GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment by Ioan Degău

Neutrosophic Sets and Systems, Vol. 15, 2017

University of New Mexico

GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment Durga Banerjee1, Bibhas C. Giri2, Surapati Pramanik3, and Florentin Smarandache4 1

Ranaghat Yusuf Institution, P. O. Ranaghat,Dist. Nadia, West Bengal, Pin Code-741201, India. E-mail: dbanerje3@gmail.com Department of Mathematics, Nandalal Ghosh B.T. College, Panpur, PO-Narayanpur, and District: North 24 Parganas, Pin Code: 743126, West Bengal, India. Email: sura_pati@yahoo.co.in 3 Department of Mathematics, Jadavpur University, West Bengal, India, Pin Code-700032, Email:bcgiri.jumath@gmail.com 4 University of New Mexico. Mathematics & Science Department, 705 Gurley Ave., Gallup, NM 87301, USA. Email: smarand@unm.edu 2

Abstract. In this paper, multi attribute decision making problem based on grey relational analysis in neutrosophic cubic set environment is investigated. In the decision making situation, the attribute weights are considered as single valued neutrosophic sets. The neutrosophic weights are converted into crisp weights. Both positve and negative GRA coefficients, and weighted GRA coefficients are determined.

Hamming distances for weighted GRA coefficients and standard (ideal) GRA coefficients are determined. The relative closeness coefficients are derived in order to rank the alternatives. The relative closeness coefficients are designed in ascending order. Finally, a numerical example is solved to demonstrate the applicability of the proposed approach.

Keywords: Grey relational coefficient, interval valued neutrosophic set, multi attribute decision making, neutrosophic set, neutrosophic cubic set, relative closeness coefficient

1 Introduction In management section, banking sector, factory, plant multi attribute decision making (MADM) problems are to be extensively encountered. In a MADM situation, the most appropriate alternative is selecting from the set of alternatives based on highest degree of acceptance. In a decision making situation, decision maker (DM) considers the efficiency of each alternative with respect to each attribute. In crisp MADM, there are several approaches [1, 2, 3, 4, 5] in the literature. The weight of each attribute and the elements of decision matrix are presented by crisp numbers. But in real situation, DMs may prefer to use linguistic variables like ‘good’, ‘bad’, ‘hot’, ‘cold’, ‘tall’, etc. So, there is an uncertainty in decision making situation which can be mathematically explained by fuzzy set [6]. Zadeh [6] explained uncertainty mathematically by defining fuzzy set (FS). Bellman and Zadeh [7] studied decision making in fuzzy environment. Atanassov [8, 9] narrated uncertainty by introducing non-membership as independent component and defined intuitionistic fuzzy set (IFS). Degree of indeterminacy (hesitency) is not independent . Later on DMs have recognized that indeterminacy plays an important role in decision making. Smarandache [10] incorporated indeterminacy as independent component and developed neutrosophic set (NS) and together with Wang et a. [11] defined single valued neutrosophic set (SVNS) which is an instance of neutrosophic set. Ye [12] proposed

a weighted correlation coefficients for ranking the alternatives for multicriteria decision making (MCDM). Ye [13] established single valued neutrosophic cross entropy for MCDM problem. Sodenkamp [14] studied multiple-criteria decision analysis in neutrosophic environment. Mondal and Pramanik [15] defined neutrosophic tangent similarity measure and presented its application to MADM. Biswas et al. [16] studied cosine similarity measure based MADM with trapezoidal fuzzy neutrosophic numbers. Mondal and Pramanik [17] presented multi-criteria group decision making (MCGDM) approach for teacher recruitment in higher education. Mondal and Pramanik [18] studied neutrosophic decision making model of school choice. Liu and Wang [19] presented MADM method based on singlevalued neutrosophic normalized weighted Bonferroni mean. Biswas et al. [20] presented TOPSIS method for MADM under single-valued neutrosophic environment. Chi and Liu [21] presented extended TOPSIS method for MADM on interval neutrosophic set. Broumi et al. [22] presented extended TOPSIS method for MADM based on interval neutrosophic uncertain linguistic variables. Nabdaban and Dzitac [23] presented a very short review of TOPSIS in neutrosophic environment. Pramanik et al. [24] studied hybrid vector similarity measures and their applications to MADM under neutrosophic environment. Biswas et al. [25] presented triangular fuzzy neutrosophic set information and its application to MADM. Sahin and Liu [26] studied

Durga Banerjee, Bibhas C. Giri, Surapati Pramanik, Florentin Smarandache, GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment

Neutrosophic Sets and Systems, Vol. 15, 2017

maximizing deviation method for neutrosophic MADM with incomplete weight information. Ye [27] studied bidirectional projection method for MADM with neutrosophic numbers of the form a + bI, where I is characterized by indeterminacy. Biswas et al. [28] presented value and ambiguity index based ranking method of single-valued trapezoidal neutrosophic numbers and its application to MADM. Dey et al. [29] studied extended projection-based models for solving MADM problems with interval-valued neutrosophic information. Deng [30, 31] studied grey relational analysis (GRA). Pramanik and Mukhopadhyaya [32] developed GRA based intuitionistic fuzzy multi criteria decision making (MCDM) approach for teacher selection in higher education. Dey et al. [33] established MCDM in intuitionistic fuzzy environment based on GRA for weaver selection in Khadi institution. Rao, and Singh [34] established modified GRA method for decision making in manufacturing situation. Wei [35] presented GRA method for intuitionistic fuzzy MCDM. Biswas et al. [36] studied GRA method for MADM under single valued neutrosophic assessment based on entropy. Dey et al. [37] presented extended GRA based neutrosophic MADM in interval uncertain linguistic setting. Pramanik and K. Mondal [38] employed GRA for interval neutrosophic MADM and presented numerical examples. Several neutrosophic hybrid sets have been recently proposed in the literature, such as neutrosophic soft set proposed by Maji [39], single valued soft expert set proposed by Broumi and Smarandache [40], rough neutrosophic set proposed by Broumi, et al. [41], neutrosophic bipolar set proposed by Deli et al. [42], rough bipolar neutrosophic set proposed by Pramanik and Mondal [43], neutrosophic cubic set proposed by Jun et al. [44] and Ali et al. [45]. Jun et al. [44] presented the concept of neut-rosophic cubic set by extending the concept of cubic set proposed by Jun et al. [46] and introduced the notions of truth-internal (indeterminacy-internal, falsity-internal) neut-rosophic cubic sets and truth-external (indeterminacy-external, falsity-external) and investigated related properties. Ali et al. [45] presented concept of neutrosophic cubic set by extending the concept of cubic set [46] and defined internal neutrosophic cubic set (INCS) and external neutrosophic cubic set (ENCS). In their study, Ali et al.[45] also introduced an adjustable approach to neutrosophic cubic set based decision making. GRA based MADM/ MCDM problems have been proposed for various neutrosophic hybrid environments [47, 48, 49, 50]. MADM with neutrosophic cubic set is yet to appear in the literature. It is an open area of research in neutrosophic cubic set environment. The present paper is devoted to develop GRA method for MADM in neutrosophic cubic set environment. The attribute weights are described by single valued neutrosophic sets. Positive and negative grey relational coefficients are determined. We define ideal grey relational coefficients and relative closeness coefficients in neutrosophic cubic set

environment. The ranking of alternatives is made in descending order. The rest of the paper is designed as follows: In Section 2, some relevant definitions and properties are recalled. Section 3 presents MADM in neutrosophic cubic set environment based on GRA. In Section 4, a numerical example is solved to illustrate the proposed approach. Section 5 presents conclusions and future scope of research. 2 Preliminaries In this section, we recall some established definitions and properties which are connected in the present article. 2.1 Definition (Fuzzy set) [6] Let W be a universal set. Then a fuzzy set F over W can be defined by F={<w,  F ( w)  : w ∈W} where  F ( w ) : W  [0, 1]is called membership function of F and  F ( w ) is the degree of membership to which w  F. 2.2 Definition (Interval valued fuzzy set) [52] Let W be a universal set. Then, an interval valued fuzzy set F over W is defined by F = {[ F (w), F (w)] / w : w  W}, where F (w ) and F ( w ) are referred to as the lower and upper degrees of membership w ∈W where 0 ≤ F (w ) + F ( w ) ≤ 1, respectively. 2.3 Definition (Cubic set) [46] Let W be a non-empty set. A cubic set C in W is of the form c = { w, F(w ), ( w )) / w  W} where F is an interval valued fuzzy set in W and  is a fuzzy set in W. 2.4 Definition (Neutrosophic set (NS)) [10] Let W be a space of points (objects) with generic element w in W. A neutrosophic set N in W is denoted by N= {< w: TN(w), IN(w), FN(w)>: w  W} where TN, IN, FN represent membership, indeterminacy and non-membership function respectively. TN, IN, FN can be defined as follows: 

TN : W →] 0, 1+ [ 

I N : W →] 0, 1+ [ 

FA : W →] 0, 1+ [

Here, TN(w), IN(w), FN(w) are the real standard and non standard subset of ] 0, 1+ [ and 

0 ≤ TN(w)+IN(w)+FN(w) ≤ 3+.

2.5 Definition (Complement of neutrosophic set) [10] The complement of a neutrosophic set N is denoted by Nand defined as

Durga Banerjee, Bibhas C. Giri, Surapati Pramanik, Florentin Smarandache, GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment

Neutrosophic Sets and Systems, Vol. 15, 2017

N= {<w: TN(w), IN(w), FN(w)>, w  W }

1 n  ( TP ( w i )  TQ ( w i )  I P ( w i )  I Q ( w i )  FP ( w i )  FQ ( w i ) ) 3n i 1

TN(w) = {1+}- TN(w) IN(w) = {1+} -IN(w)

2. 11 Deﬁnition (Interval neutrosophic set) [51]

FN(w) = {1+} - FN(w)

Let W be a non-empty set. An interval neutrosophic set (INS) P in W is characterized by the truth-membership function PT, the indeterminacy-membership function PI and the falsity-membership function PF. For each point w ∈ W, PT(w), PI(w),PF(w))⊆[0,1]. Here P can be presented as follows:

2.6 Definition (Containment) [10, 20] A neutrosophic set P is contained in the other neutrosophic set Q, P ⊆ Q, if and only if inf (TP ) ≤ inf (TQ),sup (TP ) ) ≤ sup ((TQ),

P ={< w, [PTL ( w ), PTU ( w )] , [PIL ( w ), PIU ( w )] ,

inf (IP )  inf (IQ),sup (IP ) )  sup ((IQ).

[PFL ( w ), PFU ( w )] > :w ∈W}.

inf (FP )  inf (FQ),sup (FP ) )  sup ((FQ).

2.12 Deﬁnition (Neutrosophic cubic set) [44, 45]

2.7 Definition (Union) [10] The union of two neutrosophic sets P and Q is a neutrosophic set R, written as R = P ∪ Q, whose truthmembership, indeterminacy-membership and falsity membership functions are related to those of P and Q by TR(w) = TP(w) + TQ(w) – TP(w) ×TQ(w),

an interval neutrosophic set in W and



  w ,  T ( w ),  I ( w ),  F ( w ) / w  W

 is a neutrosophic set

in W. 3 GRA for MADM in neutrosophic cubic set environment

IR(w) = IP(w) + IQ(w) – IP(w) × IQ(w), FR(w) = FP(w) + FQ(w) – FP(w) × FQ(w), for all w  W. 2.8 Deﬁnition (Intersection) [10] The intersection of two neutrosophic sets P and Q is a neutrosophic set C, written as R =P∪Q, whose truthmembership, indeterminacy-membership and falsitymembership functions are related to those of P and Q by TR(w) = TP(w) ×TQ(w), IR(w) = IP(w) × IQ(w),

We consider a MADM problem with r alternatives {A1, A2, …, Ar} and s attributes {C1, C2, …, Cs}. Every attribute is not equally important to decision maker. Decision maker provides the neutrosophic weights for each attribute. Let W  w 1 , w 2 , ..., w s T be the neutrosophic weights of the attributes. Step 1 Construction of decision matrix Step1.The decision matrix (see Table 1) is constructed as follows: Table 1: Decision matrix

FR(w) = FP(w) × FQ(w), for all w  W. 2.9 Deﬁnition (Hamming distance) [20, 53] Let P   w i : TP ( w i ), I P (w i ), FP (w i ), i 1, 2, ...,n and Q   w i : TP ( w i ), I Q ( w i ), FQ ( w i ) , i 1, 2, ...,n be any two neutrosophgic sets. Then the Hamming distance between P and Q can be defined as follows: d (P, Q) =

Let W be a set. A neutrosophic cubic set (NCS) in W is a pair (P, ) where P = { w , PT ( w ), PI ( w ), PF ( w ) / w  W} is

A  (aij )r  s C1 C2    A1 (A11 , 11 ) (A12 , 12 )  A 2 (A 21 ,  21 ) (A 22 ,  22 )  . . .  . . .  . . . A  r (A r1 ,  r1 ) (A r 2 ,  r 2 )

... Cs   ... (A1s , 1s )  ... (A 2s ,  2s )   . .  . .  . . ... (A rs ,  rs )  r s

 ( TP ( w i )  TQ ( w i )  I P ( w i )  I Q ( w i )  FP ( w i )  FQ ( w i ) )

Here a ij  ( A ij ,  ij ) , A ij  [TijL , TijU ],[I ijL , I ijU ],[FijL , FijU ] ,  ij  (Tij , I ij , Fij ) , a i j means the rating of alternative Ai with

2.10 Deﬁnition (Normalized Hamming distance)

respect to the attribute Cj. Each weight component w j of attribute C j has been taken as neutrosophic set and

(1)

i 1

The normalized Hamming distance between two SVNSs, A and B can be defined as follows: N d (P, Q)

(2)

w j  (T j , I j , Fj ) , A ij  [TijL , TijU ],[I ijL , I ijU ],[FijL , FijU ]

are interval neutrosophic set and  ij  (Tij , I ij , Fij ) is a neutrosophic set.

Durga Banerjee, Bibhas C. Giri, Surapati Pramanik, Florentin Smarandache, GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment

Neutrosophic Sets and Systems, Vol. 15, 2017

and defined as M j  Tjm  , I mj  , Fjm   where Tj

m

Step 2 Crispification of neutrosophic weight set Let w j  T j , I j , Fj  be the j – th neutrosophic weight for the attribute C j . The equivalent crisp weight of C j is defined as follows:

I mj   max I ijm , Fjm   max Fijm and i

Tj2  I 2j  Fj2 n

2 2 2  Tj  I j  Fj

Step 3 Conversion of interval neutrosophic set into neutrosophic set decision matrix In the decision matrix (1), each Aij  [TijL , TijU ],[IijL , IijU ],[FijL , FijU ] is an INS. Taking mid value of each interval the decision matrix reduces to single valued neutrosophic decision matrix (See Table 2).





Table 2: Neutrosophic decision matrix M  (mij )r s C1 C2    A1 (M11 , 11 ) (M12 , 12 )  A 2 (M 21 ,  21 ) (M 22 ,  22 )  . . .  . . .  . . . A  r (M r1 ,  r1 ) (M r 2 ,  r 2 )

... Cs   ... (M1s , 1s )  ... (M 2s ,  2s )   . .  . .  . . ... (M rs ,  rs )  r s

where each m ij  M ij ,  ij  and  TijL  TijU I ijL  I ijU FijL  FijU Mij   , ,  2 2 2 

   T m , I m , F m . ij ij ij  

,    [M1 , 1 , M2 , 2 , ...,Mq , q ]

and defined as

m j

 min I , F

m j

m ij





Tj , I j , Fj

, where Tj  max Tijm ,

 min F and i

 j

m ij

  (T , I , F )  j

 j

sophic cubic decision matrix M (m = 1, 2, ..., s.

 j

, i = 1,2,...,r and j ij )pq

Step 4.2 Definition: The ideal neutrosophic estimates unreliability solution (INEURS) can be denoted as

M



 



ij )rs

, i = 1,2,...,r and j = 1, 2,

 

,   M1 , 1 , M2 , 2 , ..., Ms , s



Step 4.3 Definition: The grey relational coefficients of each alternative from INERS can be defined as:



 ij

,  ij 

 min min  ij   max max  ij min min  ij   max max  ij  i j i j i j i j  ,             max max ij max max ij  ij ij i j i j  Here,  ij  d (M j , M ij )



   Tjm  Tijm  I m  I ijm  Fjm  Fijm j i 1





and  ij  dj ,  ij    Tj  Tij  I j  I ij  Fj  Fij r

i 1

,

i = 1, 2 ,..., r and j = 1, 2, ..., s,  [ 0,1] . We call ij ,  ij  as positive grey relational coeffi-

The grey relational coefficient of each alternative from INEURS can be defined as:



 ij



, ij 

 min min  ij   max max  ij min min  ij   max max  ij j i j i j i j  i ,      ij   max    max  ij max max  ij ij i j i j 

Here, r

where T  max Tij , I  min I ij , F  min Fij in the neutro j

 j

Step 4.4 Definition:

Step 4.1 Definition: The ideal neutrosophic estimates reliability solution (INERS) can be denoted as M j

 j

cient.

Step 4 Some definitions of GRA method for MADM with NCS The GRA method for MADM with NCS can be presented in the following steps:



 j

j 1

j1

M

cubic decision matrix M (m ...,s.

and  w cj 1 .

  (T , I , F ) where  j

Tj  min Tij , I j  max I ij , Fj  max Fij in the neutrosophic i

w cj 

 min Tijm ,



 ij  d (M j , M ij )   Tjm  Tijm  I mj  I ijm  Fjm  Fijm i 1



and:



 ij  dj ,  ij    Tj  Tij  I j  I ij  Fj  Fij r

i 1

 , i = 1,

2,..., r and j = 1, 2, ..., s,  [ 0,1] . We call  ij ,  ij  as negative grey relational coefficient.

 is called distinguishable coefficient or identification coefficient and it is used to reflect the range of comparison environment that controls the level of differences of the grey relational coefficient.   0 indicates comparison environment disappears and  1 indicates comparison environment is unaltered. Generally,   0.5 is assumed for decision making.

Durga Banerjee, Bibhas C. Giri, Surapati Pramanik, Florentin Smarandache, GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment

   

    

Neutrosophic Sets and Systems, Vol. 15, 2017

Step 4.5 Calculation of weighted grey relational coefficients for MADM with NCS We can construct two r  s order matrices namely

 ,    ij

 M GR 



 ij rs



 and M GR  ij , ij



r s

. The crisp weight is

 to be multiplied with the corresponding elements of M GR   and M GR to obtain weighted matrices W M GR and W

 M GR and defined as:

w  , w    ~ , ~  ~ ~ M  w  , w     ,   c  j ij

 M GR 

and

c  j ij rs

c  j ij

 GR

 ij

 ij rs

c  j ij rs

 ij

 ij   max max  ij min min i j i

   max max  min min i j i

   max max  i

 ij

1

Again, from the definition, we can write: i

   max max  ij 

  Now, min min  ij   ij j

 min min  ij   max max  ij   ij   max max  ij i

   ij

 ij   max max  ij i

Theorem 2

 ij   max max  ij min min i j

  1 .  ij

   max max  min min i j  ij

 ij

 ij   max max  ij i

 ij 1

Again, from the definition

   ij

 ij   max max  ij min min i j i

 ij   max max  ij i

 Now, min min    ij i

 ij

 min min    max max  ij   ij   max max  ij j

 ij

   max max  min min i j  ij

 ij

   max max  ij  ij

Note 1: i. Since ij 1 , w cj  1 then ij w cj 1  ~ij 1 ii. Since ij 1 , w cj  1 then ij w cj 1  ~ij 1

Step 4.7 We define the ideal or standard grey relational coefficient as (1, 1). Then we construct ideal grey relational coefficient matrix of order r  s (see Table 3). Table 3: Ideal grey relational coefficient matrix of order r  s

 ij   max max  ij min min i j

iv. Since  ij 1 , w cj  1 then  ij w cj 1  ~ij 1

  1

 ij

  min Now, min i j

iii. Since  ij 1 , w cj  1 then  ij w cj 1  ~ij 1

 ij

 ij 

 ij

  1 .

 min min  ij   max max  ij   ij   max max  ij  ij

 ij

 ij   ij min Now, min i j j

   max max 

  ij 

 ij   max max  ij

 ij

 ij



 

ij 

Proof: From the definition

 ij   max max  ij min min i j

 ij

 ij rs

Theorem 1 The positive grey relational coefficient is less than unity i.e. ij  1, and ij  1 .

 

Proof: From the definition, we can write

 min min  ij   max max  ij   ij   max max  ij

Step 4.6 From the definition of grey relational coefficient, it is clear that grey relational coefficients of both types must be less than equal to one. This claim is going to be proved in the following theorems.

 ij

The negative grey relational coefficient is less than unity i.e. ij  1,  ij  1 .

 1,1 1,1... 1,1     1,1 1,1... 1,1  I  .......... .........     1,1 1,1... 1,1    rs

Step 5 Determination of Hamming distances We find the distance d i between the corresponding el ements of i-th row of I and W M GR by employing Hamming

Durga Banerjee, Bibhas C. Giri, Surapati Pramanik, Florentin Smarandache, GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment

Neutrosophic Sets and Systems, Vol. 15, 2017

distance. Similarly, d i can be determined between I and  by employing Hamming distance as follows: M GR W

 

1 s ~  1 ~ [  1  ij ij 2s j1 1 s ~  1 ~ d i  [  1   ij ij 2s j1

d i 

 0.85 0.95 0.05 0.15       0.65 0 0.7 0.25   0.05 0.15 0.25 0.45    

] , i = 1, 2, …, r. ] , i = 1, 2, …, r.

The    ( ij )  ( dj ,  ij )  i, j is presented as below:

 0.45 1.2 0.4 0.15       0.05 0.5 0.2 0.2   0.25 0.3 0.2 0.5   

Step 6 Determination of relative closeness coefficient The relative closeness coefficient can be calculated as: i 

d i d  d i  i



i = 1, 2, ..., r.

   ( ij )  ( d (M j , M ij ))  i, j is presented as below:

Step 7 Ranking the alternatives According to the relative closeness coefficient, the ranking order of all alternatives is determined. The ranking order is made according to descending order of relative closeness coefficients.

 0.25 0.3 0.7 0.55       0.45 1.2 0 0.45  1.05 0.65 0.6 0.25    

The    ( ij )  ( dj ,  ij )  i, j is presented as:

4 Numerical example

 The positive grey relational coefficient M GR    ,  ij 34 is presented in the Table 7, at the end of article.

Consider a hypothetical MADM problem. The problem consists of single decision maker, three alternatives with three alternatives {A1, A2, A3} and four attributes {C1, C2, C3, C4}. The solution of the problem is presented using the following steps:

 The negative grey relational coefficient M GR  ij ,  ij 34 is presented in the Table 8, at the end of article.

Step 1. Construction of neutrosophic cubic decision matrix

Now, we multiply the crisp weight with the corresponding    elements of M GR and M GR to get weighted matrices W M GR

 ij



and W M GR and which are described in the Table 9 and 10 respectively, at the end of article.

The decison maker forms the decision matrix which is displayed in the Table 4, at the end of article. Step 2. Crispification of neutrosophic weight set

Step 5 Determination of Hamming distances

The neutrosophic weights of the attributes are taken as:

Hamming distances are calculated as follows:

W  (0.5, 0.2, 0.1), (0.6, 0.1, 0.1), (0.9, 0.2, 0.1), (0.6, 0.3, 0.4)

d 1  0.84496, d 1  0.83845625,

The equivalent crisp weights are

d 2  0.82444375, d 2  0.85328875 ,

W  (0.1907), (0.2146), (0.3228), (0.2719) c

Step 3 Conversion of interval neutrosophic set into neutrosophic set in decision matrix Taking the mid value of INS in the Table 4, the new decision matrix is presented in the following Table 5, at the end of article. Step 4 Some Definitions of GRA method for MADM with NCS The ideal neutrosophic estimates reliability solution (INERS) M ,   and the ideal neutrosophic estimates unreliability solution (INEURS) M  ,   are presented in the Table 6, at the end of article.    ( ij )  ( d (M j , M ij ))  i, j is presented as below:

d 3  0.82368675, d 3  0.85277.

Step 6 Determination of relative closeness coefficient The relative closeness coefficients are calculated as: 1 

d 1  0.501932 d 1  d 1

2 

d 2  0.491403576 d  d 2

3 

d 3  0.49132 d  d 3

 2

 3

Step 7 Ranking the alternatives The ranking of alternatives is made according to descending order of relative closeness coefficients. The ranking order is shown in the Table 11 below.

Durga Banerjee, Bibhas C. Giri, Surapati Pramanik, Florentin Smarandache, GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment

Neutrosophic Sets and Systems, Vol. 15, 2017

Alternatives A3 A2 A1

Ranking order 1 2 3

Conclusion This paper develops GRA based MADM in neutrosophic cubic set environment. This is the first approach of GRA in MADM in neutrosophic cubic set environment. The proposed approach can be applied to other decision making problems such as pattern recognition, personnel selection, etc. The proposed approach can be applied for decision making problem described by internal NCSs and external NCSs. We hope that the proposed approach will open up a new avenue of research in newly developed neutrosophic cubic set environment. References [1]

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[29] P. P. Dey, S. Pramanik, and B.C. Giri. Extended projectionbased models for solving multiple attribute decision making problems with interval –valued neutrosophic information. In: New Trends in Neutrosophic Theory and Applications, eds. F. Smarandache and S. Pramanik, Pons Editions, Brussels, 2016, 127- 140. [30] J. L. Deng. Introduction to grey system theory. The Journal of Grey System, 1(1) (1989), 1–24. [31] J. L. Deng. The primary methods of grey system theory, Huazhong University of Science and Technology Press, Wuhan, (2005). [32] S. Pramanik, and D. Mukhopadhyaya. Grey relational analysis based intuitionistic fuzzy multi criteria group decision making approach for teacher selection in higher education. International Journal of Computer Applications, 34 (10) (2011), 21 – 29. [33] P.P. Dey, S. Pramanik, and B.C. Giri, Multi-criteria group decision making in intuitionistic fuzzy environment based on grey relational analysis for weaver selection in Khadi institution. Journal of Applied and Quantitative Methods, 10(4) (2015), 1-14. [34] R. V. Rao, and D. Singh. An improved grey relational analysis as a decision making method for manufacturing situations. International Journal of Decision Science, Risk and Management, 2(2010), 1–23. [35] G. W. Wei. Grey relational analysis method for intuitionistic fuzzy multiple attribute decision making. Expert Systems with Applications, 38(2011), 11671-11677. [36] P. Biswas, S. Pramanik, and B.C. Giri. Entropy based grey relational analysis method for multi-attribute decision – making under single valued neutrosophic assessments. Neutrosophic Sets and Systems, 2 (2014), 102 – 110. [37] P.P. Dey, S. Pramanik, and B.C. Giri. An extended grey relational analysis based multiple attribute decision making in interval neutrosophic uncertain linguistic setting. Neutrosophic Sets and Systems, 11 (2016), 21-30. [38] S. Pramanik, and K. Mondal. Interval neutrosophic multi-attribute decision-making based on grey relational analysis. Neutrosophic Sets and Systems, 9(2015), 13-22. [39] P. K. Maji. Neutrosophic soft set. Annals of fuzzy Mathematics and Informatics, 5 (1) (2013), 157 – 168. [40] S. Broumi, and F. Smarandache. Single valued neutrosophic soft expert sets and their application in decision making. Journal of New Theory, 3 (2015), 67 – 88.

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Received: February 1, 2017. Accepted: February 20, 2017.

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Neutrosophic Sets and Systems, Vol. 15, 2017

Table 4: Construction of neutrosophic cubic decision matrix A  (aij )34 

  A  1   A2      A3 

C1 (([0.2, 0.3], [0.3, 0.5], [0.2, 0.5]), (0.3, 0.2, 0.3))

C2 ([0.1, 0.3], [0.2, 0.4], [0.3, 0.6]), (0.2, 0.5, 0.4))

C3 (([0.6, 0.9], [0.1, 0.2], [0, 0.2]), (0.4, 0.5, 0.1))

([0.6, 0.8], [0.4, 0.6], [0.3, 0.7]), (0.5, 0.2, 0.1))

([0.7, 0.9], [0.2, 0.3], [0.1, 0.3]), (0.7, 0.3, 0.3))

(([0.5, 0.7], [0.4, 0.6], [0.3, 0.5]), (0.4, 0.1, 0.2))

(([0.4, 0.9], [0.1, 0.4], [0, 0.2]), (0.25, 0.15, 0.1))

. (([0.8, 0.9], [0.4, 0.7], [0.4, 0.6]), (0.8, 0.1, 0.2))

(([0.6, 0.9], [0.1, 0.3], [0, 0.3]), (0.5, 0.4, 0.3))

C4  (([0.4, 0.7], [0.1, 0.3], [0.2, 0.3]),   (0.7, 0.3, 0.2))   (([0.4, 0.5], [0.1, 0.3], [0.2, 0.3]),   (0.6, 0.2, 0.1))   (([0.6, 0.8], [0.5, 0.7], [0.2, 0.4]),   (0.5, 0.1, 0.4)) 

Table 5: Construction of neutrosophic decision matrix M  (mij )34 

   A1   A2   A  3

C4   ((0.55, 0.2, 0.25), (0.7, 0.3, 0.2))   ((0.7,0.5, 0.5), (0.5, 0.2, 0.1)) ((0.8, 0.25, 0.2), (0.7, 0.3, 0.3)) ((0.6, 0.5, 0.4), (0.4, 0.1, 0.2))  ((0.45, 0.2, 0.25), (0.6, 0.2, 0.1))  .  ((0.65, 0.25, 0.1), (0.25, 0.15, 0.1)) ((0.85, 0.55, 0.5), (0.8, 0.1, 0.2)) ((0.75, 0.2, 0.15), (0.5, 0.4, 0.3)) ((0.7, 0.6, 0.3), (0.5, 0.1, 0.4))  C1 ((0.25, 0.4, 0.35), (0.3, 0.2, 0.3))

C2 ((0.2, 0.3, 0.45), (0.2, 0.5, 0.4))

C3 ((0.75, 0.15, 0.1), (0.4, 0.5, 0.1))

Table 6: The ideal neutrosophic estimates reliability solution (INERS) M ,   and the ideal neutrosophic estimates unreliability solution (INEURS) M  ,  

M M





,





,  

 (0.7,0.25,0.1),     (0.5,0.15,0.1) 

 (0.85,0.25,0.2),     (0.8,0.1,0.2) 

 (0.75,0.15,0.1),     (0.5,0.1,0.1) 

 (0.7,0.2,0.25),     (0.7,0.1,0.1) 

 (0.25,0.5,0.5),     (0.25,0.2,0.3) 

 (0.2,0.55,0.5),     (0.2,0.5,0.4) 

 (0.6,0.5,0.4),     (0.4,0.5,0.3) 

 (0.45,0.6,0.3),     (0.5,0.3,0.4) 

 Table 7: The positive grey relational coefficient M GR  ij ,  ij 34

 M GR

(0.76, 0.7222)   (0.3585, 0.6190) (0.333, 0.3611) (0.9048, 0.65)   (0.4042, 0.8125) (0.6552, 0.8125)    (0.4222, 1 ) (1, 0.5909)  (0.9048, 0.7647) (0.76, 0.7222) (0.6552, 0.8125) (0.5135, 0.5909)   

Durga Banerjee, Bibhas C. Giri, Surapati Pramanik, Florentin Smarandache, GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment

Neutrosophic Sets and Systems, Vol. 15, 2017

 Table 8: The negative grey relational coefficient M GR  ij ,  ij 34

 M GR

(0.4615, 0.75) (0.5217, 0.6)   (0.7059, 0.5454) (0.6667,1)   (0.5714, 0.5454)    ( 0.5714, 0.5714) (0.3333, 0.4286) (1, 0.5454)  (0.3636, 0.7059) (0.48, 0.3333) (0.5, 0.75) (0.7059, 0.75)  

Table 9: Weighted matrix

 W M GR

 M GR 

 (0.06836, 0.11804) (0.07153, 0.07749) (0.29207, 0.20982) (0.20664, 0.19637)     (0.08051, 0.1907 ) (0.2146, 0.12681) (0.13048, 0.26228) (0.17815, 0.22092)   (0.17252, 0.14583) (0.163096, 0.15498) (0.21150, 0.26228) (0.13962, 0.16066)    

Table 10: Weighted matrix W M GR

 M GR

 (0.13461, 0.10401) (0.14307, 0.2146) (0.14897, 0.2421) (0.14185, 0.16314)      ( 0.10896, 0.10896) (0.07153, 0.08173) (0.3228,0.17606) (0.15536, 0.14829)   (0.06934, 0.13461) (0.10301, 0.07153) (0.1614, 0.2421) (0.19193, 0.20392)   

Durga Banerjee, Bibhas C. Giri, Surapati Pramanik, Florentin Smarandache, GRA for Multi Attribute Decision Making in Neutrosophic Cubic Set Environment