2014), 2515–2525.
21.
X.Ma, J.Zhan, M.I.Ali and N.Mehmood, A survey of decision making methods based on two classes of hybrid soft set models, Artificial Intelligence Review49(4) (2018), 511–529.
22.
X.Ma, Q.Liu and J.Zhan, A survey of decision making methods based on certain hybrid soft set models, Artificial Intelligence Review47 (2017), 507–530.
23.
P.K.Maji, R.Biswas and A.R.Roy, An application of soft sets in a decision making problem, Comput Math Appl44 (2002), 1077–1083.
24.
P.K.Maji, R.Biswas and A.R.Roy, Soft set theory, Comput Math Appl45 (2003), 555–562.
25.
D.Molodtsov, Soft set theory - First results, Comput Math Appl37 (1999), 19–31.
26.
Q.Mushtaq and S.M.Yusuf, On LA-semigroups, The Ali-garh Bulletin of Mathematics8 (1978), 65–70.
27.
M.Naseeruddin, Some studies on almost semigroups and flocks, PhD Thesis,The Aligarh Muslim University India, 1970.
28.
P.V.Protic and N.Stevanovic, On Abel-Grassmann's groupoids (review), Proceeding of Mathematics Conference in Pristina, 1999, pp. 31–38.
29.
A.R.Roy and P.K.Maji, A fuzzy soft set theoretic approach to decision making problems, J Computational and Applied Mathematics203 (2007), 412–418.
30.
A.S.Sezer, A new approach to LA-semigroup theory via the soft sets, J Intell and Fuzzy Sys26 (2014), 2483–2495.
31.
A.S.Sezer, Certain characterizations of LA-semigroups by soft sets, J Intell and Fuzzy Sys27(2) (2014), 1035–1046.
32.
Z.Xiao, K.Gong and Y.Zou, A combined forecasting approach based on fuzzy soft sets, J Comput Appl Math228(1) (2009), 326–333.
33.
F.Yousafzai, A.Khan, V.Amjad and A.Zeb, On fuzzy fully regular AG-groupoids, J Intell & Fuzzy Syst26 (2014), 2973–2982.
34.
L.A.Zadeh, Fuzzy sets, Information and Control8 (1965), 338–353.
35.
J.Zhan, W.A.Dudek and J.Neggers, A new soft union set: Characterizations of hemirings, Int J Mach Learn & Cyber DOI 10.1007/s13042-015-0343-8.
36.
J.Zhan and Q.Wang, Certain types of soft coverings based rough sets with applications, Int J Mach Learn Cybern.DOI 10.1007/s13042-018-0785-x
37.
J.Zhan and J.C.R.Alcantud, A novel type of soft rough covering and its application to multicriteria group decision making, Artificial Intelligence Review (2018). DOI: 10.1007/s10462-018-9617-3
38.
J.Zhan and J.C.R.Alcantud, A survey of parameter reduction of soft sets and corresponding algorithms, Artificial Intelligence Review (2018). DOI: 10.1007/s10462-017-9592-0
39.
J.Zhan, Q.Liu and T.Herawan, A novel soft rough set: Soft rough hemirings and its multicriteria group decision making, Applied Soft Computing54 (2017), 393–402.
40.
J.Zhan, M.I.Ali and N.Mehmood, On a novel uncertain soft set model: Z-soft fuzzy rough set model and corresponding decision making methods, Applied Soft Computing56 (2017), 446–457.
41.
J.Zhan and K.Zhu, A novel soft rough fuzzy set: Z-soft rough fuzzy ideals of hemirings and corresponding decision making, Soft Computing21 (2017), 1923–1936.
42.
J.Zhan and W.Xu, Two types of coverings based multi granulation rough fuzzy sets and applications to decision making, Artificial Intelligence Review (2018).
43.
X.H.Zhang, Fuzzy anti-grouped filters and fuzzy normal filters in pseudo-BCI algebras, J Intell Fuzzy Syst33 (2017), 1767–1774.
44.
X.H.Zhang, C.Park and S.P.Wu, Soft set theoretical approach to pseudo-BCI algebras, J Intell Fuzzy Syst34 (2018), 559–568.
45.
X.H.Zhang, C.X.Bo, F.Smarandache and C.Park, New operations of totally dependent-neutrosophic sets and totally dependent-neutrosophic soft sets, Symmetry10(6) 2018.
46.
X.H.Zhang, F.Smarandache and X.L.Liang, Neutrosophic duplet semi-group and cancellable neutrosophic triplet groups, Symmetry9 (2017), doi: 10.3390/sym9110275
47.
L.Zhang and J.Zhan, Novel classes of fuzzy soft ß-coverings-based fuzzy rough sets with applications to multicriteria fuzzy group decision making, Soft Computing (2018).
48.
L.Zhang and J.Zhan, Fuzzy soft ß-covering based fuzzy rough sets and corresponding decision-making applications, Int J Mach Learn Cybern (2018). 10.1007/s13042-018-0828-3
49.
Y.Zou and Z.Xiao, Data analysis approaches of soft sets under incomplete information, Knowledge-Based Systems21(8) (2008), 941–945.