Research article

Cubic m-polar fuzzy topology with multi-criteria group decision-making

  • The concept of cubic m-polar fuzzy set (CmPFS) is a new approach to fuzzy modeling with multiple membership grades in terms of fuzzy intervals as well as multiple fuzzy numbers. We define some fundamental properties and operations of CmPFSs. We define the topological structure of CmPFSs and the idea of cubic m-polar fuzzy topology (CmPF topology) with P-order (R-order). We extend several concepts of crisp topology to CmPF topology, such as open sets, closed sets, subspaces and dense sets, as well as the interior, exterior, frontier, neighborhood, and basis of CmPF topology with P-order (R-order). A CmPF topology is a robust approach for modeling big data, data analysis, diagnosis, etc. An extension of the VIKOR method for multi-criteria group decision making with CmPF topology is designed. An application of the proposed method is presented for chronic kidney disease diagnosis and a comparative analysis of the proposed approach and existing approaches is also given.

    Citation: Muhammad Riaz, Khadija Akmal, Yahya Almalki, S. A. Alblowi. Cubic m-polar fuzzy topology with multi-criteria group decision-making[J]. AIMS Mathematics, 2022, 7(7): 13019-13052. doi: 10.3934/math.2022721

    Related Papers:

    [1] Qiang Li, Jina Zhao . Extremal solutions for fractional evolution equations of order 1<γ<2. AIMS Mathematics, 2023, 8(11): 25487-25510. doi: 10.3934/math.20231301
    [2] Choukri Derbazi, Zidane Baitiche, Mohammed S. Abdo, Thabet Abdeljawad . Qualitative analysis of fractional relaxation equation and coupled system with Ψ-Caputo fractional derivative in Banach spaces. AIMS Mathematics, 2021, 6(3): 2486-2509. doi: 10.3934/math.2021151
    [3] Mohammed A. Almalahi, Satish K. Panchal, Fahd Jarad, Mohammed S. Abdo, Kamal Shah, Thabet Abdeljawad . Qualitative analysis of a fuzzy Volterra-Fredholm integrodifferential equation with an Atangana-Baleanu fractional derivative. AIMS Mathematics, 2022, 7(9): 15994-16016. doi: 10.3934/math.2022876
    [4] Xinwei Su, Shuqin Zhang, Lixin Zhang . Periodic boundary value problem involving sequential fractional derivatives in Banach space. AIMS Mathematics, 2020, 5(6): 7510-7530. doi: 10.3934/math.2020481
    [5] Xiaoxin Zuo, Weibing Wang . Existence of solutions for fractional differential equation with periodic boundary condition. AIMS Mathematics, 2022, 7(4): 6619-6633. doi: 10.3934/math.2022369
    [6] Abdulkafi M. Saeed, Mohammed A. Almalahi, Mohammed S. Abdo . Explicit iteration and unique solution for ϕ-Hilfer type fractional Langevin equations. AIMS Mathematics, 2022, 7(3): 3456-3476. doi: 10.3934/math.2022192
    [7] Naveed Iqbal, Imran Khan, Rasool Shah, Kamsing Nonlaopon . The fuzzy fractional acoustic waves model in terms of the Caputo-Fabrizio operator. AIMS Mathematics, 2023, 8(1): 1770-1783. doi: 10.3934/math.2023091
    [8] Shaoyuan Xu, Yan Han, Qiongyue Zheng . Fixed point equations for superlinear operators with strong upper or strong lower solutions and applications. AIMS Mathematics, 2023, 8(4): 9820-9831. doi: 10.3934/math.2023495
    [9] Dumitru Baleanu, Babak Shiri . Generalized fractional differential equations for past dynamic. AIMS Mathematics, 2022, 7(8): 14394-14418. doi: 10.3934/math.2022793
    [10] Choukri Derbazi, Qasem M. Al-Mdallal, Fahd Jarad, Zidane Baitiche . Some qualitative properties of solutions to a nonlinear fractional differential equation involving two Φ-Caputo fractional derivatives. AIMS Mathematics, 2022, 7(6): 9894-9910. doi: 10.3934/math.2022552
  • The concept of cubic m-polar fuzzy set (CmPFS) is a new approach to fuzzy modeling with multiple membership grades in terms of fuzzy intervals as well as multiple fuzzy numbers. We define some fundamental properties and operations of CmPFSs. We define the topological structure of CmPFSs and the idea of cubic m-polar fuzzy topology (CmPF topology) with P-order (R-order). We extend several concepts of crisp topology to CmPF topology, such as open sets, closed sets, subspaces and dense sets, as well as the interior, exterior, frontier, neighborhood, and basis of CmPF topology with P-order (R-order). A CmPF topology is a robust approach for modeling big data, data analysis, diagnosis, etc. An extension of the VIKOR method for multi-criteria group decision making with CmPF topology is designed. An application of the proposed method is presented for chronic kidney disease diagnosis and a comparative analysis of the proposed approach and existing approaches is also given.



    Fractional derivatives and integrals are generalization of derivatives and integrals to arbitrary non-integer orders. The theory of fractional calculus has been studied and applied to be valuable tools in the investigation and explanation of many phenomena in various fields such as chemistry, physics [1,2], engineering [3], economics [4], control theory [5], epidemiology [6,8,7], etc (see [10,11,9]). Differential equations involving time-fractional derivatives are more realistic to explain some phenomena than those of integer order in time because it can describe the rate of change that depends on the past state. Consequently, fractional derivatives have been investigated on qualitative and numerical aspects [12,13] for describing physical phenomena.

    There are various definitions of fractional derivatives and integrals, which include Riemann-Liouville, Caputo, Hilfer, Riesz, Erdelyi-Kober, Hadamard, etc [11,1,10,9]. Among these definitions, Caputo derivative and Riemann-Liouville are widely used by many researchers. In addition, they are derived from the corresponding fractional integral operators. The variation of fractional calculus in many different forms of fractional derivative and integral operators arises from various special functions. In one direction, the integral operators have been extended to include the weight function φ as a definition of generalized Caputo fractional derivative by Almeida [14]. This definition has the advantage in terms of accuracy in mathematical modeling if a function φ is appropriately selected. Later, Jarad and Abdeljawad [15] constructed the Laplace transform and its inverse operator which depends on another function φ for solving some fractional differential systems in the notion of φ-Caputo fractional derivative.

    Over the past years, there has been an essential development in fractional evolution equations since many problems occurring in science, engineering and economy can be formulated by fractional evolution equations. Evolution equations are generally used to interpret the changing and evolving over time of the system. For instance, reaction-diffusion equations in chemical physics and biology [16,17], Schrödinger equations in quantum mechanics [18,19], Navier-Stokes equation in fluid mechanics [20], and Black-Scholes equation in finance [21] are common examples of fractional evolution equations. The development in the theory of fractional evolution equations is an essential branch of fractional calculus ranging from the study of existence, uniqueness, stability [22,23], numerical techniques [24] and mathematical modeling [26,25]. In particular, the existence and uniqueness theorems for fractional evolution equations have been extensively studied by means of semigroup theory and fixed point theorems [24,27,28,29,30,31,32,33,34,35,36].

    Various types of fixed point theorems are extensively used as fundamental tools for proving the existence and uniqueness of solutions for fractional evolution equations. However, some fixed point theorems are non-constructive results. As we all know, the monotone iterative method [37] is a flexible and efficient technique that provides both existence and constructive results for nonlinear differential equations [38,39,40,42,41,43] in terms of the lower and upper solutions. Furthermore, it can contribute to several comparison results which are applicable tools for the study. In this work, we emphasize on using the monotone iterative method involving the construction of upper and lower solutions.

    In 2020, Gou and Li [44] investigated the existence and uniqueness of mild solutions for impulsive fractional evolution equations of Volterra and Fredholm types in an ordered Banach space E subject to the periodic boundary condition by means of monotone iterative method for the problem

    {CDα0u(t)+Au(t)=f(t,u(t),Gu(t),Hu(t)),tJ,ttkΔu|t=tk=Ik(u(tk)),k=1,2,,m,u(0)=u(ω)

    where CDα0 is the classical Caputo fractional derivative of order 0<α<1 with the lower limit zero, A:D(A)EE is a closed linear operator and A generates a C0semigroup {T(t)}t0 in E; fC(J×E×E×E,E) is a function, IkC(E,E) is an impulsive function, k=1,2,,m; J=[0,ω], J=J{t1,t2,,tm}, J0=[0,t1], Jk=(tk,tk+1], the {tk} satisfy 0=t0<t1<t2<<tm<tm+1=ω, mN; Δu(tk)=u(t+k)u(tk), u(t+k) and u(tk) represent the right and left limits of u(t) at t=tk, respectively. The operators G and H are Volterra integral operator and Fredholm integral operator, respectively, which are defined by

    Gu(t)=t0g(t,s)u(s)ds (1.1)

    where the integral kernel gC(Ω,R+) and Ω={(t,s)R2|0stT}, and

    Hu(t)=T0h(t,s)u(s)ds (1.2)

    with the integral kernel hC(¯Ω,R+) and ¯Ω={(t,s)R2|0t,sT}.

    Recently, Derbazi et al. [45] studied the existence and uniqueness of extremal solutions for fractional differential equations involving the φCaputo derivative subject to an initial condition:

    {CDα;φau(t)=f(t,u(t)),t[a,b]u(a)=a

    where CDα;φa is the φCaputo fractional derivative of order 0<α<1, f:[a,b]×RR is a given continuous function and aR.

    Inspired by [44,45], some monotone conditions and noncompactness measure conditions of nonlinearity f, we use the monotone iterative technique to establish the existence of solutions of fractional evolution equations in an order Banach space E given by

    {CDα;φt0u(t)=Au(t)+f(t,u(t),Gu(t),Hu(t)),t>t0u(t0)=u0 (1.3)

    where 0<α<1, 0t0tT< and u0E. Here A:D(A)EE is the infinitesimal generator of an analytic semigroup of uniformly bounded linear operators {T(t)}t0 on E. The nonlinearity f:[t0,T]×E×E×EE is a function involving the Volterra integral operator G and the Fredholm integral operator H defined by

    Gu(t)=tt0g(t,s)u(s)ds (1.4)

    where the integral kernel gC(Ω,R+) and Ω={(t,s)R2|t0stT}, and

    Hu(t)=Tt0h(t,s)u(s)ds (1.5)

    with the integral kernel hC(¯Ω,R+) and ¯Ω={(t,s)R2|t0t,sT}.

    The motivation for this work is taken by Derbazi et al. [45] and we apply the same techniques used in [45]. However, the generalization of this problem to our work involves evolution operator A. Hence, in order to establish the existence of solutions, it is required to derive the form of fundamental solution in terms of a semigroup induced by resolvent with respect to the weight function φ. Moreover, we notice that our problem (1.3) can be reduced to the work of Derbazi et al. [45] when the evolution operator A, and the operators G and H are taken to be zero operators on Banach space E=R.

    In this paper, we aim to derive a mild solution for the problem (1.3) in terms of semigroup depending on a function φ from Caputo fractional derivative. In addition, we construct lower and upper solutions to prove the existence and uniqueness results of mild solution for the problem (1.3) under the condition that {T(t)}t0 do not require compactness by using the monotone iterative technique. Moreover, the results obtained in this work are in the abstract form based on a more general definition of φCaputo fractional derivative so that it can be extended and generalized some results in the literature such as the impulsive evolution equations [44,42], the evolution equations with delay and nonlocal conditions.

    This manuscript is organized as follows. In Section 2, we recall basic concepts for fractional calculus and some known results used in the later. In section 3, we construct a mild solution of the Cauchy problem (1.3) in the form of operator semigroup involving a function φ which is obtained from the generalized Caputo derivative and then give the definitions of lower and upper solutions. Next, we investigate the existence and uniqueness results of mild solutions for the Cauchy problem (1.3) under the assumption that {T(t)}t0 does not require compactness by using the monotone iterative method in Section 4. Moreover, we provide an example to illustrate the results obtained in Section 5 and conclusion in Section 6.

    In this section, we recall some notations and definitions of fractional calculus and give auxiliary results which will be used in the sequel.

    We begin by introducing some properties of cones on real Banach spaces E.

    In cone P, a partially ordered is defined which means if xy if and only if yxP. If xy and xy, then we denote x<y or y>x.

    Definition 2.1. [46] The cone P is called

    (N) normal if there exists a constant N>0 such that x1Nx2 if θx1x2, for all x1,x2E. The least positive number satisfying above is called the normal constant of P.

    (R) regular if every increasing sequence which is bounded from above is convergent. That is, if {xn}n1 is a sequence such that

    x1x2y

    for some yE, then there is xE such that limnxnx=0.

    Lemma 2.2. [46,47]

    (i). Every regular cone is normal.

    (ii). The cone P is regular if and only if every decreasing sequence which is bounded from below is convergent.

    Theorem 2.3. [48] Let E be a weakly complete Banach space and P a cone in E. Then, P is normal if and only if P is regular.

    Definition 2.4. An operator family S(t)(t0) is said to be a positive operator in E if for any uP and t0 such that S(t)uθ.

    Here, we assume that E is an ordered Banach space with the norm and the partial order , whose positive cone P={xE:xθ} (θ is the zero element of E) is normal with normal constant N>0.

    Let C([t0,T],E) be the Banach space of all continuous maps from [t0,T] to E with the norm uC=supt[t0,T]u(t). For x1,x2C([t0,T],E), x1x2 if and only if x1(t)x2(t) for all t[t0,T]. For v,wC([t0,T],E), denote the ordered interval [v,w]={uC([t0,T],E):vuw} and [v(t),w(t)]={uE:v(t)uw(t)} for all t[t0,T].

    Next, we briefly highlight the definition and some basic properties of the φCaputo fractional derivative which are used throughout this paper.

    Definition 2.5. (φ-Riemann-Liouville fractional integral, [14]) Let α>0, uL1([a,b]) and φC1([a,b]) be a function such that φ(t)>0 for all t[a,b]. The φ-Riemann-Liouville fractional integral of order α of a function u with respect to another function φ is defined by

    (Iα;φau)(t)=1Γ(α)ta(φ(t)φ(s))α1u(s)φ(s)ds. (2.1)

    The above definition can be reduced to the classical Riemann-Liouville fractional integral when φ(t)=t.

    Definition 2.6. (φ-Riemann-Liouville fractional derivative, [14] ) Let α>0, nN and u,φCn([a,b]) be two functions such that φ(t)>0, for all t[a,b]. The φ-Riemann-Liouville fractional derivative of a function u of order α is defined by

    (Dα;φau)(t)=(1φ(t)ddt)n(Inα;φau)(t)=1Γ(nα)(1φ(t)ddt)nta(φ(t)φ(s)nα1u(s)φ(s)ds

    where n=[α]+1.

    Definition 2.7. (φ-Caputo fractional derivative, [14,15]) Let α>0, nN and u,φCn([a,b]) be two functions such that φ(t)>0 for all t[a,b]. The φ-Caputo fractional derivative of a function u of order α is defined by

    (CDα;φau)(t)=(Inα;φau[n])(t)=1Γ(nα)ta(φ(t)φ(s))nα1u[n](s)φ(s)ds

    where n=[α]+1 and u[n](t):=(1φ(t)ddt)nu(t) on [a,b].

    Lemma 2.8. [14] Let α>0. If uCn([a,b]) then

    Iα;φa(CDα;φau(t))=u(t)n1k=0u[k](a+)k!(φ(t)φ(a))k.

    In particular, given α(0,1), we have

    Iα;φa(CDα;φau(t))=u(t)u(a).

    Definition 2.9. [15] Let u and φ be real valued functions on [a,) such that φ(t) is continuous and φ(t)>0 on [a,). The generalized Laplace transform of u is defined by

    Lφ{u(t)}(s)=aes(φ(t)φ(a))u(t)φ(t)dt

    for all s.

    Definition 2.10. [15] Let u and v be piecewise continuous functions on an interval [a,b] and of exponential order. The generalized convolution of u and v is defined as

    (uφv)(t)=tau(τ)v(φ1(φ(t)+φ(a)φ(τ)))φ(τ)dτ.

    Theorem 2.11. (Gronwall’s inequality, [49,50]) Let φC1([a,b]) be a function such that φ(t)>0 for all t[a,b]. Suppose that

    (i) u and v are nonnegative and integrable functions;

    (ii) w is nonnegative continuous and nondecreasing function on [a,b]

    with

    u(t)v(t)+w(t)ta(φ(t)φ(s))α1u(s)φ(s)ds.

    Then

    u(t)v(t)+tak=1[w(t)Γ(α)]kΓ(nα)(φ(t)φ(s))kα1v(s)φ(s)ds

    for all t[a,b].

    Definition 2.12. [51,52] Let 0<α<1 and zC. The function ϕα defined by

    ϕα(z)=k=0(z)kk!Γ(αk+1α)=1πk=0(z)kΓ(α(k+1))sin(π(k+1)α)k!

    is called Wright type function.

    Proposition 2.13. [51,52] The Wright type function ϕα is an entire function and has the following properties:

    (i) ϕα(θ)0 for θ0 and 0ϕα(θ)dθ=1;

    (ii) 0ϕα(θ)θrdθ=Γ(1+r)Γ(1+αr) \quad for r>1;

    (iii) 0ϕα(θ)ezθdθ=Eα(z),zC;

    (iv) α0θϕα(θ)ezθdθ=Eα,α(z),zC

    where Eα(z)=k=0zkΓ(kα+1) is the Mittag-Leffler function with zC and α>0.

    Now, we recall the definition and some properties of Kuratowski measure of noncompactness.

    Definition 2.14. [53] Let B be a bounded set of Banach space E. The Kuratowski measure of noncompactness μ() is defined by

    μ(B):=inf{ε>0:Bni=1Bj,diam(Bi)<ε}

    where diam(Bi)=sup{|yx|:x,yBi} for i=1,2,nN.

    Lemma 2.15. [54] Let C and D be bounded subsets of a Banach space E. The noncompactness measure which satisfies the following properties:

    (i) D is precompact if and only if μ(D)=0;

    (ii) μ(CD)=max{μ(C),μ(D)} ;

    (iii) μ(C+D)μ(C)+μ(D);

    (iv) μ(λC)=|λ|μ(C) where λR;

    (v) Let X be another Banach space. If S:D(S)EX satisfies Lipschitz continuity with constant L, then

    μ(S(B))Lμ(B)

    for any bounded subset BD(S).

    Lemma 2.16. [54] If BC([t0,T],E) is bounded and equicontinuity, then μ(B(t)) is continuous on [t0,T], and

    μ(B)=supt[t0,T]μ(B(t))

    where B(t)={u(t):uB} for all t[t0,T].

    Lemma 2.17. [55] If BC([t0,T],E) is bounded and equicontinuous, then μ(B(t)) is continuous on [t0,T], and

    μ({Tt0u(t)dt|uB})Tt0μ(B(t))dt.

    Lemma 2.18. [56] If B={un}n=1C([t0,T],E) be a bounded and countable set, then μ(B(t)) is Lebesgue integral on [t0,T], and

    μ({Tt0un(t)dtnN})2Tt0μ(B(t))dt.

    Throughout this work, A is assumed to be the infinitesimal generator of a strongly continuous semigroup (i.e., C0-semigroup) of uniformly bounded linear operators {T(t)}t0 on E with

    M=supt[0,)T(t)for some M1.

    In this section, we derive the mild solution of the Cauchy problem (1.3) based on the semigroup theory and generalized Laplace transform.

    Lemma 3.1. Assume vC([t0,T],E) and 0<α<1. The mild solution of the linear Cauchy problem

    {CDα;φt0u(t)=Au(t)+v(t),t>t0u(t0)=u0E (3.1)

    is given by

    u(t)=Sα;φ(t,t0)u0+tt0(φ(t)φ(s))α1Tα;φ(t,s)v(s)φ(s)ds,t[t0,T] (3.2)

    where the operators Sα;φ(t,s) and Tα;φ(t,s) are defined by

    Sα;φ(t,s)u=0ϕα(θ)T((φ(t)φ(s))αθ)udθ (3.3)

    and

    Tα;φ(t,s)u=α0θϕα(θ)T((φ(t)φ(s))αθ)udθ (3.4)

    for 0stT and uE.

    Proof. The proof follows similar ideas as in [57]. Firstly, we apply the Definition 2.7 and Lemma 2.8 into the Cauchy problem (3.1). It can be rewritten the Cauchy problem (3.1) in form of the integral representation as

    u(t)=u0+1Γ(α)tt0(φ(t)φ(τ))α1(Au(τ)+v(τ))φ(τ)dτ. (3.5)

    Taking the generalized Laplace transforms to both sides of (3.5), we get that for λ>0,

    U(λ)=λα1(λαIA)1u0+(λαIA)1V(λ)=λα10eλαsT(s)u0ds+0eλαsT(s)V(λ)ds=α0(λη)α1e(λη)αT(ηα)u0dη+α0ηα1e(λη)αT(ηα)V(λ)dη=:J1+J2

    where

    U(λ)=t0eλ(φ(τ)φ(t0))u(τ)φ(τ)dτ

    and

    V(λ)=t0eλ(φ(τ)φ(t0))v(τ)φ(τ)dτ.

    Substituting η=φ(t)φ(t0) into J1 and J2 gives

    J1=αt0λα1(φ(t)φ(t0))α1e(λ(φ(t)φ(t0)))αT((φ(t)φ(t0))α)u0φ(t)dt=t01λddt(e(λ(φ(t)φ(t0)))α)T((φ(t)φ(t0))α)u0dt

    and

    J2=t0α(φ(t)φ(t0))α1e(λ(φ(t)φ(t0)))α×T((φ(t)φ(t0))α)V(λ)φ(t)dt=t0t0α(φ(t)φ(t0))α1e(λ(φ(t)φ(t0)))α×T((φ(t)φ(t0))α)e(λ(φ(s)φ(t0)))v(s)φ(s)φ(t)dsdt.

    The following one-sided stable probability density in [2] is considered by

    ρα(θ)=1πk=1(1)k1θαk1Γ(αk+1)k!sin(kπα),θ(0,)

    whose integration is given by

    0eλθρα(θ)dθ=eλαfor0<α<1. (3.6)

    Applying (3.6) to J1 and J2, it follows that

    J1=t00θρα(θ)eλ(φ(t)φ(t0))θT((φ(t)φ(t0))α)u0φ(t)dθdt=t0eλ(φ(t)φ(t0))(0ρα(θ)T((φ(t)φ(t0))αθα)u0dθ)φ(t)dt

    and

    J2=t0t00α(φ(t)φ(t0))α1ρα(θ)eλ(φ(t)φ(t0))θ×T((φ(t)φ(t0))α)eλ(φ(s)φ(t0))v(s)φ(s)φ(t)dθdsdt=t0t00αeλ(φ(t)+φ(s)2φ(t0))(φ(t)φ(t0))α1θαρα(θ)×T((φ(t)φ(t0))αθα)v(s)φ(s)φ(t)dθdsdt=t0t0αeλ(φ(τ)φ(t0))ρα(θ)(φ(t)φ(t0))α1θα×T((φ(t)φ(t0))αθα)v(φ1(φ(τ)φ(t)+φ(t0)))φ(τ)φ(t)dθdτdt=t0τt00αeλ(φ(τ)φ(t0))ρα(θ)(φ(t)φ(t0))α1θα×T((φ(t)φ(t0))αθα)v(φ1(φ(τ)φ(t)+φ(t0)))φ(τ)φ(t)dθdtdτ=t0eλ(φ(τ)φ(t0))×(τt00αρα(θ)(φ(τ)φ(s))α1θαT((φ(τ)φ(s))αθα)v(s)φ(s)dθds)φ(τ)dτ.

    It follows that

    U(λ)=t0eλ(φ(t)φ(t0))(0ρα(θ)T((φ(t)φ(t0))αθα)u0dθ)φ(t)dt+t0eλ(φ(τ)φ(t0))×(τt00αρα(θ)(φ(τ)φ(s))α1θαT((φ(τ)φ(s))αθα)v(s)φ(s)dθds)φ(τ)dτ.

    Hence, we apply the inverse Laplace transform to get

    u(t)=0ρα(θ)T((φ(t)φ(t0))αθα)u0dθ+tt00αρα(θ)(φ(t)φ(s))α1θαT((φ(t)φ(s))αθα)v(s)φ(s)dθds=0ϕα(θ)T((φ(t)φ(t0))αθ)u0dθ+tt0(φ(t)φ(s))α1(0αθϕα(θ)T((φ(t)φ(t0))αθ)dθ)v(s)φ(s)ds:=Sα;φ(t,t0)u0+tt0(φ(t)φ(s))α1Tα;φ(t,s)v(s)φ(s)ds

    where ϕα(θ)=1αθ11αρα(θ1α) is the probability density function defined on (0,).

    Lemma 3.2. [57] The operators Sα;φ and Tα;φ have the following properties:

    (i) For any fixed 0st, Sα;φ(t,s) and Tα;φ(t,s) are bounded linear operators with

    Sα;φ(t,s)(u)MuandTα;φ(t,s)(u)αMΓ(1+α)u=MΓ(α)u

    for all uE.

    (ii) The operators Sα;φ(t,s) and Tα;φ(t,s) are strongly continuous for all 0st, that is, for every uE and 0st1<t2T we have

    Sα;φ(t2,s)uSα;φ(t1,s)u0andTα;φ(t2,s)uTα;φ(t1,s)u0

    as t1t2.

    Definition 3.3. A function uC([t0,T],E) is called a mild solution of (1.3) if it satisfies

    u(t)=Sα;φ(t,t0)u0+tt0(φ(t)φ(s))α1Tα;φ(t,s)f(s,u(s),Gu(s),Hu(s))φ(s)ds

    where the operators Sα;φ and Tα;φ are defined by (3.3) and (3.4), respectively.

    From Definition 2.4, if T(t)(t0) is a positive semigroup generated by A, f and u0 are nonnegaive, then the mild solution uC([t0,T],E) of Cauchy problem (1.3) satisfies uθ.

    Definition 3.4. A function u_C([t0,T],E) is called a lower solution of problem (1.3) and satisfies

    {CDα;φt0u_(t)Au_(t)+f(t,u_(t),Gu_(t),Hu_(t)),t(t0,T]u_(t0)u0. (3.7)

    Analogously, a function ¯uC([t0,T],E) is called a upper solution of problem (1.3) and satisfies

    {CDα;φt0¯u(t)A¯u(t)+f(t,¯u(t),G¯u(t),H¯u(t)),t(t0,T]¯u(t0)u0. (3.8)

    Before stating and proving the main results, we introduce following assumptions:

    (H1) There exists lower and upper solutions u_0,¯u0C([t0,T],E) of Cauchy problem (1.3) respectively, such that u_0¯u0.

    (H2) The nonlinear term f is a function in C([t0,T]×E×E×E,E) and there exists a nonnegative constant C such that

    f(t,u2,v2,w2)f(t,u1,v1,w2)C(u2u1),

    for any t[t0,T], u_0(t)u1u2¯u0(t), Gu_0(t)v1v2G¯u0(t) and Hu_0(t)w1w2H¯u0(t).

    (H3) There exist nonnegative constants L1,L2,L3 such that for any bounded and countable sets B1,B2,B3E

    μ({f(t,B1,B2,B3)})L1μ(B1)+L2μ(B2)+L3μ(B3),

    for t[t0,T].

    (H4) There are nonnegative constants S1,S2,S3 such that

    f(t,u2,v2,w2)f(t,u1,v1,w1)S1(u2u1)+S2(v2v1)+S3(w2w1),

    for any t[t0,T], u_0(t)u1u2¯u0(t), Gu_0(t)v1v2G¯u0(t) and Hu_0(t)w1w2H¯u0(t).

    For convenience, we write G=max(t,s)Ω|g(t,s)|, and H=max(t,s)¯Ω|h(t,s)|.

    Theorem 4.1. Let E be an ordered Banach space, whose positive cone P is normal with normal constant N. Assume that (H1)-(H3) holds with T(t)(t0) is positive and

    R:=2M(φ(T)φ(t0))αΓ(α+1)(L1+2GL2T+2HL3T+C)<1.

    Then, the Cauchy problem (1.3) has the minimal and maximal mild solutions between u_0 and ¯u0 which can be iteratively constructed by monotone sequence starting from u_0 and ¯u0, respectively.

    Proof. Let D=[u_0,¯u0]={vC([t0,T],E)|u_0v¯u0} and we define an operator Q:DC([t0,T],E) by

    Qu(t)=Sα;φ(t,t0)u0+tt0(φ(t)φ(s))α1Tα;φ(t,s)[f(s,u(s),Gu(s),Hu(s))+Cu(s)]φ(s)ds.

    First, we will verify that Q:DD is monotone increasing. For u1,u2D and u1u2, by the positivity of operators Sα;φ(t,s) and Tα;φ(t,s) for t0stT, and (H2), we have

    Qu1(t)=Sα;φ(t,t0)u0+tt0(φ(t)φ(s))α1×Tα;φ(t,s)[f(s,u1(s),Gu1(s),Hu1(s))+Cu1(s)]φ(s)dsSα;φ(t,t0)u0+tt0(φ(t)φ(s))α1×Tα;φ(t,s)[f(s,u2(s),Gu2(s),Hu2(s))+Cu2(s)]φ(s)ds=Qu2(t)

    which implies Qu1Qu2. Let ρ(t)=CDα;φt0u_0(t)Au_0(t)+Cu_0(t). By Definition 3.4, we obtain ρ(t)f(t,u_0(t),Gu_0(t),Hu_0(t))+Cu_0(t), for t[t0,T]. From Lemma 3.1, and the positivity of operators Sα;φ(t,s) and Tα;φ(t,s) for t0stT, we have

    u_0(t)=Sα;φ(t,t0)u0+tt0(φ(t)φ(s))α1Tα;φ(t,s)ρ(s)φ(s)dsSα;φ(t,t0)u0+tt0(φ(t)φ(s))α1×Tα;φ(t,s)[f(s,u_0(s),Gu_0(s),Hu_0(s))+Cu_0(s)]φ(s)ds=Qu_0(t)for t[t0,T],

    and hence u_0Qu_0. Similarly, we can show that Q¯u0¯u0. This implies that for uD

    u_0Qu_0QuQ¯u0¯u0.

    Hence, Q is an increasing monotonic operator.

    Now, we define two sequences {u_n} and {¯un} in D by the iterative scheme

    u_n=Qu_n1and¯un=Q¯un1,nN. (4.1)

    Then, by the monotonicity of Q, it follows that

    u_0u_1u_n¯un¯u1¯u0. (4.2)

    Next, we claim that {u_n} and {¯un} are uniformly convergent in [t0,T]. Let B={u_n|nN} and B0={u_n1|nN}. Then B0=B{u_0} and hence μ(B(t))=μ(B0(t)) for t[t0,T].

    In view of (4.2), since the positive cone P is normal, then B0 and B are bounded in C([t0,T],E).

    Now, we prove that Q(B) is equicontinuous. For any uD, by (H2), we have

    f(t,u_0,Gu_0,Hu_0)+Cu_0f(t,u,Gu,Hu)+Cuf(t,¯u0,G¯u0,H¯u0)+C¯u0.

    By the normality of the positive cone P, there exists a constant K>0 such that

    f(t,u,Gu,Hu)+CuKfor uE.

    For any u_nB and t0t1<t2T, we have

    (Qu_n)(t2)(Qu_n)(t1)Sα;φ(t2,t0)u0Sα;φ(t1,t0)u0+||t2t0(φ(t2)φ(s))α1Tα;φ(t2,s)[f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s)]φ(s)dst1t0(φ(t1)φ(s))α1Tα;φ(t1,s)[f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s)]φ(s)ds||=Sα;φ(t2,t0)u0Sα;φ(t1,t0)u0+||t1t0(φ(t2)φ(s))α1Tα;φ(t2,s)[f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s)]φ(s)ds+t2t1(φ(t2)φ(s))α1Tα;φ(t2,s)[f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s)]φ(s)ds+t1t0(φ(t1)φ(s))α1Tα;φ(t2,s)[f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s)]φ(s)dst1t0(φ(t1)φ(s))α1Tα;φ(t2,s)[f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s)]φ(s)dst1t0(φ(t1)φ(s))α1Tα;φ(t1,s)[f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s)]φ(s)ds||Sα;φ(t2,t0)u0Sα;φ(t1,t0)u0+||t2t1(φ(t2)φ(s))α1Tα;φ(t2,s)(f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s))φ(s)ds||+||t1t0[(φ(t2)φ(s))α1(φ(t1)φ(s))α1]×Tα;φ(t2,s)[f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s)]φ(s)ds||+||t1t0(φ(t1)φ(s))α1×[Tα;φ(t2,s)Tα;φ(t1,s)](f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s))φ(s)ds||=:J1+J2+J3+J4.

    By Lemma 3.2, it is clear that J10 as t1t2 and we obtain

    J2MKΓ(α+1)(φ(t2)φ(t1))α

    and

    J3MKΓ(α+1)[(φ(t2)φ(t0))α(φ(t1)φ(t0))α(φ(t2)φ(t1))α]

    and hence J20 and J30 as t2t1. For t1=0 and 0<t2T, it is easy to see that J4=0. Then, for any ε(0,t1), we have

    J4||t1εt0(φ(t1)φ(s))α1[Tα;φ(t2,s)Tα;φ(t1,s)](f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s))φ(s)ds||+||t1t1ε(φ(t1)φ(s))α1[Tα;φ(t2,s)Tα;φ(t1,s)](f(s,u_n(s),Gu_n(s),Hu_n(s))+Cu_n(s))φ(s)ds||Kα[(φ(t1)φ(t0))α(φ(t1)φ(t1ε))α]supt0s<t1εTα;φ(t2,s)Tα;φ(t1,s)+2MKΓ(α+1)[(φ(t1)φ(t1ε))α]

    By Lemma 3.2, it follows that J40 as t2t1 and ε0. Thus, we obtain

    (Qu)(t2)(Qu)(t1)0independentlyofuDast2t1.

    which means that Q(B) is equicontinuous.

    For t[t0,T], by Lemma 2.18 we have

    μ(GB0(t))=μ({tt0g(t,s)un1(s)ds}n=1)2GTsupt[t0,T]μ{B0(t)}

    and

    μ(HB0(t))=μ({Tt0h(t,s)un1(s)ds}n=1)2HTsupt[t0,T]μ{B0(t)}.

    Since the sequence {u_n1(t0)}n=1 is convergent, we obtain μ({u_n1(t0)}n=1)=0. For any t[t0,T], by (H3) and Lemma 2.17 and Lemma 2.18 we have

    μ(B(t))=μ(B0(t))=μ({Sα;φ(t,t0)u0+tt0(φ(t)φ(s))α1Tα;φ(t,s)[f(s,un1(s),Gun1(s),Hun1(s))+Cun1(s)]φ(s)ds}n=1)μ({Sα;φ(t,t0)u0})+μ({tt0(φ(t)φ(s))α1Tα;φ(t,s)[f(s,un1(s),Gun1(s),Hun1(s))+Cun1(s)]φ(s)ds}n=1)2tt0μ({(φ(t)φ(s))α1×Tα;φ(t,s)[f(s,un1(s),Gun1(s),Hun1(s))+Cun1(s)]φ(s)}n=1)ds2MΓ(α)tt0(φ(t)φ(s))α1×μ({[f(s,un1(s),Gun1(s),Hun1(s))+Cun1(s)]}n=1)φ(s)ds2MΓ(α)tt0(φ(t)φ(s))α1((L1μ({un1(s)}n=1)+L2μ({Gun1(s)}n=1))+L3μ({Hun1(s)}n=1))+μ({Cun1(s)}n=1))φ(s)ds2MΓ(α)(L1+2GL2T+2HL3T+C)supt[t0,T]μ(B0(t))tt0(φ(t)φ(s))α1φ(s)ds2M(φ(T)φ(t0))αΓ(α+1)(L1+2GL2T+2HL3T+C)supt[t0,T]μ(B0(t))=:Rsupt[t0,T]μ(B0(t)).

    Since {Qu_n}n=0 is equicontinuous on [t0,T] and by Lemma 2.16, we get

    μ(B)Rμ(B).

    Since R<1, we obtain μ(B)=0. Hence the set B is relatively compact in E and so there is a convergent subsequence of {u_n} in E. Combining this with the monotonicity (4.2), we can prove that {u_n} itself is convergent, i.e., limnu_n(t)=u_(t),t[t0,T]. Similarly, we can prove that limn¯un(t)=¯u(t),t[t0,T].

    For any t[t0,T], we see that

    u_n(t)=Qu_n1(t)=Sα;φ(t,t0)u0+tt0(φ(t)φ(s))α1×Tα;φ(t,s)[f(s,u_n1(s),Gu_n1(s),Hu_n1(s))+Cu_n1(s)]φ(s)ds.

    Taking n in the above equality, by the Lebesgue dominated convergence theorem, we obtain

    u_=Qu_andu_C([t0,T],E).

    Similarly, we can prove that there exists ¯uC([t0,T],E) such that ¯u=Q¯u.

    Combining this fact with monotonicity (4.2) we notice that

    u_0u_u¯u¯u0.

    Now, we will claim that u_ and ¯u are the minimal and maximal fixed points of Q on [u_0,¯u0], respectively. For any uD and u is a fixed point of Q, we have

    u_1=Qu_0Qu=uQ¯u0=¯u1.

    By induction, we obtain u_nu¯un for all nN. From (4.2) and taking the limit as n, we conclude that

    u_u¯u.

    Thus, u_ and ¯u are minimal and maximal mild solutions of the Cauchy problem (1.3) on [u_0,¯u0], respectively, and u_, ¯u can be obtained by the iterative scheme (4.1) starting from u_0 and ¯u0, respectively.

    Corollary 4.2. Let E be an ordered Banach space, whose positive cone P is regular and (H1)-(H3) hold with T(t)(t0) is positive. Then, the Cauchy problem (1.3) has the minimal and maximal mild solutions between u_0 and ¯u0 which can be iteratively constructed by monotone sequence starting from u_0 and ¯u0, respectively.

    Proof. As (H2) and (H3) are satisfied, we have that the sequences {u_n} and {¯un} defined by (4.1) satisfies (4.2). Since the positive cone P is regular, we obtain that any monotonic and ordered-bounded sequence is convergent, and hence there are u_,¯uC([t0,T],E) such that

    limnu_n=u_andlimn¯un=¯u.

    It follows from the proof of Theorem 4.1 that the statement of this theorem is satisfied.

    Corollary 4.3. Suppose E is a partially ordered and weakly sequentially complete Banach space with normal positive cone P. Assume that (H1)-(H2) hold with T(t)(t0) is positive. Then, the Cauchy problem (1.3) has extremal mild solutions in [u_0,¯u0].

    Proof. Since E is an ordered and weakly sequentially complete Banach space, the cone P is regular by Theorem 2.3. By the proof of Theorem 4.1, we have that the sequences {u_n} and {¯un} defined by (4.1) satisfies (4.2).

    Let {un} be an increasing or a decreasing sequence with {un}[u_0(t),¯u0(t)]. Then by the condition (H2), the sequence {f(t,un,Gun,Hun)+Cun} is a monotonic and order-bounded sequence, so μ{f(t,un,Gun,Hun)+Cun}=0. Thus, by the properties of the measure of noncompactness, we obtain

    μ{f(t,un,Gun,Hun)}μ{f(t,un,Gun,Hun)+Cun}+μ{Cun}=0.

    Hence, the condition (H3) holds.

    By the proof of Theorem 4.1, we obtain that the sequences are uniformly convergent. Let u_(t)=limnu_n(t) and ¯u(t)=limn¯un(t), for t[t0,T]. By Lebesgue dominated convergence theorem, we obtain

    u_=Qu_and¯u=Q¯u

    with u_,¯uC([t0,T],E). Hence u_ and ¯u are a mild solutions for (1.3). If uD and u=Qu, then

    u_1=Qu_0u=QuQ¯u0=¯u1.

    From the process of induction, u_nu¯un and u_0u_u¯u¯u0 as n. This means u_ is the minimal and ¯u is the maximal mild solution for (1.3).

    Next, we will prove the uniqueness of solution of the Cauchy problem (1.3) by using monotone technique. To this end, we replace (H3) by (H4) .

    Theorem 4.4. Let E be an ordered Banach space, whose positive cone P is normal with normal constant N. Assume that T(t)(t0) is positive and the assumption (H1)-(H2) and (H4) hold. If

    ˜R:=2M(φ(T)φ(t0))αΓ(α+1)(NS1+NC+2GNS2T+2HNS3T+2C)<1,

    then the Cauchy problem (1.3) has the unique mild solution between between u_0 and ¯u0 which can be iteratively constructed by monotone sequence starting from u_0 and ¯u0, respectively.

    Proof. For t[t0,T], let {un}[u_0,¯u0], {vn}[Gu_0,G¯u0] and {wn}[Hu_0,H¯u0] be an increasing sequence. For m,n=1,2, with m>n, by (H2) and (H4), we have

    θf(t,um,vm,wm)f(t,un,vn,Hwn)+C(umun)(S1+C)(umun)+S2(vmvn)+S3(wmwn).

    By the normality of cone P it follows that

    f(t,um,vm,wm)f(t,un,vn,wn)(NS1+NC+C)umun+NS2vmvn+NS3wmwn.

    From the definition of the measure of noncompactness, we have

    μ(f(t,un,Gun,Hun)(NS1+NC+C)μ(un)+NS2μ(vn)+NS3μ(wn):=L1μ(un)+L2μ(vn)+L3μ(wn)

    where L1=NS1+NC+C, L2=NS2 and L3=NS3. Hence, (H3) holds. Therefore, by Theorem 4.1, the Cauchy problem (1.3) has the minimal mild solution u_ and the maximal mild solution ¯u on D=[u_0,¯u0]. In view of the proof of Theorem 4.1, we show that u_=¯u. For t[t0,T], by the positivity of operator Tα;φ, we have

    θ¯uu_=Q¯uQu_=tt0(φ(t)φ(s))α1Tα;φ(t,s)[f(s,¯u(s),G¯u(s),H¯u(s))f(s,u_(s),Gu_(s),Hu_(s))+C(¯u(s)u_(s))]φ(s)dstt0(φ(t)φ(s))α1Tα;φ(t,s)[S1(¯u(s)u_(s))+S2(G¯u(s)Gu_(s))+S3(H¯u(s)Hu_(s))+C(¯u(s)u_(s))]φ(s)ds.

    Since the positive cone P is normal, we obtain

    ¯uu_N||tt0(φ(t)φ(s))α1Tα;φ(t,s)[S1(¯u(s)u_(s))+S2(G¯u(s)Gu_(s))+S3(H¯u(s)Hu_(s))+C(¯u(s)u_(s))]φ(s)ds||NM(S1+S2G+S3H)tt0(φ(t)φ(s))α1¯u(s)u_(s)φ(s)ds.

    By Theorem 2.11, we get u_=¯u on [t0,T]. Hence, u_=¯u is the the unique mild solution of the Cauchy problem (1.3) on D. By the proof of Theorem 4.1, the solution can be obtained by a monotone iterative procedure starting from u_0 or ¯u0.

    Similar to Corollary 4.2 and Corollary 4.3, we obtain the following result.

    Corollary 4.5. Assume that T(t)(t0) is positive and the assumption (H1)-(H2) and (H4) hold. One of the following conditions is satisfied:

    (i) E is an ordered Banach space, whose positive cone P is regular;

    (ii) E is an ordered and weakly sequentially complete Banach space, whose positive cone P is normal with normal constant N,

    then the Cauchy problem (1.3) has the unique mild solution between u_0 or ¯u0, which can be obtained which can be iteratively constructed by monotone sequence starting from u_0 and ¯u0, respectively.

    We consider the following initial-boundary value problem of time-fractional parabolic partial differential equation with nonlinear source term:

    {CDα;φ0u(x,t)Δu(x,t)=sin(πt)2(1+et)u(x,t)+cos2(t)(φ(t)φ(0))α3Γ(1α)+150t0(ts)u(x,s)ds+e4t3410e|ts|u(x,s)ds,x[0,π],t(0,1],u(0,t)=u(π,t)=0t[0,1],u(x,0)=u0(x),x(0,π), (5.1)

    where α(0,1) and u00.

    Let E=L2([0,π]) and P={yEyθ}. Then P is normal cone in Banach space E with normal constant N=1. Define the operator A:D(A)EE as follows:

    Au=Δu

    with the domain

    D(A)={vEv,vx are absolutely continuous,2vx2E,v(0)=v(π)=0}.

    It is well known that A generates an analytic semigroup of uniformly bounded analytic semigroup {T(t)}t0 in E with T(t) is positive and T(t)1 for t0.

    Further, for any t[0,1], we define

    u(t)=u(,t),g(t,s)=tsfor 0s,t1,
    h(t,s)=e|ts|for 0st1,
    Gu(t)=t0g(t,s)u(,s)ds,Hu(t)=10h(t,s)u(,s)ds,

    and

    f(t,u(t),Gu(t),Hu(t))=sin(πt)2(1+et)u(t)+cos2(t)(φ(t)φ(0))α3Γ(1α)+150Gu(t)+e4t34Hu(t)

    Then the problem (5.1) can be reformulated as the Cauchy problem (1.3) in E.

    Let u_0(x,t)=0 for (x,t)[0,π]×[0,1]. Then

    f(t,u_(x,t),Gu_(x,t),Hu_(x,t))0,for (x,t)[0,π]×[0,1].

    Let ¯u0=v be the positive solution of the following problem:

    {CDα;φ0v(x,t)Δv(x,t)=12(1+et)v(x,t)+(φ(t)φ(0))α3Γ(1α)+150t0(ts)v(x,s)ds+13410e|ts|v(x,s)ds,x[0,π],t(0,1],v(0,t)=v(π,t)=0t[0,1],v(x,0)=u0(x)x(0,π)

    which can be obtained by modifying the proof of Theorem 5.1 in [57]. It is clearly seen that u_ and ¯u are lower and upper solutions, respectively and u_0¯u0.

    Suppose that {un}[u_0,¯u0] is a monotone increasing sequence. Then, we have that for each nm

    0f(t,um,Gum,Hum)f(t,un,Gun,Hun)14(umun)+150(GumGun)+134(HumHun).

    By normality of P, we have

    f(t,um,Gum,Hum)f(t,un,Gun,Hun)14umun+150GumGun+134HumHun

    and hence by Lemma 2.15

    μ(f(t,un,Gun,Hun))14μ(un)+150μ(Gun)+134μ(Hun).

    This implies that the conditions (H2) - (H3) are satisfies with L1=14,L2=150 and L3=134.

    For example α=37 and φ(t)=2t. Then, upon computation, we get

    R:=2MΓ(α+1)(L1+2GL2T+2HL3T+C)(φ(T)φ(0))α0.7873<1.

    where G=H=T=1 and C=0. Therefore, by Theorem 4.1, we obtain that the minimal and maximal mild solutions for the Cauchy problem (5.1) are between the lower solution u_0 and upper solution ¯u0.

    Moreover, the condition (H4) is satisfied with S1=14, S2=150 and S3=134. Then, the Cauchy problem (5.1) has a unique mild solution between the lower and upper solutions by Theorem 4.4.

    This paper investigates the existence and uniqueness results of mild solutions for φCaputo fractional integro-differential evolution equations. The method is inspired by using the monotone iterative technique involving lower and upper solutions, some existence and uniqueness result of mild solutions for φCaputo fractional integro-differential evolution equations has been proved. Here, the compactness condition of C0-semigroup {T(t)}t0 does not require.

    The authors would like to thank referees for valuable comments and suggestions to improve the manuscript.

    The authors declare no conflict of interest in this paper.



    [1] L. A. Zadeh, Fuzzy sets, Inform. Control., 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
    [2] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning, Inform. Sci., 8 (1975), 199–249. https://doi.org/10.1016/0020-0255(75)90036-5 doi: 10.1016/0020-0255(75)90036-5
    [3] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Set. Syst., 20 (1986), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
    [4] K. T. Atanassov, Intuitionistic fuzzy sets: Theory and applications, Springer-Verlag Berlin Heidelberg GmbH, 283 (2012), 1–322. https://doi.org/10.1007/978-3-7908-1870-3 doi: 10.1007/978-3-7908-1870-3
    [5] R. R. Yager, Pythagorean fuzzy subsets, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013, 57–61. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
    [6] R. R. Yager, Pythagorean membership grades in multicriteria decision making, IEEE Trans. Fuzzy Syst., 22 (2014), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989 doi: 10.1109/TFUZZ.2013.2278989
    [7] R. R. Yager, Generalized orthopair fuzzy sets, IEEE Trans. Fuzzy Syst., 25 (2017), 1220–1230. https://doi.org/10.1109/TFUZZ.2016.2604005 doi: 10.1109/TFUZZ.2016.2604005
    [8] W. R. Zhang, Bipolar fuzzy sets and relations: A computational framework for cognitive modeling and multiagent decision analysis, NAFIPS/IFIS/NASA94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference, The Industrial Fuzzy Control and Intellige., (1994), 305–309. https://doi.org/10.1109/IJCF.1994.375115
    [9] W. R. Zhang, (Yin)(Yang) bipolar fuzzy sets, IEEE International Conference on Fuzzy Systems Proceedings, IEEE World Congress Comput. Intell., 1 (1998), 835–840. https://doi.org/10.1109/FUZZY.1998.687599 doi: 10.1109/FUZZY.1998.687599
    [10] J. Chen, S. Li, S. Ma, X. Wang, m-Polar fuzzy sets: An extension of bipolar fuzzy sets, Sci. World J., 2014 (2014), 1–8. https://doi.org/10.1155/2014/416530 doi: 10.1155/2014/416530
    [11] F. Smarandache, A unifying field in logics, neutrosophy: Neutrosophic probability, set and logic, Amer. Res. Press: Rehoboth, DE, USA., (1999). 1–141.
    [12] F. Smarandache, Neutrosophic set-a generalization of the intuitionistic fuzzy set, Int. J. Pure Appl. Math., 24 (2005), 287–297. https://doi.org/10.1089/blr.2005.24.297 doi: 10.1089/blr.2005.24.297
    [13] B. C. Cuong, Picture fuzzy sets, J. Comput. Sci. Cybern., 30 (2014), 409–420. https://doi.org/10.15625/1813-9663/30/4/5032
    [14] Z. S. Xu, Intuitionistic fuzzy aggregation operators, IEEE Trans. Fuzzy Syst., 15 (2007), 1179–1187. https://doi.org/10.1109/TFUZZ.2006.890678 doi: 10.1109/TFUZZ.2006.890678
    [15] H. Garg, Nancy, Linguistic single-valued neutrosophic prioritized aggregation operators and their applications to multiple-attribute group decision-making, J. Ambient. Intell. Human. Comput., 9 (2018), 1975–1997. https://doi.org/10.1007/s12652-018-0723-5 doi: 10.1007/s12652-018-0723-5
    [16] D. Molodtsov, Soft set theory-first results, Comput. Math. Appl., 37 (1999), 19–31. https://doi.org/10.1016/S0898-1221(99)00056-5 doi: 10.1016/S0898-1221(99)00056-5
    [17] N. agman, S. Enginoglu, Soft set theory and uniint decision making, Eur. J. Oper. Res., 207 (2010), 848–855. https://doi.org/10.1016/j.ejor.2010.05.004 doi: 10.1016/j.ejor.2010.05.004
    [18] Y. B. Jun, C. S. Kim, K. O. Yang, Cubic Sets, Annal. Fuzzy Math. Inform., 4 (2012), 83–98.
    [19] M. Riaz, M. R. Hashmi, MAGDM for agribusiness in the environment of various cubic m-polar fuzzy averaging aggregation operators, J. Intell. Fuzzy Syst., 37 (2019), 3671–3691. https://doi.org/10.3233/JIFS-182809 doi: 10.3233/JIFS-182809
    [20] M. Riaz, M. R. Hashmi, Linear Diophantine fuzzy set and its applications towards multi-attribute decision making problems, J. Intell. Fuzzy Syst., 37 (2019), 5417–5439. https://doi.org/10.3233/JIFS-190550 doi: 10.3233/JIFS-190550
    [21] M. Riaz, M. R. Hashmi. H. Kalsoom, D. Pamucar, Y. M. Chu, Linear Diophantine fuzzy soft rough sets for the selection of sustainable material handling equipment, Symmetry., 12 (2020), 1–39. https://doi.org/10.3390/sym12081215 doi: 10.3390/sym12081215
    [22] M. Riaz, M. R. Hashmi, D. Pamucar, Y. M. Chu, Spherical linear Diophantine fuzzy sets with modeling uncertainties in MCDM, Comput. Model. Eng. Sci., 126 (2021), 1125–1164. https://doi.org/10.32604/cmes.2021.013699 doi: 10.32604/cmes.2021.013699
    [23] P. Liu, Z. Ali, T. Mahmood, N. Hassan, Group decision-making using complex q-rung orthopair fuzzy Bonferroni mean, Int. J. Comput. Intell. Syst., 13 (2020), 822–851. https://doi.org/10.2991/ijcis.d.200514.001 doi: 10.2991/ijcis.d.200514.001
    [24] P. Liu, P. Wang, Multiple attribute group decision making method based on intuitionistic fuzzy Einstein interactive operations, Int. J. Fuzzy Syst., 22 (2020), 790–809. https://doi.org/10.1007/s40815-020-00809-w doi: 10.1007/s40815-020-00809-w
    [25] A. Jain, J. Darbari, A. Kaul, P. C. Jha, Selection of a green marketing strategy using MCDM under fuzzy environment, In: Soft Computing for Problem Solving, (2020), https://doi.org/10.1007/978-981-15-0184-5_43
    [26] C. L. Chang, Fuzzy topological spaces, J. Math. Anal. Appl., 24 (1968), 182–190. https://doi.org/10.1016/0022-247X(68)90057-7
    [27] D. Coker, An introduction to intuitionistic fuzzy topological spaces, Fuzzy Sets Syst., 88 (1997), 81–89. https://doi.org/10.1016/S0165-0114(96)00076-0 doi: 10.1016/S0165-0114(96)00076-0
    [28] M. Olgun, M. Unver, Yardimci, Pythagorean fuzzy topological spaces, Complex Intell. Syst., 5 (2019), 177–183. https://doi.org/10.1007/s40747-019-0095-2 doi: 10.1007/s40747-019-0095-2
    [29] N. Cagman, S. Karatas, S. Enginoglu, Soft topology, Comput. Math. Applic., 62 (2011), 351–358. https://doi.org/10.1016/j.camwa.2011.05.016
    [30] A. Saha, T. Senapati, R. R. Yager, Hybridizations of generalized Dombi operators and Bonferroni mean operators under dual probabilistic linguistic environment for group decision-making, Int. J. Intell. Syst., 11 (2021), 6645–6679. https://doi.org/10.1002/int.22563 doi: 10.1002/int.22563
    [31] A. Saha, H. Garg, D. Dutta, Probabilistic linguistic q-rung orthopair fuzzy generalized Dombi and Bonferroni mean operators for group decision-making with unknown weights of experts, Int. J. Intell. Syst., 12 (2021), 7770–7804. https://doi.org/10.1002/int.22607 doi: 10.1002/int.22607
    [32] C. Jana, G. Muhiuddin, M. Pal, D. Al-Kadi, Intuitionistic fuzzy Dombi hybrid decision-making method and their applications to enterprise financial performance evaluation, Math. Prob. Eng., 2021 (2021), 1–14. https://doi.org/10.1155/2021/3218133 doi: 10.1155/2021/3218133
    [33] C. Jana, M. Pal, J. Wang, Bipolar fuzzy Dombi prioritized aggregation operators in multiple attribute decision making, Soft Comput., 24 (2020), 3631–3646. https://doi.org/10.1007/s00500-019-04130-z. doi: 10.1007/s00500-019-04130-z
    [34] M. Akram, G. Ali, J. C. R. Alcantud, Attributes reduction algorithms for m-polar fuzzy relation decision systems, Int. J. Approx. Reas., 140 (2022), 232–254. https://doi.org/10.1016/j.ijar.2021.10.005 doi: 10.1016/j.ijar.2021.10.005
    [35] M. Akram, A. Luqman, J. C. R. Alcantud, Risk evaluation in failure modes and effects analysis: Hybrid TOPSIS and ELECTRE I solutions with Pythagorean fuzzy information, Neural Comput. Applic., 33 (2021), 5675–5703. https://doi.org/10.1007/s00521-020-05350-3 doi: 10.1007/s00521-020-05350-3
    [36] S. Ashraf, S. Abdullah, Decision aid modeling based on sine trigonometric spherical fuzzy aggregation information, Soft Comput., 25 (2021), 8549–8572. https://doi.org/10.1007/s00500-021-05712-6 doi: 10.1007/s00500-021-05712-6
    [37] A. O. Almagrabi, S. Abdullah, M. Shams, Y. D. Al-Otaibi, S. Ashraf, A new approach to q-linear Diophantine fuzzy emergency decision support system for COVID19, J. Ambient. Intell. Human. Comput., 13 (2021), 1687–1713. https://doi.org/10.1007/s12652-021-03130-y doi: 10.1007/s12652-021-03130-y
    [38] M. Ali, I. Deli, F. Smarandache, The theory of neutrosophic cubic sets and their applications in pattern recognition, J. Intell. Fuzzy Syst., 30 (2016), 1957–1963. https://doi.org/10.3233/IFS-151906 doi: 10.3233/IFS-151906
    [39] M. Ali, L. H. Son, I. Deli, N. D. Tien, Bipolar neutrosophic soft sets and applications in decision making, J. Intell. Fuzzy Syst., 33 (2017), 4077–4087. https://doi.org/10.3233/JIFS-17999 doi: 10.3233/JIFS-17999
    [40] J. Zhao, X. Y. You, H. C. Liu, S. M. Wu, An extended VIKOR method using intuitionistic fuzzy sets and combination weights for supplier selection, Symmetry, 9 (2017), 1–16. https://doi.org/10.3390/sym9090169 doi: 10.3390/sym9090169
    [41] R. Joshi, S. Kumar, An intuitionistic fuzzy information measure of order-(α,β) with a new approach in supplier selection problems using an extended VIKOR method, J. Appl. Math. Comput., 60 (2019), 27–50. https://doi.org/10.1007/s12190-018-1202-z doi: 10.1007/s12190-018-1202-z
    [42] J. H. Park, H. J. Cho, J. S. Hwang, Y. C. Kwun, Extension of the VIKOR method to dynamic intuitionistic fuzzy multiple attribute decision making, Third International Workshop on Advanced Computational Intelligence, (2010), 189–195. https://doi.org/10.1109/IWACI.2010.5585223
    [43] Z. Shouzhen, C. S. Ming, K. L. Wei, Multiattribute decision making based on novel score function of intuitionistic fuzzy values and modified VIKOR method, Inform. Sci., 488 (2019), 76–92. https://doi.org/10.1016/j.ins.2019.03.018 doi: 10.1016/j.ins.2019.03.018
    [44] V. Arya, S. Kumar, A novel VIKOR-TODIM Approach based on Havrda-Charvat-Tsallis entropy of intuitionistic fuzzy sets to evaluate management information system, Fuzzy Inform. Eng., 11 (2019), 357–384. https://doi.org/10.1080/16168658.2020.1840317 doi: 10.1080/16168658.2020.1840317
    [45] K. Devi, Extension of VIKOR method in intuitionistic fuzzy environment for robot selection, Expert Syst. Applic., 38 (2011), 14163–14168. https://doi.org/10.1016/j.eswa.2011.04.227 doi: 10.1016/j.eswa.2011.04.227
    [46] X. Luo, X. Wang, Extended VIKOR method for intuitionistic fuzzy multiattribute decision-making based on a new distance measure, Math. Prob. Eng., 2017 (2017), 1–16. https://doi.org/10.1155/2017/4072486 doi: 10.1155/2017/4072486
    [47] T. Y. Chen, Remoteness index-based Pythagorean fuzzy VIKOR methods with a generalized distance measure for multiple criteria decision analysis, Inf. Fus., 41 (2018), 129–150. https://doi.org/10.1016/j.inffus.2017.09.003 doi: 10.1016/j.inffus.2017.09.003
    [48] F. Zhou, T. Y. Chen, An extended Pythagorean fuzzy VIKOR method with risk preference and a novel generalized distance measure for multicriteria decision-making problems, Neural Comput. Applic., 33 (2021), 11821–11844. https://doi.org/10.1007/s00521-021-05829-7 doi: 10.1007/s00521-021-05829-7
    [49] G. Bakioglu, A. O. Atahan, AHP integrated TOPSIS and VIKOR methods with Pythagorean fuzzy sets to prioritize risks in self-driving vehicles, Appl. Soft Comput., 99 (2021), 1–19. https://doi.org/10.1016/j.asoc.2020.106948 doi: 10.1016/j.asoc.2020.106948
    [50] A. Guleria, R. K. Bajaj, A robust decision making approach for hydrogen power plant site selection utilizing (R, S)-Norm Pythagorean Fuzzy information measures based on VIKOR and TOPSIS method, Int. J. Hydr. Energy., 45 (2020), 18802–18816. https://doi.org/10.1016/j.ijhydene.2020.05.091 doi: 10.1016/j.ijhydene.2020.05.091
    [51] M. Gul, Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and colouring unit, Int. J. Occup. Safety Ergon., 26 (2020), 705–718. https://doi.org/10.1080/10803548.2018.1492251 doi: 10.1080/10803548.2018.1492251
    [52] M. Kirisci, I. Demir, N. Simsek, N. Topa, M. Bardak, The novel VIKOR methods for generalized Pythagorean fuzzy soft sets and its application to children of early childhood in COVID-19 quarantine, Neural Comput. Applic., 34 (2021), 1877–1903. https://doi.org/10.1007/s00521-021-06427-3 doi: 10.1007/s00521-021-06427-3
    [53] S. Dalapati, S. Pramanik, A revisit to NC-VIKOR based MAGDM strategy in neutrosophic cubic set environment, Neutrosophic Sets Sy., 21 (2018), 131–141. https://doi.org/10.20944/preprints201803.0230.v1 doi: 10.20944/preprints201803.0230.v1
    [54] S. Pramanik, S. Dalapati, S. Alam, T. K. Roy, NC-VIKOR based MAGDM strategy under neutrosophic cubic set environment, Neutrosophic Sets Sy., 20 (2018), 95–108. https://doi.org/10.20944/preprints201803.0230.v1 doi: 10.20944/preprints201803.0230.v1
    [55] S. Pramanik, S. Dalapati, S. Alam, T. K. Roy, VIKOR based MAGDM strategy under bipolar neutrosophic set environment, Neutrosophic Sets Sy., 19 (2018), 57–69. https://doi.org/10.20944/preprints201801.0006.v1 doi: 10.20944/preprints201801.0006.v1
    [56] L. Wang, H. Y. Zhang, J. Q. Wang, L. Li, Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project, Appl. Soft Comput., 64 (2018), 216–226. https://doi.org/10.1016/j.asoc.2017.12.014 doi: 10.1016/j.asoc.2017.12.014
    [57] V. Arya, S. Kumar, A picture fuzzy multiple criteria decision-making approach based on the combined TODIM-VIKOR and entropy weighted method, Cogn. Comput., 13 (2021), 1172–1184. https://doi.org/10.1007/s12559-021-09892-z doi: 10.1007/s12559-021-09892-z
    [58] R. Joshi, A novel decision-making method using R-Norm concept and VIKOR approach under picture fuzzy environment, Expert Syst. Applic., 147 (2020), 1–12. https://doi.org/10.1016/j.eswa.2020.113228 doi: 10.1016/j.eswa.2020.113228
    [59] V. Arya, S. Kumar, A new picture fuzzy information measure based on shannon entropy with applications in opinion polls using extended VIKOR-TODIM approach, Comp. Appl. Math., 39 (2020), 1–24. https://doi.org/10.1007/s40314-020-01228-1 doi: 10.1007/s40314-020-01228-1
    [60] M. J. Khan, P. Kumam, W. Kumam, A. N. A. Kenani, Picture fuzzy soft robust VIKOR method and its applications in decision-making, Fuzzy Inf. Eng., 13 (2021), 296–322. https://doi.org/10.1080/16168658.2021.1939632. doi: 10.1080/16168658.2021.1939632
    [61] C. Yue, Picture fuzzy normalized projection and extended VIKOR approach to software reliability assessment, Appl. Soft Comput., 88 (2020), 1–13. https://doi.org/10.1016/j.asoc.2019.106056. doi: 10.1016/j.asoc.2019.106056
    [62] P. Meksavang, H. Shi, S. M. Lin, H. C. Liu, An extended picture fuzzy VIKOR approach for sustainable supplier management and its application in the beef industry, Symmetry., 11 (2019), 1–19. https://doi.org/10.3390/sym11040468. doi: 10.3390/sym11040468
    [63] A. Singh, S. Kumar, Picture fuzzy Choquet integral-based VIKOR for multicriteria group decision-making problems, Gran. Comput., 6 (2021), 587–601. https://doi.org/10.1007/s41066-020-00218-2. doi: 10.1007/s41066-020-00218-2
  • This article has been cited by:

    1. Danuruj Songsanga, Parinya Sa Ngiamsunthorn, Single-step and multi-step methods for Caputo fractional-order differential equations with arbitrary kernels, 2022, 7, 2473-6988, 15002, 10.3934/math.2022822
    2. Lahcene Rabhi, Mohammed Al Horani, Roshdi Khalil, Existence results of mild solutions for nonlocal fractional delay integro-differential evolution equations via Caputo conformable fractional derivative, 2022, 7, 2473-6988, 11614, 10.3934/math.2022647
    3. Kassimu MPUNGU, Aminu MA'ARUF NASS, On Complete Group Classification of Time Fractional Systems Evolution Differential Equation with a Constant Delay, 2022, 2645-8845, 10.33401/fujma.1147657
    4. Yonghong Ding, Yongxiang Li, Finite-approximate controllability of impulsive ψ
    Caputo fractional evolution equations with nonlocal conditions, 2023, 26, 1311-0454, 1326, 10.1007/s13540-023-00164-1
    5. Rajesh Dhayal, J. F. Gómez-Aguilar, Eduardo Pérez-Careta, Stability and controllability of ψ
    -Caputo fractional stochastic differential systems driven by Rosenblatt process with impulses, 2024, 12, 2195-268X, 1626, 10.1007/s40435-023-01286-3
    6. Danuruj Songsanga, Parinya Sa Ngiamsunthorn, A modified predictor–corrector scheme with graded mesh for numerical solutions of nonlinear Ψ-caputo fractional-order systems, 2025, 23, 2391-5455, 10.1515/math-2024-0127
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2112) PDF downloads(81) Cited by(4)

Figures and Tables

Tables(11)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog