A Novel Approach of Refined Plithogenic Neutrosophic Sets in Multi Criteria Decision Making

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A NOVEL APPROACH OF REFINED PLITHOGENIC NEUTROSOPHIC SETS IN MULTI CRITERIA DECISION MAKING

S.P. Priyadharshini *1, F. Nirmala Irudayam *2

*1ResearchScholar,DepartmentofMathematics,NirmalaCollegeforWomen,Coimbatore, TamilNadu,India.

*2AssistantProfessor,DepartmentofMathematics,NirmalaCollegeforWomen,Coimbatore, TamilNadu,India.

ABSTRACT

In decision-making problems with sub-attributes Refined Plithogenic Neutrosophic Sets(RPNS) are efficiently used by considering their reality, indeterminacy and falsehood components. In this article a few concepts of RPNS along with union, intersection, complement and partial orders are introduced in a unique approach to cope up with uncertain and conflicting data. Some ideal properties of RPNS based on these perceptions have alsobeenstudied.Wewillimplementtheconceptinordertoevaluatetheefficacyoftheproposedmethodology ofRPNSinmulticriteriadecisionmakingwiththenumericalexamples.

Keywords: Plithogenic set, Neutrosophic Set, Refined Plithogenic Neutrosophic Set, Multi criteria decision making.

I. INTRODUCTION

Numerous concepts for dealing with ambiguity, inconsistency and incoherence have recently been implemented.Probabilistic theory, fuzzy set theory, intuitionist fuzzy set, rough set theory, etc. are constantly usedasimpactfultechniquestohandlewithvarioustypesofcomplexitiesandinaccuracyinembeddedmethod. Allthesehypothesesmentionedconsequentlyhavefailedtodealwithundefinedandcontradictoryinformation intheworldview.

FlorentinSmarandacheintroducedtheNeutrosophicset(NS)theoryin1998anditwasdevelopedfromanew field of study, namely Neutrosophy. NS is capable of managing volatility, indeterminacy and contradictory information. NS approaches are appropriate for analysing complexity, indeterminacy and contradictory informationproblemsinwhichtechnicalexpertiseisrequiredandhumanassessmentisessential.

Yager[21]firstproposedthemulti-settheoryasarepresentationoftheframeworkofnumbertheory,andafter thatthemulti-set wasestablishedbyCaludeetal [3].A varietyofgeneralizationsof themulti-settheoryhave been made by many scholars from different perspectives. Sebastian and Ramakrishnan [9, 10] introduced a newtechniquetermedmultifuzzysets,whichisamulti-setgeneralization.Eversince,manyresearchers[4,5, 6, 7] have addressed more multi-fuzzy set properties and they expanded the idea of fuzzy multi-sets to an intuitionistic fuzzy set called intuitionistic fuzzy multi-sets (IFMS). Since then, many exemplary methods have been suggested by the researchers in the IFMS analysis [2, 6, 12, 20]. A multi fuzzy set component can occur quiteoftenwithpossiblythesameordifferentmembershipfunctions,althoughanintuitionisticfuzzymulti-set techniquerequiresmembershipandnon-membershipvaluestooccurfrequently.FMSandIFMSdefinitionsfail todealwithindeterminacy.

In 2013, by refining each neutrosophical component t, i, f into t1, t2, ..., ts and i1, i2, ..., ir and f1, f2, ..., fm Smarandache [17] generalized traditional neutrosophical logic to n-value refined neutrosophical logic. The definitionofrefined(multi-set)neutrosophicalsets(RNS)wasrecentlyusedbyDelietal.[3] Ye[20]andsome of their basic properties were studied by Said Broumi and Irfan Deli. RNS is the generalization of fuzzy multisetsandintuitionisticfuzzymulti-sets.

Plithogeny which was introduced in 2017 by Florentine Smarandache is the origination, existence, development, growth and emergence of various entities from technologies and organic combinations of old objects that are conflicting and/or neutral and/or non-contradictory. A plithogenic set P is a set whose membersarecharacterizedbyoneormoreattributesandtheremaybeseveralvaluesforeachattribute. The motive of this article is an attempt to study Plithogenic Neutrosophic Sets to Refined Plithogenic Neutrosophic Sets (RPNS). The manuscript is carried out in the following manner. In Section 2, we introduce some concepts and perceptions of PNS and RPNS theory which will support us in the subsequent study. We

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examinetheoperatorsofPNRSinSection3.Insection4,wemakeanapplicationforPNRSindecision-making. Finally,weconcludethisarticlewiththefuturework.

II. PRELIMINARY CONCEPTS

Inthissection,werefertothenoteworthyfundamentalpreliminariesandinparticular,theworkofZadeh[23] Attanassov[1],AtiqeUrRahmanetal[2],I.Delietal[5]and Smarandache[15]whointroducedthePlithogenic Setwhichinvestigatesthecomplexitiesofseveraltypesofoppositesandtheirneutralsandnon-opposites.

Definition 2.1 [15] Plithogenicset(PS)isageneralisationofacrispset,afuzzyset(FS),anintuitionisticfuzzy set (IFS) and a neutrosophic set (NS), while these four categories are represented by a single attribute value (appurtenance):singlevalue(belonging)-foracrispsetandaFS,twovalues(belonging,non-belonging)-foran IFS, or triple values (belonging, non-belonging and indeterminacy) for NS. In general, PS is a set whose membersaredeterminedbyasetofelementswithfourormorevaluesofattributes.

Definition 2.2 [8] LetUbeauniverseofdiscourse,andPanon-emptysetofelements,P  U,then (P,  ,R,d,C)iscalledaPNSwhere Theattributeis  =“appurtenance”

ThesetofattributevaluesR={belonging,indeterminacy,non-belonging},whosecardinal|R|=3; TheDominantAttributeValue=belonging; Theattributevalueappurtenancegradefunction:  0,1 :   R P d , [0,1] ) , ( [0,1], ) , ( [0,1], ) , (    belonging non y d indeterminacy y d belonging y d with 3; ) , ( ) min det , ( ) , ( 0     nonbelonging y d acy er in y d belonging y d andtheattributevaluecontradictiongradefunction: [0,1], :   R R C 0, , ( ) ( ) , (    nonbelonging nonbelonging C indeterminacy,indeterminacy C belonging belonging C 0, ) , (  nonbelonging belonging C 2, 1 ) , ( ) , (   indeterminacy nonbelonging c indeterminacy belonging C WhichmeansthatforthePNSaggregationoperators(Intersection,Union,Complementetc.),ifoneappliesthe norm  onbelongingfunction,thenonehastoapplythe conorm  onnon-belonging(andmutually),whileon indeterminacyoneappliestheaverageof norm  and conorm  (i.e) ab b a b a fuzzy ab b a fuzzy f conorm f norm          & .

III. REFINED PLITHOGENIC NEUTROSOPHIC SETS (RPNS) AND ITS OPERATORS

In this section we will consider some possible definitions for the fundamental concepts of the RPNS and its operators.

Definition 3.1 LetUbeauniverseofdiscourse,andPanon-emptysetofelements,P  U,then  C D rpn , , , ,    where‘rpn’standsfor refinedplithogenicneutrosophiciscalled RPNSifatleastoneofthevaluesofattribute A ai  issplitintotwoormoresubvaluesofattribute: A a a i i , , 2 1 ,withthesubvalueappurtenancedegree functionofattribute   1,2, , 0,1 P ) , ( 3 

s for a z D si Definition 3.2 Asinglevaluedfinitelyrefined PNShastheform 

l x b

4, , int 1 , ,

, , , ; , , ; , , , 2 1 2 1 2 1 l q and x j b k for N H M all and l x b with egers are l x b where N N N H H H M M M q j K

 }. {1,2, } {1,2, , 1,2, 0,1, , ,

Theattribute  =“appurtenance”. Thesetofattributevalues } , , ; , , ; , , { 2 1 2 1 2 1 l x b y y y e e e t t t A 

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   
       

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Where“t”meanssubmembership,“e”meanssubindeterminacy,“y”meanssubnon-membership.

Thedominantattributevalue(DAV)= bt t t , , 2 1 ;

Thevalueofattributeappurtenancedegreefunction:   0,1, :   A P D

k 0, ) , ( ) , ( ) , ( 2 1 2 1 1

u x r

r

l q v x j

w u x

      

      5)

) ,1 ; 1 ; ,1 ( ) ,1 ; 1 ; ,1 (  

v

In this section, we refine the existing attributes into sub-attributes in order to achieve a better accurateness whichreallyhelpstomakedecisionswhenattemptingtodealwithmulti-attributescenarios. (i)ConsideraUniattribute“Evaluation”(of5attributevalues)PNSforselectingtheCandidateinaninterview isrepresentedby

1 0.75 0.5 0.25 0 deg bad very bad Fair good good very Appurtenance of Degree value Attribute ree disimilarity z

  

  

1 075 05 025 0 deg bad very bad Fair good good very Appurtenance of Degree value Attribute ree disimilarity z PN

        

7 5,0 2,0 0 6 1,0 9,0 0 5 8,0 2,0 0 4,06,03 0 1 2,03,0 0 ,

1 075 5 0 25 0 0 deg bad very good below Fair bad above good very Appurtenance of Degree value Attribute ree disimilarity z PN

0.2,0.5,0.7 0.9,0.1,0.6 0.2,0.8,0.5 0.4,0.6,0.3 0.2,0.3,0.1 ,

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            ; 0 ,
1
l x
e
t
z
e z
t z
q z l q j z x j x z b k q j x               
, 0,1, ) , ( 0,1, ) , ( 0,1, ) , (
1 1
b y D
D
D with q j x all for y
D
D
D
q q j j k
1      , ,
};
q
l q
j q j k q k       3.2.1 RPNS intersection        l q h v w o w o u x g r l q h x j w u x g l q v x j o u x r q f q j f j j f j x f x q j x RPNS q j x            
j x
x
                     
 
 
 
       
q a q j a j x a x
j x
Andthevalueofattributedissimilarityfunction: forall y y C e e C t t C q j x
5, 0 ) , ( 1, ) , ( 1, ) , (
{1,2, , } {1,2, , , 1,2, , 2 1 2 1 2 1
j x all for e y C e t C y t C
q and x j j b k k
   
      ,1 , 5* ;0 ,1 ) ,1 ; 1 ; ,1 ( ) ,1 ; 1 ; ,1 ( 3.2.2 RPNS union        l q h v w o w o u x g r l q h x j w u x g l q v x j o u x r q f q j f j j f
f
q j x RPNS q j x 
,1 , 5* ;0 ,1 ) ,1 ; 1 ; ,1 ( ) ,1 ; 1 ; ,1 ( 3.2.3 RPNS Complement
), ,1 ; ,1 ; ,1 ; ,1 ( ,1 ; ,1 ; ,1 u x r N l q v x j o H l q v M l q v N x j o H b x r M RPNS x q q j j q x q q j j x x          
    
 
Whereall x M =subtruths,all j H =subindeterminacies,andall qN =subfalsehoods. 3.2.4 RPNS Inclusions (Partial orders)
[0,0 , 1 1 , 1
a
q
RPNS
C where h C
all and w C o all g C
all if only and if l q h x j
g
o
isthedissimilaritydegreebetweenthevaluesofattribute a If aC doesnotoccur,wewilltakeitaszeroby default.
            
IV. NUMERICAL EXAMPLE
0.2,0.5,0.7 0.9,0.1,0.6 0.2,0.8,0.5 0.4,0.6,0.3 0.2,0.3,0.1 ,
     
ThePNSnegationandRPNAttributeValueSetfortheabovedataisgivenbelow.    
      
=              

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Therefore,theoriginalattributeset

bad very bad fair good good very A , , , ,

Andhasbeenpartiallyrefinedinto  verybad belowgood fair abovebad good very A fined , , , , Re  Where   , , bad good belowgood abovebad 

(ii)Considerauni-attribute(of4attributevalues)PNSfortheattribute“Proficiency”ofselectingthestudents inaclassthathasthefollowingattributevalues:Excellent(thedominantone),good,average,poor.    

1 75 0 5 0 0 Appurtenance of Degrees

Excellent good average poor AttributeValues contradiction of Degrees

    

6) 9,0 8,0 (0 4) 3,0 9,0 (0 6) 8,0 4,0 (0 4) 3,0 2,0 (0

Nowletusintroducethecomponent'best'asarefinementoftheprecedingtablewhichistabulatedbelow     

1 9 0 8 0 5 0 3 0 1 0 0 Appurtenance of Degree

contradiction of Degree

Excellent best good Average good anti lessaverage best anti poor above poor Attributevalues

     

 6) 9,0 8,0 (0 7) 6,0 5,0 (0 4) 3,0 9,0 (0 6) 8,0 4,0 (0 4) 2,0 8,0 (0 6) 5,0 4,0 (0 4) 3,0 2,0 (0 ) ( ) (

Thecomplementoftheattributevalue“Excellent”whichis80%dissimilaritywith“good”,willbeanattribute valuewhichis1-0.8=0.2=20%dissimilaritywith“Excellent”,soitwillbeequalto  Average Excellent 2 1 .Letus callit“lessaverage”,whosedegreeofappurtenanceis   3). 2,0 8,0 (0 4 3,0 9,0 0 1 

Iftheattribute“Proficiency”hasothervalues“Excellent”beingthedominant. Thenegationofbestis1-0.9=0.1=10%dissimilaritydegreewiththedominantattributevalue“Excellent”,so itisinbetweenExcellentandAverage,wemaysayitisincludedintotheattributevalueinterval  average Excellent , muchclosertoExcellentthantoAverage.Letuscall “abovepoor”,whosedegreeof appurtenanceis   (0.7,0.8,0.3). 0.8,0.9,0.4 1 

V. CONCLUSION AND FUTURE WORK

Inthisarticle,wehavedefined the RPNS byintroducingitscoreoperators. Wehavepresent anapplicationof RPNS in multi criteria decision making. In future work, we will extend this concept to study the correlation measuresofRPNSandalsoitselementaryproperties.

VI. REFERENCES

[1] K.T.Atanassov,IntuitionisticFuzzySet.FuzzySetsandSystems,20(1),87–86,1986.

[2] Atiqe Ur Rahman, Muhammad Rayees Ahmad, Muhammad Saeed, Muhammad Ahsan, Muhammad Arshad, Muhammad Ihsan, A study on fundamentals of Refined intuitionistic fuzzy set with some properties,Journaloffuzzyextensionandapplications,1(4),300-314,2020.

[3] C.S.Calude,G. Paun, G.Rozenberg,A. Saloma,Lecture notesin computerscience: MultisetProcessing Mathematical, Computer Science, and Molecular Computing Points of View, 2235, Springer, New York,2001.

[4] S. Das, M. B. Kar and S. Kar, Group multi-criteria decision making using intuitionistic multi-fuzzy sets, JournalofUncertaintyAnalysisandApplications,10(1),1-16,2013.

[5] I. Deli, S. Broumi and F. Smarandache On Neutrosophic refined sets and their applications in medical diagnosis,Journalofnewtheory,88-98,2015.

[6] P.A.Ejegwa,J.A.Awolola,IntuitionisticFuzzyMultiset(IFMS)InBinomialDistributions,International JournalofScientificandTechnologyResearch,3(4),335-337,2014.

[7] R.MuthurajandS.Balamurugan,Multi-FuzzyGroupanditsLevel Subgroups,Gen.Math.Notes,17(1), 74-81,2013.

[8] S.P. Priyadharshini, F. Nirmala Irudayam Plithogenic Neutrosophic set and Plithogenic Neutrosophic TopologicalSpaces(Submitted),2021.

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[9] S. Sebastian and T. V. Ramakrishnan, Multi-fuzzy extension of crisp functions using bridge functions, AnnalsofFuzzyMathematicsandInformatics,2(1),1–8,2011.

[10] S.SebastianandT.V.Ramakrishnan,Multi-FuzzySets,InternationalMathematicalForum,5(50),2471–2476,2010.

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[12] T.K.ShinojandS.J.John,Intuitionisticfuzzymulti-setsanditsapplicationinmedicaldiagnosis,World AcademyofScience,EngineeringandTechnology,6,01–28,2012.

[13] P.K. Singh, “Plithogenic set for multi-variable data analysis”, International Journal of Neutrosophic Science(IJNS),1(2),81-89,2020

[14] F. Smarandache, A Unifying Field in Logics. Neutrosophy: Neutrosophic Probability, Set and Logic, Rehoboth:AmericanResearchPress,1998.

[15] F. Smarandache, “Plithogeny, PlithogenicSet, Logic,Probability, and Statistics”, 141 pages, Pons Editions, Brussels,Belgium,2017, arXiv.org (Cornell University), Computer Science-Artificial Intelligence.

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[17] F.Smarandache,”PhysicalPlithogenicsets”,71st AnnualGaseousElectronicsconference,SessionLW1, Oregon Convention Center Room, Portland, Oregon, USA, November 5-9, 2018;http://meetings.aps.org/Meeting/GEC18/Session/LW1.110

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