Neutrosophic Cubic Hamacher Aggregation Operators And .
Neutrosophic Sets and Systems, Vol. 33, 2020University of New MexicoNeutrosophic Cubic Hamacher Aggregation Operators and Their Applications inDecision MakingHüseyin KamacıDepartment of Mathematics, Faculty of Science and Arts, Yozgat Bozok University, 66100 Yozgat, Turkey;huseyin.kamaci@hotmail.com, huseyin.kamaci@bozok.edu.trCorrespondence: huseyin.kamaci@hotmail.comAbstract. In this paper, firstly, novel approaches of score function and accuracy function are introduced to achievemore practical and convincing comparison results of two neutrosophic cubic values. Furthermore, the neutrosophic cubicHamacher weighted averaging operator and the neutrosophic cubic Hamacher weighted geometric operator are developedto aggregate neutrosophic cubic values. Some desirable properties of these operators such as idempotency, monotonicityand boundedness are discussed. To deal with the multi-criteria decision making problems in which attribute valuestake the form of the neutrosophic cubic elements, the decision making algorithms based on some Hamacher aggregationoperators, which are extensions of the algebraic aggregation operators and Einstein aggregation operators, are constructed.Finally, the illustrative examples and comparisons are given to verify the proposed algorithms and to demonstrate theirpracticality and effectiveness.Keywords: Neutrosophic set; Neutrosophic cubic set; Score function; Accuracy function; Hamacher operations; �————————————-1. IntroductionIn real life, there are many problems with inconsistent, indeterminate and incomplete informationwhich cannot be described by crisp numbers. Under these circumstances, Zadeh [34] proposed thefuzzy set, which is an effective method to deal with such problems. To express uncertainty, Sambuc [26] extended the fuzzy set and initiated the interval valued fuzzy theory. In [33], the researchersdiscussed the multipolar types of fuzzy sets. In 2012, Jun combined the idea of fuzzy sets and intervalvalued fuzzy sets to form cubic sets. Some researchers used the cubic sets in different directions to havemore applications [23, 24]. In some situations, hesitancy may exist when ones determine the membership degree of an object. Torra [29] improved the hesitant fuzzy set to depict this hesitant information.Moreover, Smarandache [27] introduced the neutrosophic set to reflect the truth, indeterminate andHüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 2020235false information simultaneously. In addition, Wang et al. pointed out that the neutrosophic set is difficult to truly apply to practical problems in real world scenarios. To overcome this flaw, they proposedsingle valued neutrosophic sets [32]. In addition, they put forward that in many real life problems, thedegrees of truth, indeterminacy and falsity of a certain statement may be adaptly preferred by intervalforms, instead of real numbers [31]. Moreover, many papers were published on the neutrosophic set’scase studies [1, 2, 19, 20, 30], their some extensions [3–5, 12, 16], and combining with other theories, likegraph theory [11, 18], soft set theory [8, 15, 28], rough set theory [6].By combining the single valued neutrosophic set and interval neutrosophic set, Jun et al. [14], and Aliet al. [7] introduced the notion of neutrosophic cubic set. These sets enable us to choose both intervalvalues and single values for the membership, indeterminacy and non-membership. This characteristicof neutrosophic cubic sets enables us to deal with ambiguous and uncertain data more efficiently. Inaddition, the application of sundry extensions of neutrosophic cubic sets studied by researchers in avariety of fields, like decision-making, supplier selection and similarity measure [9, 21, 22, 35].The aggregation operators are an indispensable part of decision making in neutrosophic cubic environments. In 2019, Khan et al. [17] developed the neutrosophic cubic Einstein weighted geometricoperator, and also defined the score and accuracy functions to reveal the superiority among the neutrosophic cubic numbers. It is known that Einstein t-norm and Einstein t-conorm are special formsof Hamacher t-norm and Hamacher t-conorm respectively, that is, Hamacher t-norm and Hamachert-conorm are the more general version. This paper aims to introduce the neutrosophic cubic Hamacherweighted averaging operator and neutrosophic cubic Hamacher weighted geometric operator, whichgeneralize the aggregation operators proposed by Khan et al. [17]. Furthermore, it proposes new scorefunction and accuracy function, which provide more efficient outputs than Khan et al.’s functions.By using these emerging operators and functions, the phenomenal algorithms are elaborated to solvemulti-criteria decision making problems. The contributions of this study can be summarized as follows. The models are proposed to compare neutrosophic cubic numbers, and the operators which aremore efficient than some existing netrosophic cubic aggregation operators are developed. In additionto these, it is instilled that these concepts can be used to handle the problems with neutrosophic cubicinformation.This paper is arranged as follows. Section 2 presents some fundamental concepts of fuzzy set, neutrosophic set, interval neutrosophic set, cubic set and neutrosophic cubic set. Section 3 presents comparison strategy of two neutrosophic cubic elements. Section 4 is devoted to improve the Hamacheroperations of neutrosophic cubic elements. Section 5 introduces neutrosophic cubic Hamacher weightedaggregation operators and their basic properties. Section 6 is devoted to proposing the neutrosophiccubic decision making algorithms with possible applications and analyzing the ranking order withdifferent reducing factors. Section 7 is the conclusion and the future scope of research.Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 20202362. PreliminariesIn this part, we briefly remind the definitions of fuzzy set, neutrosophic set, interval neutrosophic set,cubic set, neutrosophic cubic set and neutrosophic cubic element.Definition 2.1. ( [34]) Let O be a universal set. Then, a fuzzy set (FS) Ψ in O is defined byΨ {µΨ (o)/o : o O}where µΨ : O [0, 1] is said to be the membership function and µΥ (o) denotes the degrees ofmembership of o O to the set Ψ.Definition 2.2. ( [26]) Let O be a universal set and D[0, 1] be the set of all closed subintervals of thee in O is characterized byinterval [0,1]. Then, an interval-valued fuzzy set (IFS) Ψe {eΨµΨe (o)/o : o O}where µeΨµLe , µeUe ] : O D[0, 1] is said to be the membership function, and µeLe (o) and µeUe (o) (wheree [eΨ ΨΨΨeµeL (o) µeU (o)) denote the lower degree and upper degree of membership of o O to the set Ψ,eΨeΨrespectively.Definition 2.3. ( [27]) Let O be a universal set. Then, a nuetrosophic set (NS) Υ in O is describedin the following formΥ {(µΥ , ιΥ , ηΥ )/o : o O}where µΥ , ιΥ , ηΥ : O ]0 , 1 [ are said to be the functions of membership, indeterminacy and nonmembership, respectively. Also, µΥ (o), ιΥ (o) and ηΥ (o) denote the degrees of membership, indeterminacy and non-membership of o O to the set Υ respectively.Definition 2.4. ( [32]) Let O be a universal set. Then, a single nuetrosophic set Γ in O is describedin the following formΥ {(µΥ , ιΥ , ηΥ )/o : o O}where µΥ , ιΥ , ηΥ : O [0, 1] are called the functions of membership, indeterminacy and nonmembership, respectively. Also, µΥ (o), ιΥ (o) and ηΥ (o) denote the degrees of membership, indeterminacy and non-membership of o O to the set Υ respectively.Remark. Throughout the paper, Υ means the single valued neutrosophic set.Definition 2.5. ( [31]) Let O be a universal set and D[0, 1] be the set of all closed subintervals of theinterval [0,1]. Then, an interval neutrosophic set (INS) Υ in O is characterized bye {(eeΥΥµΥιΥe,ee,ηe )/o : o O}where µeΥιΥeΥe,ee,ηe : O D[0, 1] are termed to be the functions of membership, indeterminacy andnon-membership, respectively. Also, µeLe (o), µeUe (o) denote the lower and upper degrees of membership,ΥΥeιLe (o), eιUe (o) denote the lower and upper degrees of indeterminacy and ηeLe (o), ηeUe (o) denote the lowerΥΥΥΥand upper degrees of non-membership, respectively.Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 2020237Definition 2.6. ( [13]) Let O be a universal set. Then, a cubic set (CS) in O is a structure in thefollowing forme {(Ψ(o),Ψ(o))/o : o O}e is an IFS in O and Ψ is an FS in Owhere ΨDefinition 2.7. ( [7, 14]) Let O be a universal set. Then, a neutrosophic cubic set (NCS) Λ in O is astructure in the following formeΛ {(Υ(o),Υ(o))/o : o O}e is an INS in O and Υ is an NS in Owhere ΥSimply, the structure of neutrosophic cubic set can be considered as followse {((eΥµΥιΥeΥe (o), ee (o), ηe (o)), (µΥ (o), ιΥ (o), ηΥ (o)))/o : o O}Furthermore, ((eµΥιΥeΥe (o), ee (o), ηe (o)), (µΥ (o), ιΥ (o), ηΥ (o))), which is an element in Λ, is called a neutrsophic cubic element (NCE). For simplicity, an NCE is denoted by υk (eµk , eιk , ηek , µk , ιk , ηk ).Example 2.8. Suppose that O {o1 , o2 , o3 , o4 } be a universal set. Then,(i): a fuzzy set Ψ in O can be exemplified as follows.Ψ {0.3/o1 , 0.7/o2 , 1/o3 , 0.1/o4 }.e in O can be illustrated as follows.(ii): an interval-valued fuzzy set Ψe {[0.3, 0.4]/o1 , [0.4, 0.7]/o2 , [0, 1]/o3 , [0.1, 0.1]/o4 }Ψ(iii): As a sample of a neutrosophic set Υ in O, the following set can be given.Υ {(0.3, 0.7, 0.2)/o1 , (0.1, 0.1, 0.1)/o2 , (1, 0.7, 0.3)/o3 , (0, 0, 0.9)/o4 }e in O can be shown in the following form.(iv): an interval neutrosophic set Υ(e Υ([0.2, 0.6], [0.4, 0.4], [0.1, 0.8])/o1 , ([0.5, 1], [0.3, 0.4], [0.6, 0.7])/o2 ,([0, 0], [0.1, 0.8], [0.2, 0.4])/o3 , ([0.1, 0.4], [0.3, 0.5], [0.2, 0.2])/o4).(v): a cubic set in O is can be exemplified as follows. {([0.2, 0.6], 0.5)/o1 , ([0.1, 0.5], 0.2)/o2 , ([0.5, 0.7], 1)/o3 , ([0.1, 1], 0.4)/o4 }.(vi): a neutrosophic cubic set Λ in O is an object having the following form (([0.1, 0.4], [0.1, 0.4], [0.3, 0.6]), (0.5, 0.3, 0.8))/o1 , (([0.8, 0.9], [0.1, 0.7], [0.2, 0.7]), (0.6, 1, 0.7))/o ,2Λ (([0.3,1],[0,0.5],[0.4,0.6]),(0,0.3,0.7))/o, 3 (([0.4, 0.9], [0.2, 0.2], [0.6, 0.8]), (0.1, 0.1, 0.1))/o4 . Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 20202383. Score and Accuracy Functions of Neutrosophic Cubic ElementWe can develop the score and accuracy functions to compare two NCEs. For comparison of two NCEs,firstly, we use the score functions, sometimes the score values of two NCEs can be equal althoughthey have different components of membership, indeterminacy and non-membership functions. In suchcases, it is aimed to achieve a ranking priority between the NCEs using the accuracy function.Definition 3.1. Let υk (eµk , eιk , ηek , µk , ιk , ηk ) be an NCE, where µek [eµLeUιk [eιLιUk,µk ], ek ,ek ] andηek [eηkL , ηekU ]. Then, the score function fscr is defined byfscr 12 6 (eµLeUιLιUηkL ηekU ) 3 µk 2ιk ηkk µk ) 2(ek ek ) (e.8(1)Proposition 3.2. The score function of any NCE lies between 0 to 1, i.e., fscr (υk ) [0, 1] for any υk .Proof. Consider υk (eµk , eιk , ηek , µk , ιk , ηk ). By using the definitions of INS and NS, we have all µeLk,ηkU , µk , ιk , ηk [0, 1].ekL ,eιUιLµeUk, ηk, ek,eThen, it is easily seen thateUeLeU0 µeLk 2,k µk 1 0 µk 1, 0 µ(2)0 eιLιUιLιUιLιUk ) 0,k 1, 0 ek 1 0 ek ek 2 4 2(ek e(3)ηkL ηekU 0.0 ηekL 1, 0 ηekU 1 0 ηekL ηekU 2 2 e(4)andBy adding Eqs. (2), (3) and (4), we obtainηkL ηekU ) 2ιUιLeU 6 (eµLk ) (ek ek ) 2(ek µ1ηkL ηekU ) 4ιUµLeUιL 0 6 (ek ek ) (ek ) 2(ek µ2(5)In addition, we obtain0 µk 1, 2 2ιk 0, 1 ηk 0 3 µk 2ιk ηk 1 0 3 µk 2ιk ηk 4.(6)By adding Eqs. (5) and (6) and then dividing by 8, we have0 12 6 (eµLeUιLιUηkL ηekU ) 3 µk 2ιk ηkk µk ) 2(ek ek ) (e 1.8This result completes the proof.Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making(7)
Neutrosophic Sets and Systems, Vol. 33, 2020239Definition 3.3. Let υk (eµk , eιk , ηek , µk , ιk , ηk ) be an NCE, where µek [eµLeUιk [eιLιUk ,ek ] andk,µk ], eηek [eηkL , ηekU ]. Then, the accuracy function facr is defined by 1eLeUιLιUekL ηekU µk ιk ηkk µk ek ek η2 µfacr .6(8)Proposition 3.4. The accuracy function of any NCE lies between 0 to 1, i.e., facr (υk ) [0, 1] for anyυk .Proof. Consider υk (eµk , eιk , ηek , µk , ιk , ηk ). Since µeLeUιLιUekL ,eηkU , µk , ιk , ηk [0, 1] from thek, µk, ek, ek, ηdefinitions of INS and NS, it is obvious that0 1 Lµek µeUιLιUekL ηekU µk ιk ηk 6.k ek ek η2(9)Dividing by 6, we have12 µeLeUιLιUekL ηekU µk ιk ηkk µk ek ek η0 1.6Thus, the proof is complete.(10)The following definition is proposed to compare two NCEs, thereby ensuring the order priority betweenthe NCEs.Definition 3.5. Let υ1 and υ2 be two NCEs. The comparison method for any two NCEs υ1 and υ2is defined as follows:(1) If fscr (υ1 ) fscr (υ1 ) then υ1 υ2(2) fscr (υ1 ) fscr (υ1 ) then υ1 υ2(3) fscr (υ1 ) fscr (υ1 ) then when facr (υ1 ) facr (υ1 ), υ1 υ2 when facr (υ1 ) facr (υ1 ), υ1 υ2 when facr (υ1 ) facr (υ1 ), υ1 υ2Example 3.6. We consider any two NCEs as υ1 ([0.4, 0.6], [0.3, 0.4], [0.4, 0.5], 0.8, 0.6, 0.5) andυ2 ([0.5, 0.7], [0.2, 0.5], [0.5, 0.6], 0.5, 0.6, 0.2).Then, it is obtain fscr (υ1 ) fscr (υ2 ) 0.5968.If we compare this two NCEs by using the accuracy functions, then we have υ1 υ2 sincefacr (υ1 ) 0.5333 0.4666 facr (υ2 ).4. Hamacher Operations of Neutrosophic Cubic ElementsThe concepts of t-norm and t-conorm, which are useful notions in fuzzy set theory and neutrosophic settheory, are proposed by Roychowdhury and Wang [25]. In 1978, Hamacher [10] defined Hamacher sum( ) and Hamacher product ( ), which are samples of t-conorm and t-norm, respectively. Hamachert-norm and Hamacher t-conorm are given as follows.For all â, b̂ [0, 1],Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 2020240â b̂ â b̂ âb̂ (1 ξ)âb̂,1 (1 ξ)âb̂â b̂ âb̂ξ (1 ξ)(â b̂ âb̂)where ξ 0.Especially, if it is taken ξ 1, then Hamacher t-norm and Hamacher t-conorm will reduce to the formâ b̂ â b̂ âb̂,â b̂ âb̂which represent algebraic t-norm and t-conorm, respectively.If it is taken ξ 2, then Hamacher t-norm and Hamacher t-conorm will conclude to the formâ b̂ â b̂ â b̂,1 âb̂âb̂1 (1 â)(1 b̂)which are called Einstein t-norm and Einstein t-conorm, respectively.By using the Hamacher t-norm and Hamacher t-conorm, we can create the Hamacher sum andHamacher product of two NCEs.Definition 4.1. Let υ1 (eµ1 , eι1 , ηe1 , µ1 , ι1 , η1 ) and υ2 (eµ2 , eι2 , ηe2 , µ2 , ι2 , η2 ) be two CNEs and ξ 0,then the operational rules based on the Hamacher t-norm and Hamacher t-conorm are established asfollows:(a): L L L L µe1 eµ2 eµ1 µe2 (1 ξ)eµLeLµeUµUµUeUµUeU1µ21 e2 e1 µ2 (1 ξ)e1 µ2,,LLUU1 (1 ξ)eµ1 µe21 (1 ξ)eµ1 µe2 UeUL eιιeιLeι1 22 ξ (1 ξ)(eι1L e,L ιLeL ,ιUιUιUιU1 ι2 e1 ι2 ) ξ (1 ξ)(e1 e2 e1e2 )υ1 υ2 LL ηe1 ηe2ηe1U ηe2U ,,LLLLUUUU ξ (1 ξ)(eη1 eη2 eη1 ηe2 ) ξ (1 ξ)(eη1 eη2 eη1 ηe2 )ι1 ι2 ι1 ι2 (1 ξ)ι1 ι2 η1 η2 η1 η2 (1 ξ)η1 η2µ1 µ2,ξ (1 ξ)(µ1 µ2 µ1 µ2 ) ,1 (1 ξ)ι1 ι21 (1 ξ)η1 η2 µeUeUµeLeL1µ21 µ2L eL eLµL ) , ξ (1 ξ)(eU eU eUµU) ,ξ (1 ξ)(eµµµeµµµe L L L 1L 2 1L 2L U U U 1U 2 1U 2U eι1 eι2 eι1 eι2 (1 ξ)eι1 eι2 eι1 eι2 eι1 eι2 (1 ξ)eι1 eι2 ,,1 (1 ξ)eιLιL1 (1 ξ)eιUιU1e21e2υ1 υ2 UUULLUU ηe1L eη2L eη1L ηe2L (1 ξ)eη2 eη1 ηe2 ηe1 eη1 ηe2 (1 ξ)eη1 ηe2U ,,LηLUηU1 (1 ξ)eηe1 (1 ξ)eηe 1 21 2µ1 µ2 µ1 µ2 (1 ξ)µ1 µ2η1 η22, ξ (1 ξ)(ιι11ι ι,1 (1 ξ)µ1 µ22 ι1 ι2 ) ξ (1 ξ)(η1 η2 η1 η2 ) . (11)(b): . (12)(c): qqqq(1 (ξ 1)eµLµL(1 (ξ 1)eµUµU1 ) (1 e1)1 ) (1 e1 )L )q (ξ 1)(1 eL )q , (1 (ξ 1)eU )q (ξ 1)(1 eU )q ,(1 (ξ 1)eµµµµ1111 ξ(eιL )qξ(eιU )q (1 (ξ 1)(1 eιL1))q (ξ 1)(eιL )q , (1 (ξ 1)(1 eιU1))q (ξ 1)(eιU )q , ,1111qυ1 ξ(eη L )qξ(eη U )q (1 (ξ 1)(1 eηL1))q (ξ 1)(eηL )q , (1 (ξ 1)(1 eηU1))q (ξ 1)(eηU )q , , 1111ξµq1(1 (ξ 1)ι1 )q (1 ι1 )q(1 (ξ 1)η1 )q (1 η1 )q,,q(1 (ξ 1)(1 µ ))q (ξ 1)µ (1 (ξ 1)ι1 )q (ξ 1)(1 ι1 )q (1 (ξ 1)η1 )q (ξ 1)(1 η1 )q1 (13)1Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 2020241where q 0.(d): qqξ(eµUξ(eµL1 )1),,,LLUUqqqqµ1 )) (ξ 1)(eµ1 ) (1 (ξ 1)(1 eµ1 )) (ξ 1)(eµ ) (1 (ξ 1)(1 e 1U )q (1 eU )qq (1 eL )q (1 (ξ 1)eιι(1 (ξ 1)eιL)ι111 (1 (ξ 1)eιL )q1 (ξ 1)(1 eq , (1 (ξ 1)eqq ,ιLιUιU11)1 ) (ξ 1)(1 e1 )υ1q (1 (ξ 1)eη1L )q (1 eη1L )q(1 (ξ 1)eη1U )q (1 eη1U )q (1 (ξ 1)eηL )q (ξ 1)(1 eηL )q , (1 (ξ 1)eηU )q (ξ 1)(1 eηU )q , 1111ξη1qξιq1(1 (ξ 1)µ1 )q (1 µ1 )q,q,qqq(1 (ξ 1)µ1 ) (ξ 1)(1 µ1 ) (1 (ξ 1)(1 ι )) (ξ 1)ι (1 (ξ 1)(1 η ))q (ξ 1)η q111 (14)1where q 0.Example 4.2. Assume that two NCEs are υ1 ([0.4, 1], [0.7, 0.8], [0, 0.2], 0.5, 0, 0.7) and υ1 ([0.2, 0.4], [0.5, 0.6], [0.5, 0.6], 0.1, 1, 0.4) and q 2. Then, for ξ 3υ1 υ2 ([0.8095, 1], [0.2692, 0.4137], [0, 0.0731], 0.0263, 1, 0.8846),υ1 υ2 ([0.0408, 0.4], [0.9117, 0.9591], [0.5, 0.7419], 0.5909, 0, 0.2058),qυ2 ([0.4074, 0.7272], [0.1666, 0.2727], [0.1666, 0.2727], 0.0038, 1, 0.7272),υ1q ([0.1237, 1], [0.9545, 0.9824], [0, 0.4074], 0.8333, 0, 0.4152).Proposition 4.3. Let υ1 and υ2 be two NCEs and q, q 0 0.(1): υ1 υ2 υ2 υ1 .(2): υ1 υ2 υ2 υ1 .(3): q(υ1 υ2 ) qυ1 qυ2 .(4): qυ1 q 0 υ1 ) (q q 0 )υ1 .(5): (υ1 υ2 )q υ1q υ2q .00(6): υ1q υ1q υ1q q .Proof. They are easily seen from the formulas in Definition 4.1, hence omitted.5. Neutrosophic Cubic Hamacher Weighted Aggregation OperatorsIn this section, we will introduce the neutrodophic cubic Hamacher weighted averaging operator andneutrosophic cubic Hamacher weighted geometric operator.Definition 5.1. Let υk (k 1, 2, ., r) be a collection of the CNEs. Then, neutrosophic cubicHamacher weighted averaging (NCHWA) operator is defined as the mapping N CHW A : N r Nsuch thatN CHW A (υ1 , υ2 , ., υr ) rMk 1where N is the set of all NCEs and ( 1 , 2 , ., rrP k [0, 1] and k 1.)T ( k υk )(15)is weight vector of (υ1 , υ2 , ., υr ) such thatk 1Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 2020242Theorem 5.2. The aggregation value of NCEs by using the NCHWA operator is still an NCE, andevenN CHW A (υ1 , υ2 , ., υr ) rMk 1 ( k υk ) rQrrrQQQ k k k k(1 (ξ 1)eµL (1 eµL(1 (ξ 1)eµU (1 eµUk)k)k)k)k 1k 1k 1k 1rrrrQQQQ(1 (ξ 1)eµL) k (ξ 1)(1 (ξ 1)eµU) k (ξ 1)(1 eµL) k(1 eµU) kkkkkk 1k 1k 1k 1rrQ L Q U ξ(eιk ) kξ(eιk ) kk 1k 1rrrrQQQ L Q(1 (ξ 1)(1 eιL)) k (ξ 1)(1 (ξ 1)(1 eιU)) k (ξ 1)(eιk ) k(eιU) kkkkk 1k 1k 1k 1rrQQL kU k)ξ(eηkξ(eηk)k 1k 1rrrrQQQQ LULU ) k(1 (ξ 1)(1 eηk )) k (ξ 1)(1 (ξ 1)(1 eηk )) k (ξ 1)(eηk ) k(eηkk 1k 1k 1k 1rrrrrQQQQQ(1 (ξ 1)ιk ) k (1 ιk ) k(1 (ξ 1)ηk ) k (1 ηk ) kξ(µk ) kk 1k 1k 1k 1k 1rrrrrrQQQQQQ(1 (ξ 1)(1 µk )) k (ξ 1)(1 (ξ 1)ιk ) k (ξ 1)(1 (ξ 1)ηk ) k (ξ 1)(µk ) k(1 ιk ) k(1 ηk ) kk 1k 1k 1k 1k 1k 1hi, h h ,,i,,i, ,, (16) Proof. This can be proved by mathematical induction.When r 1, for the left side of Eq. (16), N CHW A (υ1 ) 1 υ1 υ1 and for the right side of Eq.(16) we have 1 (ξ 1)eµUµU1 (ξ 1)eµLµL1 (1 e1 )1 (1 e1),,LLUUµ1 ) 1 (ξ 1)eµ1 (ξ 1)(1 eµ1 ) 1 (ξ 1)eµ1 (ξ 1)(1 e U ξeιLξeι 1 (ξ 1)(1 eι1L ) (ξ 1)eιL , 1 (ξ 1)(1 eι1U ) (ξ 1)eιU , 1111 ξeη1Lξeη1U ,,LLUU 1 (ξ 1)(1 eη1 ) (ξ 1)eη1 1 (ξ 1)(1 eη1 ) (ξ 1)eη11 (ξ 1)ι1 (1 ι1 )1 (ξ 1)η1 (1 η1 )ξµ11 (ξ 1)(1 µ1 ) (ξ 1)µ1 , 1 (ξ 1)ι1 (ξ 1)(1 ι1 ) , 1 (ξ 1)η1 (ξ 1)(1 η1 ) . Suppose that Eq. (16) holds for r t, i.e., we haveN CHW A (υ1 , υ2 , ., υt ) tMk 1 ( k υk ) htttQQQ k k k k(1 (ξ 1)eµU (1 eµU(1 (ξ 1)eµL (1 eµLk)k)k)k)k 1k 1k 1k 1ttttQQQQ k k k k(1 (ξ 1)eµL (ξ 1)(1 eµL(1 (ξ 1)eµU (ξ 1)(1 eµUk)k)k)k)k 1k 1k 1k 1ttQQ k kξ(eιLξ(eιUk)k)k 1k 1ttttQQQQ k k k k(1 (ξ 1)(1 eιL (ξ 1)(eιL(1 (ξ 1)(1 eιU (ξ 1)(eιUk ))k)k ))k)k 1k 1k 1k 1ttQQL kU kξ(eηk)ξ(eηk)k 1k 1ttttQQQQL )) k (ξ 1)L ) kU )) k (ξ 1)U ) k(1 (ξ 1)(1 eηk(eηk(1 (ξ 1)(1 eηk(eηkk 1k 1k 1k 1tttQQQξ(µk ) k(1 (ξ 1)ιk ) k (1 ιk ) k tQ h h tQi,,i,,,k 1(1 (ξ 1)(1 µk )) k (ξ 1)k 1tQ,(µk ) kk 1k 1tQk 1(1 (ξ 1)ιk ) k (ξ 1)k 1tQ,(1 ιk ) kk 1i,tQ(1 (ξ 1)ηk ) k k 1tQtQ(1 (ξ 1)ηk ) k (ξ 1)k 1(1 ηk ) kk 1tQ(1 ηk ) kk 1Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making .
Neutrosophic Sets and Systems, Vol. 33, 2020243When r t 1,N CHW A (υ1 , υ2 , ., υt 1 ) N CHW A (υ1 , υ2 , ., υt ) ( t 1 υt 1 ) tQh k(1 (ξ 1)eµL k)tQtQ k(1 eµLk) k(1 (ξ 1)eµU k)tQ k(1 eµUk)ik 1k 1k 1k 1 , Q,tttt QQQ k k k kLLU (ξ 1)(1 eµk ) (ξ 1)(1 eµU(1 (ξ 1)eµk )(1 (ξ 1)eµk )k) k 1k 1k 1k 1tt QQU k ki hξ(eι)ξ(eιL)kk k 1k 1,, QttttQ L Q U Q kL k k (ξ 1)(eιk ) k(1 (ξ 1)(1 eιUk )) k 1(1 (ξ 1)(1 eιk )) (ξ 1) k 1(eιk )k 1k 1 ttQQU kL k hiξ(eηkξ(eηk)) k 1k 1 Q,,ttttQQQ L U L U k 1(1 (ξ 1)(1 eηk )) k (ξ 1) k 1(eηk ) k k 1(1 (ξ 1)(1 eηk )) k (ξ 1) k 1(eηk ) k tttttQQQQQ ξ(µk ) k(1 (ξ 1)ιk ) k (1 (ξ 1)ηk ) k (1 ιk ) k(1 ηk ) k k 1k 1k 1k 1k 1 Q,,ttttttQQQQQ(1 (ξ 1)(1 µk )) k (ξ 1)k 1(µk ) kk 1(1 (ξ 1)ιk ) k (ξ 1)k 1(1 ιk ) k(1 (ξ 1)ηk ) k (ξ 1)k 1k 1 (1 ηk ) kk 1 1 (ξ 1)eµL (1 eµL ) 1 (ξ 1)eµUµUt 1t 1t 1 (1 et 1 ),,LLUµt 1 ) 1 (ξ 1)eµt 1 (ξ 1)(1 eµUt 1 ) 1 (ξ 1)eµt 1 (ξ 1)(1 eU ξeιξeιL 1 (ξ 1)(1 eιL 1 ) (ξ 1)eιL , 1 (ξ 1)(1 eιUt 1) (ξ 1)eιU ,t 1t 1t 1t 1 UL ξeηt 1ξeηt 1 ,,LUL ) (ξ 1)eU ) (ξ 1)eηt 1ηt 11 (ξ 1)(1 eηt 1 1 (ξ 1)(1 eηt 1ξµt 11 (ξ 1)ιt 1 (1 ιt 1 )1 (ξ 1)ηt 1 (1 ηt 1 )1 (ξ 1)(1 µt 1 ) (ξ 1)µt 1 , 1 (ξ 1)ιt 1 (ξ 1)(1 ιt 1 ) , 1 (ξ 1)ηt 1 (ξ 1)(1 ηt 1 ) t 1Qh k (1 (ξ 1)eµLk)t 1Qt 1Q k(1 eµLk) k (1 (ξ 1)eµUk)t 1Q k(1 eµUk) . i,k 1k 1k 1k 1 , t 1 t 1t 1t 1QQ k (ξ 1) Q (1 eL ) kU ) k (ξ 1) Q (1 e (1 (ξ 1)eµL)µ(1 (ξ 1)eµµU) kkkkk k 1k 1k 1k 1 t 1t 1Q U Q L hiξ(eιk ) kξ(eιk ) k k 1k 1 t 1,,t 1t 1t 1QQ L QQ U )) k (ξ 1))) k (ξ 1)(1 (ξ 1)(1 eιL(eιk ) k(1 (ξ 1)(1 eιU(eιk ) k kkk 1k 1k 1k 1 t 1t 1QQL kU ki)ξ(eηkξ)(eηk hk 1k 1 ,, t 1t 1t 1t 1QQQQL )) k (ξ 1)U )) k (ξ 1)L ) kU ) k (1 (ξ 1)(1 eηk(1 (ξ 1)(1 eηk(eηk(eηk k 1k 1k 1k 1 t 1t 1t 1t 1t 1QQQQQ (1 (ξ 1)ιk ) k (1 ιk ) k(1 (ξ 1)ηk ) k (1 ηk ) kξ(µk ) k k 1k 1k 1k 1k 1 t 1,,t 1t 1t 1t 1t 1Q(1 (ξ 1)(1 µk )) k (ξ 1)k 1Q(µk ) kk 1Q(1 (ξ 1)ιk ) k (ξ 1)k 1Q(1 ιk ) kk 1Q(1 (ξ 1)ηk ) k (ξ 1)k 1Q(1 ηk ) k . , k 1So, Eq. (16) holds for r t 1. Thus, the proof is complete.Definition 5.3. Let υk (k 1, 2, ., r) be a collection of the CNEs. Then, neutrosophic cubicHamacher weighted geometric (NCHWG) operator is defined as the mapping N CHW G : N r Nsuch thatN CHW G (υ1 , υ2 , ., υr ) rOk 1 υk k(17)where N is the set of all NCEs and ( 1 , 2 , ., r )T is weight vector of (υ1 , υ2 , ., υr ) such thatrP k [0, 1] and k 1.k 1Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 2020244Theorem 5.4. The aggregation value of NCEs by using the NCHWG operator is still an NCE, andevenN CHW G (υ1 , υ2 , ., υr ) rOk 1 rQξh υk k k(eµLk)ξk 1rQ k(eµUk) ik 1 Qr (1 (ξ 1)(1 eµL )) k (ξ 1) Qr (eµL ) k , Qr (1 (ξ 1)(1 eµU )) k (ξ 1) Qr (eµU ) k ,kkkk k 1k 1k 1k 1 rrrrQQQQU kU k kL ki(1 (ξ 1)eι) (1 eι)(1 (ξ 1)eιL) (1 eι) h k 1kkkkk 1k 1k 1 Qr,,rrrQQQ L U L U k 1(1 (ξ 1)eιk ) k (ξ 1) k 1(1 eιk ) k k 1(1 (ξ 1)eιk ) k (ξ 1) k 1(1 eιk ) krr h Qr (1 (ξ 1)eηL ) k Qr (1 eηL ) kQQU kU ki(1 (ξ 1)eηk) (1 eηk) kkk 1k 1k 1 Qr k 1, Q,rrrQQ L U L U k 1(1 (ξ 1)eηk ) k (ξ 1) k 1(1 eηk ) k k 1(1 (ξ 1)eηk ) k (ξ 1) k 1(1 eηk ) krrr QQQξ(ιk ) k k 1(1 (ξ 1)µk ) k k 1(1 µk ) kk 1rQ(1 (ξ 1)µk ) k (ξ 1)k 1rQ,(1 µk ) krQ(1 (ξ 1)(1 ιk )) k (ξ 1)k 1k 1rQ(ιk ) k,rQξrQ(ηk ) kk 1rQ(1 (ξ 1)(1 ηk )) k (ξ 1)k 1k 1 (18) (ηk ) kk 1Proof. It can be demonstrated similar to the proof of Theorem 5.2.Theorem 5.5. (Idempotency)Let υk (k 1, 2, ., r) be a collection of the NCEs. If υk υ for all k 1, 2, ., r then(1): N CHW A (υ1 , υ2 , ., υr ) υ.(2): N CHW G (υ1 , υ2 , ., υr ) υ.Proof. Let’s prove (2), the other can be proved similar to this. Assume υk υ for all k 1, 2, ., r.By Theorem 5.4, we obtain thatN CHW GΩ (υ1 , υ2 , ., υr ) rOk 1 ξhrQ(eµL ) k υ j k rOk 1υ j ξk 1rQ(eµU ) kk 1, Qrrrr QQQ(1 (ξ 1)(1 eµL )) k (ξ 1)(eµL ) k(1 (ξ 1)(1 eµU )) k (ξ 1)(eµU ) k k 1k 1k 1 k 1 QrrrrQQQ hi(1 (ξ 1)eιL ) k (1 eιL ) k(1 (ξ 1)eιU ) k (1 eιU ) k k 1k 1k 1k 1 Q, Q,rrrrQQ L L U U k 1(1 (ξ 1)eι ) k (ξ 1) k 1(1 eι ) k k 1(1 (ξ 1)eι ) k (ξ 1) k 1(1 eι ) krrrr QQU ki(1 (ξ 1)eηk) (1 eη U ) k h Q (1 (ξ 1)eηL ) k Q (1 eηL ) kk 1k 1k 1 r k 1,,rrrQQ Q (1 (ξ 1)eηL ) k (ξ 1) QL ) k(1 eηk(1 (ξ 1)eη U ) k (ξ 1)(1 eη U ) k k 1k 1k 1k 1 rrrQQQ (1 (ξ 1)µ) k (1 µ) kξ(ι) k k 1k 1k 1rQ(1 (ξ 1)µ) k (ξ 1)k 1rQ(1 µ) kk 1,rQ(1 (ξ 1)(1 ι)) k (ξ 1)k 1rQ(ι) kk 1,rQ i,ξrQ(η) kk 1(1 (ξ 1)(1 η)) k (ξ 1)k 1 υ. rQ(η) kk 1Theorem 5.6. (Monotonicity)Let υk and νk (k 1, 2, ., r) be two collections of the NCEs. If υk υk0 for all k 1, 2, ., r then(1): N CHW A (υ1 , υ2 , ., υr ) N CHW A (υ10 , υ20 , ., υr0 ).(2): N CHW G (υ1 , υ2 , ., υr ) N CHW G (υ10 , υ20 , ., υr0 ).Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 2020245Proof. (1) If υk υk0 then we have e0 L , µeL µ Lk e0 Lk e0 UµeUk µ kUe0eιk ι k , eιUk ι kLLLe0 Le0 , ηe ηηe ηkk kk. 0µk µ0k , ιk ι0k , ηk ηk(k 1,2,.,r)With these assumptions, we find thatrQ h k(1 (ξ 1)eµL k)rQrQ k(1 eµLk) k(1 (ξ 1)eµU k)rQ k(1 eµUk)ik 1k 1k 1k 1 Q,, QrrrrQQ k ) k) k (ξ 1)(1 eµL) k (ξ 1)(1 eµU(1 (ξ 1)eµL(1 (ξ 1)eµUkk)kk k 1k 1k 1k 1 rrQQ k k hiξ(eιU(eιLξk)k) k 1k 1, Q, QrrrrQQ k kL kL k (ξ 1)(eιU(1 (ξ 1)(1 eιUk)k )) k 1(1 (ξ 1)(1 eιk )) (ξ 1) k 1(eιk )k 1k 1 rrQQL kU k hiξ(eηkξ(eηk)) k 1k 1,, QrrrrQQQL ) kU ) kL )) k (ξ 1)U )) k (ξ 1) (eηk(eηk(1 (ξ 1)(1 eηk(1 (ξ 1)(1 eηk k 1k 1k 1k 1rrrrr QQQQQ ξ(µk ) k(1 (ξ 1)ιk ) k (1 ιk ) k(1 (ξ 1)ηk ) k (1 ηk ) k k 1k 1k 1k 1k 1,,rQrQ(1 (ξ 1)(1 µk )) k (ξ 1)k 1(µk ) kk 1rQ hL(1 (ξ 1)µe0 k ) k k 1rQrQ(1 (ξ 1)ιk ) k (ξ 1)k 1rQ(1 ιk ) kk 1rQL(1 µe0 k ) kk 1U(1 (ξ 1)µe0 k ) k k 1rQ(1 (ξ 1)ηk ) k (ξ 1)k 1rQrQ(1 ηk ) k , k 1U (1 µe0 k ) kik 1 Q, Q,rrrrQQ e0 L e0 U e0 L e0 U k 1(1 (ξ 1)µ k ) k (ξ 1) k 1(1 µ k ) k k 1(1 (ξ 1)µ k ) k (ξ 1) k 1(1 µ k ) k rrQQLU hiξ(ιe0 k ) kξ(ιe0 k ) k k 1k 1,, QrrrrQQ e0 L Q e0 U Ue0 L k k(1 (ξ 1)(1 ιe0 k )) k (ξ 1)(ι k ) k k 1(1 (ξ 1)(1 ι k )) (ξ 1) k 1(ι k )k 1k 1 rrQQUL hiξ(ηe0 k ) kξ(ηe0 k ) k k 1k 1,, QrrrrQQQUUe0 L ke0 L k (ηe0 k ) k(1 (ξ 1)(1 ηe0 k )) k (ξ 1) k 1(1 (ξ 1)(1 η k )) (ξ 1) k 1(η k )k 1k 1rrrrr QQQQQ0 k0 k ξ(µ0k ) k(1 (ξ 1)ι0k ) k (1 (ξ 1)ηk(1 ι0k ) k) (1 ηk) k 1k 1k 1k 1k 1,,rQ(1 (ξ 1)(1 µ0k )) k (ξ 1)k 1ThenrMk 1 ( k υk ) rMk 1 rQ(µ0k ) kk 1rQ(1 (ξ 1)ι0k ) k (ξ 1)k 1rQ(1 ι0k ) kk 1rQ0 ) k (ξ 1)(1 (ξ 1)ηkk 1rQ0 ) k(1 ηkk 1( k υk0 ), so N CHW A (υ1 , υ2 , ., υr ) N CHW A (υ10 , υ20 , ., υr0 ).(2) It is shown similar to the proof of (1) by using Eq. (18).Theorem 5.7. (Boundedness Property)Let υk (k 1, 2, ., r) be a collection of the NCEs. Then(1): υmin N CHW A (υ1 , υ2 , ., υr ) υmax(2): υmin N CHW G (υ1 , υ2 , ., υr ) υmaxwhereυmin min{eµLµUk }, min{ek } , max{eιLιUk }, max{ek } , Lmax{eηk }, max{eηkU } , max{µk }, min{ιk }, min{ηk } (k 1,2,.,r)Hüseyin Kamacı, Neutrosophic Cubic Hamacher Aggregation Operators and Their Applications in Decision Making
Neutrosophic Sets and Systems, Vol. 33, 2020246andυmax max{eµLµUk }, max{ek } , min{eιLιUk }, min{ek } , min{eηkL }, min{eηkU } , min{µk }, max{ιk }, max{ηk } . (k 1,2,.,r)Proof. They can be proved using similar techniques, therefore omitted.6. The approaches to multiple-criteria decision making under neutrosophic cubic environmentLet oi (i 1, 2, . . . , p) be a fixed of alternatives, ek (k 1, 2, . . . , r) be a criterion and k(k 1, 2, . . . , r) be the weight of criterion ek (k 1, 2, . . . , r) respectively such that k [0, 1]rPand k 1. Let υki denotes the neutrosophic cubic element (NCE) of the alternative oi withk 1respect to criterion ek .Algorithm 1.Step 1. Obtain the aggregation value υ i of neutrosophic cubic elements υ1i , υ2i , . . . , υri by using ofneutrosophic cubic Hamacher weighted averaging (NCHWA) operator or neutrosophic cubicHamacher weighted geometric (NCHWG) operator, i.e., respectivelyrMiiiN CHW A (υ1 , υ2 , . . . , υr ) ( k υki ) i 1, 2, . . . , porN CHW G (υ1i , υ2i , . . . , υri ) k 1 rOk 1 (υki ) k i 1, 2, . . . , p.Step 2. Compute the value of score function fscr (υ i ) i 1, 2, . . . , p. If fscr (υ p1 ) fscr (υ p2 ) for anyp1 , p2 {1, 2, . . . , p}, then compute the values of accuracy function facr (υ p1 ) and fscr (υ p2 ) tocompare these alternatives.Step 3. Find the optimal alternative according to the
Hamacher weighted averaging operator and the neutrosophic cubic Hamacher weighted geometric operator are developed to aggregate neutrosophic cubic values. Some desirable properties of these operators such as idempotency, monotonicity and boundedness are discussed. To deal with the multi-cr
Citation: Liang W, Zhao G, Luo S (2018) Linguistic neutrosophic Hamacher aggregation operators and the application in evaluating land reclamation schemes for mines. PLoS ONE 13(11): e0206178. . Hamacher t-norm and t-conorm, can be also utilized to model the intersection and union of various fuzzy sets [36]. A great number of extended Hamacher .
Laws of Classical Logic That Do Not Hold in The Interval Neutrosophic Logic 184 Modal Contexts 186 Neutrosophic Score Function 186 Applications. 187 Neutrosophic Lattices 188 Conclusion 191 CHAPTER XII Neutrosophic Predicate Logic 196 Neutrosophic Quantifiers 199 Neutrosophic Existential Quantifier. 199 Neutrosophic Universal Quantifier. 199
Content THEORY Definition of Neutrosophy A Short Historyyg of the Logics Introduction to Non-Standard Analysis Operations with Classical Sets Neutrosophic Logic (NL) Refined Neutrosophic Logic and Set Classical Mass and Neutrosophic Mass Differences between Neutrosophic Logic and Intuitionistic Fuzzy Logic Neutrosophic Logic generalizes many Logics
Neutrosophic Modal Logic Florentin Smarandache University of New Mexico, Mathematics & Science Department, 705 Gurley Ave., Gallup, NM 87301, USA. . modalities. It is an extension of neutrosophic predicate logic and of neutrosophic propositional logic. Applications of neutrosophic modal logic are to neutrosophic modal metaphysics. Similarly .
(of hole length) m3 cubic meter Mcf thousand cubic feet cm centimeter cm3/g cubic centimeter per gram Mcfd thousand cubic feet per day ft foot m3/d cubic meter per day cubic foot per minute cubic foot per short ton gallon gallon per minute hour inch kilogram kilo pascal pound (m3/d)/m cubic meter per day per meter (of hole length) MMcf
Size of Container Depth Height Width 1 or 1.5 cubic yard 2 cubic yard bin 3 cubic yard bin 4 cubic yard bin 6 cubic yard bin 15 cubic yard box 20 cubic yard box 30 cubic yard box 34.5 in x 40.5 in x 45 in x 55 in x . enclosure plans should be designed to allow drivers to pull through. Facility roadways must be designed in such a manner that .
integer neutrosophic complex numbers, rational neutrosophic complex numbers and real neutrosophic complex numbers and derive interesting properties related with them. Throughout this chapter Z denotes the set of integers, Q the rationals and R the reals. I denotes the indeterminacy and I 2 I. Further i is the complex number and i 2 -1 or .
ANALISIS PENERAPAN AKUNTANSI ORGANISASI NIRLABA ENTITAS GEREJA BERDASARKAN PERNYATAAN STANDAR AKUNTANSI KEUANGAN NO. 45 (STUDI KASUS GEREJA MASEHI INJILI DI MINAHASA BAITEL KOLONGAN) KEMENTERIAN RISET TEKNOLOGI DAN PENDIDIKAN TINGGI POLITEKNIK NEGERI MANADO – JURUSAN AKUNTANSI PROGRAM STUDI SARJANA TERAPAN AKUNTANSI KEUANGAN TAHUN 2015 Oleh: Livita P. Leiwakabessy NIM: 11042103 TUGAS AKHIR .
