Issuu

New Trends in Neutrosophic Theory and Applications. Volume II

Some weighted arithmetic operators and geometric operators with SVNSs and their application to multi-criteria decision making problems Mehmet Şahin, Vakkas Uluçay*, and Hatice Acıoglu Department of Mathematics, Gaziantep University, Gaziantep27310-Turkey E-mail: mesahin@gantep.edu.tr, vulucay27@gmail.com, haticeacioglu6@gmail.com

ABSTRACT As a variation of fuzzy sets and intuitionistic fuzzy sets, neutrosophic sets have been developed to represent uncertain, imprecise, incomplete and inconsistent information that exists in the real world. In this paper,this article introduces an approach to handle multi-criteria decision making (MCDM) problems under the SVNSs. Therefore, we develop some new geometric and arithmetic aggregation operators, such as the single valued neutrosophic weighted arithmetic (SVNWA) operator, the single valued neutrosophic ordered weighted arithmetic (SVNOWA) operator,the single-valued neutrosophic sets hybrid ordered weighted arithmetic (SVNSHOWA) operator, the single-valued neutrosophic weighted geometric (SVNWG) operator and the single-valued neutrosophic ordered weighted geometric(SVNOWG) operator and the single-valued neutrosophic hybrid ordered weighted geometric (SVNHOG)operator, which extend the intuitionistic fuzzy weighted geometric and intuitionistic fuzzy ordered weighted geometric operators to accommodate the environment in which the given arguments are single valued neutrosophic sets which are characterized by a membership function, an indeterminacy-membership function and a nonmembership function. Some numerical examples are given to illustrate the developed operators. Finally, a numerical example is used to demonstrate how to apply the proposed approach.

KEYWORDS:

Neurosophic set, Single valued neutrosophic weighted geometric (SVNWG)

operator, Single valued neutrosophic weighted arithmetic (SVNWA) operator. Section1. Introduction To handle with imprecision and uncertainty, concept of fuzzy sets and intuitionistic fuzzy sets originally introduced by Zadeh (Zadeh 1965) and Atanassov (Atanassov 1986), respectively. Then, Smarandache (Smarandache 1998) proposed concept of neutrosophic set which is generalization of fuzzy set theory and intuitionistic fuzzy sets. The neutrosophic sets may express more abundant and flexible information as compared with the fuzzy sets and intuitionistic fuzzy sets. Recently, neutrosophic sets have been researched by many scholars in different fields. For example; on multi-criteria decision making problems (Liuet al. 2017a, 2017b, 2017c, Lin 2017, Kandasamy and Smarandache 2017) etc. Also the notations such as fuzzy sets, intuitionistic fuzzy sets and neutrosophic sets have been applied to some different fields in (Broumi et al. 2014a, 2014b,2015a, 2015b, 2015c, 2016a,2016b,2016c,2016d, He et al. 2014a, 2014b, 2014c, Sahin et al 2015,2016,Ulucay et al. 2016a, 2016b). This paper mainly discusses extension forms of these aggregation operators with intuitionistic fuzzy sets, including the intuitionistic fuzzy weighted averaging operator, intuitionistic fuzzy OWA operator, intuitionistic fuzzy hybrid weighted averaging operator, intuitionistic fuzzy GOWA operator, intuitionistic fuzzy generalized hybrid weighted averaging operator and their

Florentin Smarandache, Surapati Pramanik (Editors)

applications to multi-attribute decision-making with intuitionistic fuzzy sets (Li 2011,Li 2010, Li et al. 2010a Wang et al.2009, Li et al 2010b). In this paper, we shall develop some geometric operators andarithmetic operator, such as the single-valuedneutrosophicweighted arithmetic operator (SVNWAO), the single valued neutrosophic weighted geometric (SVNWG) operator, the intuitionistic fuzzy ordered weighted geometric (IFOWG) operator and the intuitionistic fuzzy hybrid geometric (IFHG) operator. To do so, this paper is structured as follows. In Section 2, we review the weighted geometric (WG) operator and the ordered weighted geometric (OWG) operator. In Section 3, we develop the IFWG operator, the IFOWG operator, and the IFHG operator, and study their various properties. In Section 4, we give an application of the IFHG operator to multiple attribute decision making with intuitionistic fuzzy information. Concluding remarks are made in Section 5. Section 2. Preliminaries To facilitate the following discussion, some concepts related to neutrosophic set and single valued neutrosophic set are briefly introduced in this section. Definition 2.1(Smarandache 1998) Let be a space ofpoints (objects), with a generic element in , denoted by .An in is characterised by a truth-membership function , an indeterminacy-membership function and a falsity-membership function . and are standard or non-standard subsets of ,that is, , and .There is no restriction on the sumof and , therefore . Definition 2.2(Smarandache 1998)The complement of a neutrosphic set is denoted by defined as

and is

forevery element in . Definition 2.3(Smarandache 1998) A neutrosophic set is contained in the other neutrosophicset , if and only if

for every in . Definition 2.4 (Smarandache 1998) The union of two neutrosophic sets and is a neutrosophic set denoted by , whose truth-membership, indeterminacy-membership and falsemembership functions are related to those of and by

and for any in .

New Trends in Neutrosophic Theory and Applications. Volume II

Definition 2.5 (Smarandache 1998)The intersection of two neutrosophic sets and is a , whose truth-membership, indeterminacy-membership neutrosophicset , denoted by and false-membership functions are related to those of and by and for any in . A single valued neutrosophic set (SVNS) is an instance of a neutrosophic set, which can be used in real scientific and engineering applications (Ye 2014) Note that the set of all SVNSs on R will be denoted by . Definition 2.6 (Wang et al. 2010)Let be a universe set, with a generic element in denoted by A single valued neutrosophic set (SVNS) in is characterized by truth-membership function , indeterminacy-membership function and falsity-membership function . . Therefore, a SVNS A can be written as For each element in , follows: . For two SVNSs A, B, Wang et al. (Wang et al. 2010)presented the following expressions: (1)

if and only if

(2)

if and only if

(3)

, and

and

for every

. .

A SVNS

is usually denoted by the simplified symbol

for any

For any two SVNSs and , the operational relations are defined by Wang et al. (Wang et al. 2010). (1)

for every

(2)

for every

(3) For a SVNS

for every in

, Ye (Ye 2014) called the triplet

neutrosophic number (SVNN), which is denoted by Definition 2.7(Ju 2014)Let

be a

single valued .

, then the score function and the

accuracyfunction of are determined by Eqs. (1) and (2), respectively

Florentin Smarandache, Surapati Pramanik (Editors)

(1) (2) Theorem 2.8(Ju 2014) Let and laws between them are shown as follows: 1-If

, then

;

2-If

, then

;

3-If

, then:

be two

(1) If

, then

;

(2) If

, then

;

(3) If

, then

;

, then the comparison

Definition 2.9(Chi 2013) Let and be any threesingle valued neutrosophic numbers, and , then some operational laws of the are defined as follows. (1) ; (2) ; ; (3) (4) Obviously, the above operational results are still . Some relationships can be further . established for these operations on Section 3. Arithmetic operators and Geometric operators of the 3.1 Arithmetic operators of the Definition 3.1.1 Let operator,denoted by , is defined as;

where, every

Then

weighted

is a weight vector associated with the and

such that,

Theorem 3.1.2 Let every In such that operator is also a

arithmetic

operator, for

be a weight vectorof , for Then, their aggregated value by using

and and

New Trends in Neutrosophic Theory and Applications. Volume II

Proof: The proof can be made by using mathematical induction on and

If holds for

then, when

Example 3.1.3

be two

as; assume that,

then, for n = 2, we have

, that is

; by the operational laws in Definition 2.9, I have

and

Florentin Smarandache, Surapati Pramanik (Editors)

Definition 3.1.4 Let weighted arithmetic operator denoted by

.Then

ordered

, is defined as; ,

is a weight vector associated with the mapping ; which satisfies the where and ; is the k-th largest of the normalized conditions: n which is determined through using ranking method in Definition 2.7. It is not difficult to follows from Definition 3.1.4 that

which is summarized as in Theorem 3.1.5 Theorem 3.1.5 Let operator denoted by

where ofthen

. Then

ordered weighted arithmetic

, is defined as;

and

the

k-th

which is determined through using ranking method in Definition 2.7.

Proof: Theorem 3.1.5 can be proven in a similar way to that of Theorem 3.1.2 (omitted). Example 3.1.6

largest

and

New Trends in Neutrosophic Theory and Applications. Volume II

It is obvious that function ranking method, it follows that

. Hence, according to the above scoring . Thus, we have:

Definition 3.1.7 Let ordered weighted arithmetic operator denoted by

where

.Then

hybrid

, is defined as;

and

is a weight vector associated with the

here n is regarded as a balance factor is a mapping weight vector of the is the k-th largest of the n which are determined through using some ranking method such as the above scoring function ranking method. Note that if Theorem 3.1.8 Let with and

, then

degenerates to the

be a weight vector of . Then their aggregated value by using operator is also a

and

Florentin Smarandache, Surapati Pramanik (Editors)

where is the k-th largest of the n which is determined through using some ranking method such as the above scoring function ranking method. Proof : Theorem 3.1.8 can be proven in a similar way to that of Theorem 3.1.2 (omitted). Example 3.1.9

we obtain the scores of the Simplifiedneutrosophicsets

respectively. Obviously, scoring function ranking method, we have:

and

as follows:

.Thereby, according to the above

New Trends in Neutrosophic Theory and Applications. Volume II

3.2. Geometric operators of the In this section, three weighted geometric operator of is called weighted geometric operator, ordered weighted geometric operator and hybrid ordered weighted geometric operator is given. Some of it is quoted from application in (He 2014a 2014b, 2014c, Xu and Yager 2006, Wei 2010). Definition 3.2.1 Let denoted by , is defined as;

.Then

weighted geometric operator,

where, is a weight vector associated with the operator, for such that, and every Theorem 3.2.2 Let be a weight vectorof , for every In such that and Then, their aggregated value by using operator is also a and

Proof:The proof can be made by using mathematical induction on n as; assume that, and

be two

then,

forn = 2, we have

If holds for

, that is

Florentin Smarandache, Surapati Pramanik (Editors)

then, when

; by the operational laws in Definition 2.9, I have

Example3.2.3

and

Definition 3.2.4Let weighted geometric operator denoted by

where

.Then

ordered

, is defined as;

is a weight vector associated with the mapping

; which satisfies the

and ; is the k-th largest of the normalized conditions: n which is determined through using ranking method in Definition 2.7. It is not difficult to follows from Definition 3.2.4 that

New Trends in Neutrosophic Theory and Applications. Volume II

which is summarized as in Theorem 3.2.5. . Then

Theorem 3.2.5 Let operator denoted by

orderedweighted geometric

, is defined as;

where

and

the

k-th

largest

then

which is determined through using ranking method in Definition 2.7.

Proof: Theorem 3.2.5 can be proven in a similar way to that of Theorem 3.2.2 (omitted). Example3.2.6

and

It is obvious that function ranking method, it follows that

. Hence, according to the above scoring . Thus, we have:

Florentin Smarandache, Surapati Pramanik (Editors)

Definition 3.2.7Let ordered weighted geometric operator denoted by

where

.Then

hybrid

, is defined as;

and

is a weight vector associated with the

mapping here n is regarded as a balance factor is a weight vector of the is the k-th largest of the n which are determined through using some ranking method such as the above scoring function ranking method. , then

Note that if Theorem 3.2.8 Let and

degenerates to the

be a weight vector of with . Then their aggregated value by using operator is also a

and

where is the k-th largest of the n which is determined through using some ranking method such as the above scoring function ranking method. Proof : Theorem 3.2.8 can be proven in a similar way to that of Theorem 3.2.2 (omitted). Example 3.2.9

we obtain the scores of the single-valuedneutrosophicsets

and

as follows:

New Trends in Neutrosophic Theory and Applications. Volume II

respectively. Obviously, scoring function ranking method, we have:

.Thereby, according to the above

Section4. Single valued neutrosophic sets and their applications in multi-criteria groupdecision-making problems There is a panel with four possible alternatives to invest the money (adapted from Herrera 2000): (1) is a car company; (2) is a food company; (3) is a computer company; (4) is a television company. TheinvestmentCompany must take a decision according to the following three criteria: (1) is the risk analysis;(2) is the growth analysis; (3) is the environmental impact analysis; (4) social political impactanalysis. The four possible alternatives are to be evaluated under the above three criteria by correspondingto linguistic values of SVNSs for linguistic terms (adapted from Ye 2011), as shown in Table 1.

Florentin Smarandache, Surapati Pramanik (Editors)

Definition 4.1 :Let attributes. If

be a set of alternatives,

be the set of

, then

is called an SVNS-multi-criteria decision-making matrix of the decision maker. Now, we can give an algorithm of the

-multi-criteria decision-making method as follows;

Algorithm: for decision;

Step 1. Construct the decision-making matrix Step 2. Compute the

the decision-making matrix Step 3. Compute the

and write ; and obtain the scores of the

Step 4. Compute the

and ; and obtain the scores of the

;

the decision-making matrix

and write ; and obtain the scores of the

;

Step 5. Compute the

write the decision-making matrix

then, ; and obtain the scores of the

;

Step 6. Compute the

then, write the decision-making matrix

Step 7. Compute the

;

write the decision-making matrix

; and obtain the scores of the

; s

then, write the decision-making matrix ;

; and obtain the scores of the

New Trends in Neutrosophic Theory and Applications. Volume II

Step 8. Rank all alternatives

by using the ranking method of

s and determine the best

alternative. Section5. Application In this section, we give an application for the s -multi-criteria decision-making method, by using the operator. Some of it is quoted from application in (Deli 2015, Herrera 2000, Ye 2011). Example 5.1Let us consider the decision-making problem adapted from (Ye 2015). There is an investment company, which wants to invest a sum of money in the best option. There is a panel with the set of the four alternatives is denoted by

to invest the money. The investment company must take a decision according to the set of the four attributes is denotedby ={ =risk analysis, =growth analysis, =environmental impact analysis, = social political impact analysis}.Then, the weight vector of the attributes is and the position weight vector is by using the weight determination based on the normal distribution. For the evaluation of an alternative with respect to a criterion , it is obtained from the questionnaire of a domain expert. Then, Step 1.The decision maker construct the decision matrix

Step 2.The values of weighted arithmetic operator.

The score function values of

as follows:

are compute with the help of single-valued neutrosophic

are calculated.

Florentin Smarandache, Surapati Pramanik (Editors)

Step 3.The values of are compute with the help of single-valued neutrosophic ordered weighted arithmetic operator.

The score function values of

are calculated.

Step 4. The values of are compute with the help ofthe Single-valued neutrosophic sets hybrid ordered weighted arithmetic operator(SVNSHOWA).

The score function values of

Step 5. The values of neutrosophic weighted geometric operator.

are calculated.

are compute with the help of single-valued

100

New Trends in Neutrosophic Theory and Applications. Volume II

The score function values of

are calculated.

Step 6.The values of are compute with the help of single-valued neutrosophic ordered weighted geometric operator.

The score function values of

are calculated.

Step 7.The values of are compute with the help of single-valued neutrosophic hybrid ordered weighted geometric operator.

The score function values of

are calculated.

101

Florentin Smarandache, Surapati Pramanik (Editors)

Step 8. Operators

Section 6. FUTURE RESEARCH DIRECTIONS In this paper, this article introduces an approach to handle multi-criteria decision making (MCDM) problems under the SVNSs. Using this concept we can extend our work in (1) More effective approaches for SVNSs (2) How to determine the weight vectors for SVNSs (3) An approach of multi-criteria decision making with weight expressed by SVNSs. Section 7. Conclusion This paper proposes six operator are called the single valued neutrosophic weighted geometric (SVNWG) operator, the single valued neutrosophic ordered weighted geometric (SVNOWG) operator, the single-valued neutrosophic sets hybrid ordered weighted arithmetic (SVNSHOWA) operator, the single-valued neutrosophic weighted geometric (SVNWG) operator , the singlevalued neutrosophic ordered weighted geometric(SVNOWG) operator and the single-valued neutrosophic hybrid ordered weighted geometric (SVNHOG)operator. Then an approach is developed to solve more general multi-criteria decision making problems as straightforward manner. ACKNOWLEDGMENT We thank both editors for their useful suggestions. REFERENCES Atanassov, K. Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1986) 87-96. Broumi, S. I. Deli, F. Smarandache, Relations on Interval Valued Neutrosophic Soft Sets, Journal of New Results in Science 5 (2014a) 01-20. Broumi, S. I. Deli, F. Smarandache, Interval valued neutrosophic parameterized soft set theory and its decision making, Journal of New Results in Science 7 (2014b) 58-71. Broumi, S., &Smarandache, F. (2015). Single valued neutrosophic soft expert sets and their application in decision making. Journal of New Theory, 3, 67-88.

102

New Trends in Neutrosophic Theory and Applications. Volume II

Broumi, S., &Smarandache, F. (2015). Possibility Single Valued Neutrosophic Soft Expert Sets and Its Application In Decision Making. Journal of New Theory, 4, 06-29. Broumi, S., Mumtaz, A., &Smarandache, F. (2015). Mappings on Neutrosophic Soft Expert Sets. Journal of New Theory, 5, 26-42. Broumi, S, M. Talea, A. Bakali, F. Smarandache,(2016). “Single Valued Neutrosophic Graphs,” Journal of New Theory, N 10, pp. 86-101. Broumi, S, M. Talea, A. Bakali, F. Smarandache, (2016) “On Bipolar Single Valued Neutrosophic Graphs,” Journal of New Theory, N11, pp.84-102. Broumi, S, A. Bakali, M, Talea, and F, Smarandache, (2016). Isolated Single Valued Neutrosophic Graphs. Neutrosophic Sets and Systems, Vol. 11, pp.74-78. Broumi, S, F. Smarandache, M. Talea and A. Bakali,(2016). An Introduction to Bipolar Single Valued Neutrosophic Graph Theory. Applied Mechanics and Materials, vol.841,184-191. Broumi, S, M. Talea, F. Smarandache and A. Bakali, (2016). Single Valued Neutrosophic Graphs: Degree, Order and Size. IEEE World Congress on Computational Intelligence, pp. 2444-2451. Chi PP, Liu PD. An extended TOPSIS method for multiple attribute decision making problems based on interval neutrosophic set. Neutrosophic Sets and Systems (2013); 1: 63–70. Deli, I., &Şubaş, Y. (2015). Some weighted geometric operators with SVTrN-numbers and their application to multi-criteria decision making problems. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-11. He, Y. H. Chen, L. Zhou, B. Han, Q. Zhao, J. Liu, Generalized intuitionistic fuzzy geometric interaction operators and their application to decision making, Expert Systems with Applications 41 (2014a) 2484-2495. He, Y. H. Chen, L. Zhou, B. Han, Q. Zhao, J. Liu, Generalized intuitionistic fuzzy geometric interaction operators and their application to decision making, Expert Systems with Applications 41 (2014b) 2484- 2495. He, Y. H. Chen, L. Zhou, J. Liu, Z. Tao, Intuitionistic fuzzy geometric interaction averaging operators and their application to multi-criteria decision making, Information Sciences 259 (2014c) 142-159. Herrera, F. E. H. Viedma, Linguistic decision analysis: steps for solving decision problems under linguistic information, Fuzzy Sets and Systems 115 (2000) 67-82. Ju YB. Some aggregation operators with single valued neutrosophic information and their application to multiple attribute decision making. Technical Report 2014. Kandasamy, I., &Smarandache, F. (2017). Multicriteria Decision Making Using Double Refined Indeterminacy Neutrosophic Cross Entropy and Indeterminacy Based Cross Entropy. In Applied Mechanics and Materials (Vol. 859, pp. 129-143). Trans Tech Publications. Li, D.-F., Wu, Z.Q.: Generalized OWA operator based methodology for multiattribute decision-making using intuitionistic fuzzy sets. Crit. View 4, 13–34 (2010b) Li, D.-F.: The GOWA operator based approach to multiattribute decision-making using intuitionistic fuzzy sets. Math. Comput. Modell. 53(5–6), 1182–1196 (2011) Li, D.-F.: Multiattribute decision-making method based on generalized OWA operators with intuitionistic fuzzy sets. Expert Syst. Appl. 37(12), 8673–8678 (2010a) Li, D.-F., Wang, L.L., Chen, G.H.: Group decision-making methodology based on the Atanassov’s intuitionistic fuzzy set generalized OWA operator. Int. J. Uncertainty Fuzziness andKnowl. Based Syst. 18(6), 801–817 (2010) Lin, J., & Zhang, Q. (2017). Note on continuous interval-valued intuitionistic fuzzy aggregation operator. Applied Mathematical Modelling, 43, 670-677

103

Florentin Smarandache, Surapati Pramanik (Editors)

Liu, P., & Li, D. (2017a). Some Muirhead Mean Operators for Intuitionistic Fuzzy Numbers and Their Applications to Group Decision Making. PloS one, 12(1), e0168767. Liu, P., & Li, H. (2017b). Multiple attribute decision-making method based on some normal neutrosophic Bonferroni mean operators. Neural Computing and Applications, 28(1), 179-194. Liu, C., & Luo, Y. (2017c). Power aggregation operators of simplified neutrosophic sets and their use in multi-attribute group decision making. IEEE/CAA Journal of AutomaticaSinica. Smarandache, F. (1998). A unifying field in logics. Neutrosophy: Neutrosophic probability, set and logic. Rehoboth, DE: AmericanResearch Press. Şahin, M.,Alkhazaleh, S., &Ulucay, V. (2015). Neutrosophic soft expert sets. AppliedMathematics, 6(01), 116. Şahin, M., Deli, I., & Uluçay, V. Jaccard Vector Similarity Measure of Bipolar Neutrosophic Set Based on Multi-Criteria Decision Making. International Conference on Natural Science and Engineering (ICNASE’16) Kilis, Türkiye. Wang, S.Q., Li, D.-F., Wu, Z.Q.: Generalized ordered weighted averaging operators based methods for MADM in intuitionistic fuzzy set setting. J. Syst. Eng. Electron. 20(6), 1247–1254 (2009) Wang H, Smarandache F, Zhang YQ. Single valued neutrosophic sets. MultispaceMultistruct 2010; 4: 410–3. Wei, G. Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making, Applied Soft Computing 10 (2010) 423-431. Xu, Z., R. R. Yager, Some geometric aggregation operators based on intuitionistic fuzzy sets, International Journal of General Systems, 35(4) (2006), 417433. Uluçay, V., Deli, I., & Şahin, M. (2016). Similarity measures of bipolar neutrosophic sets and their application to multiple criteria decision making. Neural Computing and Applications, 1-10. Uluçay, V., Deli, I., & Şahin, M.(2016). Trapezoidal fuzzymulti-number and its application to multi-criteria decision-making problems. Neural Computing and Applications, 1-10. Ye, J. Expected value method for intuitionistic trapezoidal fuzzy multicriteria decision-making problems, Expert Systems with Applications 38 (2011) 11730-11734. Ye, J. (2015). Trapezoidal neutrosophic set and its application to multiple attribute decisionmaking. Neural Computing and Applications, 26(5), 1157-1166. Ye J. A Single valued neutronsophic cross-entropy for multi-criteria decision making problems. Appl Math Model 2014; 38: 1170–5. Ye J, Multiple attribute group decision-making method with completely unknown weights based on similarity measures under single valued neutrosophic environment, J Intell Fuzzy Syst 2014; DOI: 10.3233/IFS–141252. Zadeh, L.A. Fuzzy Sets, Information and Control, 8, 338-353, 1965.

104