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Research article

A novel decision model with Einstein aggregation approach for garbage disposal plant site selection under q-rung orthopair hesitant fuzzy rough information

  • Environmental science and pollution research has benefits around the globe. Human activity produces more garbage throughout the day as the world's population and lifestyles rise. Choosing a garbage disposal site (GDS) is crucial to effective disposal. In illuminated of the advancements in society, decision-makers concede a significant challenge for assessing an appropriate location for a garbage disposal site. This research used a multi-attribute decision-making (MADM) approach based on q-rung orthopair hesitant fuzzy rough (q-ROHFR) Einstein aggregation information for evaluating GDS selection schemes and providing decision-making (DM) support to select a suitable waste disposal site. In this study, first, q-ROHFR Einstein average aggregation operators are integrated. Some intriguing characteristics of the suggested operators, such as monotonicity, idempotence and boundedness were also explored. Then, a MADM technique was established using the novel concept of q-ROHFR aggregation operators under Einstein t-norm and t-conorm. In order to help the decision makers (DMs) make a final choice, this technique aims to rank and choose an alternative from a collection of feasible alternatives, as well as to propose a solution based on the ranking of alternatives for a problem with conflicting criteria. The model's adaptability and validity are then demonstrated by an analysis and solution of a numerical issue involving garbage disposal plant site selection. We performed a the sensitivity analysis of the proposed aggregation operators to determine the outcomes of the decision-making procedure. To highlight the potential of our new method, we performed a comparison study using the novel extended TOPSIS and VIKOR schemes based on q-ROHFR information. Furthermore, we compared the results with those existing in the literature. The findings demonstrate that this methodology has a larger range of information representation, more flexibility in the assessment environment, and improved consistency in evaluation results.

    Citation: Attaullah, Asghar Khan, Noor Rehman, Fuad S. Al-Duais, Afrah Al-Bossly, Laila A. Al-Essa, Elsayed M Tag-eldin. A novel decision model with Einstein aggregation approach for garbage disposal plant site selection under q-rung orthopair hesitant fuzzy rough information[J]. AIMS Mathematics, 2023, 8(10): 22830-22874. doi: 10.3934/math.20231163

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  • Environmental science and pollution research has benefits around the globe. Human activity produces more garbage throughout the day as the world's population and lifestyles rise. Choosing a garbage disposal site (GDS) is crucial to effective disposal. In illuminated of the advancements in society, decision-makers concede a significant challenge for assessing an appropriate location for a garbage disposal site. This research used a multi-attribute decision-making (MADM) approach based on q-rung orthopair hesitant fuzzy rough (q-ROHFR) Einstein aggregation information for evaluating GDS selection schemes and providing decision-making (DM) support to select a suitable waste disposal site. In this study, first, q-ROHFR Einstein average aggregation operators are integrated. Some intriguing characteristics of the suggested operators, such as monotonicity, idempotence and boundedness were also explored. Then, a MADM technique was established using the novel concept of q-ROHFR aggregation operators under Einstein t-norm and t-conorm. In order to help the decision makers (DMs) make a final choice, this technique aims to rank and choose an alternative from a collection of feasible alternatives, as well as to propose a solution based on the ranking of alternatives for a problem with conflicting criteria. The model's adaptability and validity are then demonstrated by an analysis and solution of a numerical issue involving garbage disposal plant site selection. We performed a the sensitivity analysis of the proposed aggregation operators to determine the outcomes of the decision-making procedure. To highlight the potential of our new method, we performed a comparison study using the novel extended TOPSIS and VIKOR schemes based on q-ROHFR information. Furthermore, we compared the results with those existing in the literature. The findings demonstrate that this methodology has a larger range of information representation, more flexibility in the assessment environment, and improved consistency in evaluation results.



    Fractional differential equations (FDEs) provide many mathematical models in physics, biology, economics, and chemistry, etc [1,2,3,4]. In fact, it consists of many integrals and derivative operators of non-integer orders, which generalize the theory of ordinary differentiation and integration. Hence, a more general approach is allowed to calculus and one can say that the aim of the FDEs is to consider various phenomena by studying derivatives and integrals of arbitrary orders. For intercalary specifics about the theory of FDEs, the readers are referred to the books of Kilbas et al.[2] and Podlubny [4]. In the literature, several concepts of fractional derivatives have been represented, consisting of Riemann-Liouville, Liouville-Caputo, generalized Caputo, Hadamard, Katugampola, and Hilfer derivatives. The Hilfer fractional derivative [5] extends both Riemann-Liouville and Caputo fractional derivatives. For applications of Hilfer fractional derivatives in mathematics and physics, etc see [6,7,8,9,10,11]. For recent results on boundary value problems for fractional differential equations and inclusions with the Hilfer fractional derivative see the survey paper by Ntouyas [12]. The ψ-Riemann-Liouville fractional integral and derivative operators are discussed in [1], while the ψ-Hilfer fractional derivative is discussed in [13]. Recently, the notion of a generalized proportional fractional derivative was introduced by Jarad et al. [14,15,16]. For some recent results on fractional differential equations with generalized proportional derivatives, see [17,18].

    In [19], an existence result was proved via Krasnosel'ski˘i's fixed-point theorem for the following sequential boundary value problem of the form

    {HDα,ς,ψ[HDβ,ς,ψp(w)ϕ(w,p(w))ni=1Iνi;ψhi(w,p(w))]=Υ(w,p(w)),w[a,b],p(a)=0,HDb,ς,ψp(a)=0,p(b)=τp(ζ), (1.1)

    where HDω,ς,ψ indicates the ψ-Hilfer fractional derivative of order ω{α,β}, with 0<α1, 1<β2, 0ς<1, Iνi;ψ is the ψ-Riemann–Liouville fractional integral of order νi>0, for i=1,2,,n, hiC([0,1]×R,R), for i=1,2,,n, ϕC([0,1]×R,R{0}), ΥC([0,1]×R,R), τR and ζ(a,b). In [16], the consideration of Hilfer-type generalized proportional fractional derivative operators was initiated.

    Coupled systems of fractional order are also significant, as such systems appear in the mathematical models in science and engineering, such as bio-engineering [20], fractional dynamics [21], financial economics [22], etc. Coupled systems of FDEs with diverse boundary conditions have been the focus of many researches. In [23], the authors studied existence and Ulam-Hyers stability results of a coupled system of ψ-Hilfer sequential fractional differential equations. Existence and uniqueness results are derived in [24] for a coupled system of Hilfer-Hadamard fractional differential equations with fractional integral boundary conditions. Recently, in [25] a coupled system of nonlinear fractional differential equations involving the (k,ψ)-Hilfer fractional derivative operators complemented with multi-point nonlocal boundary conditions were discussed. Moreover, Samadi et al. [26] have considered a coupled system of Hilfer-type generalized proportional fractional differential equations.

    In this article, motivated by the above works, we study a coupled system of ψ-Hilfer sequential generalized proportional FDEs with boundary conditions generated by the problem (1.1). More precisely, we consider the following coupled system of nonlinear proportional ψ-Hilfer sequential fractional differential equations with multi-point nonlocal boundary conditions of the form

    {HDν1,ϑ1;ς;ψ[HDν2,ϑ2;ς;ψp1(w)Φ1(w,p1(w),p2(w))ni=1pIηi,ς,ψHi(w,p1(w),p2(w))]=Υ1(w,p1(w),p2(w)),w[t1,t2],HDν3,ϑ3;ς;ψ[HDν4,ϑ4;ς;ψp1(w)Φ2(w,p1(w),p2(w))mj=1pI¯ηj,ς,ψGj(w,p1(w),p2(w))]=Υ2(w,p1(w),p2(w)),w[t1,t2],p1(t1)=HDν2,ϑ2;ς;ψp1(t1)=0,p1(t2)=θ1p2(ξ1),p2(t1)=HDν4,ϑ4;ς;ψp2(t1)=0,p2(t2)=θ2p1(ξ2), (1.2)

    where HDν,ϑ1;ς;ψ denotes the ψ-Hilfer generalized proportional derivatives of order ν{ν1,ν2,ν3,ν4}, with parameters ϑl, 0ϑl1, l{1,2,3,4}, ψ is a continuous function on [t1,t2], with ψ(w)>0, pIη,ς,ψ is the generalized proportional integral of order η>0, η{ηi,ηj}, θ1,θ2R, ξ1,ξ2[t1,t2], Φ1,Φ2C([t1,t2]×R×R,R{0}) and Hi,Gj,Υ1,Υ2C([t1,t2]×R×R,R), for i=1,2,,n and j=1,2,,m.

    We emphasize that:

    ● We study a general system involving ψ-Hilfer proportional fractional derivatives.

    ● Our equations contain fractional derivatives of different orders as well as sums of fractional integrals of different orders.

    ● Our system contains nonlocal coupled boundary conditions.

    ● Our system covers many special cases by fixing the parameters involved in the problem. For example, taking ψ(w)=w, it will reduce to a coupled system of Hilfer sequential generalized proportional FDEs with boundary conditions, while if ς=1, it reduces to a coupled system of ψ-Hilfer sequential FDEs. Besides, by taking Φ1,Φ2=1 in the problem (1.2), then we obtain the following new coupled system of the form:

    {HDν1,ϑ1;ς;ψ[HDν2,ϑ2;ς;ψp1(w)ni=1pIηi,ς,ψHi(w,p1(w),p2(w))]=Υ1(w,p1(w),p2(w)),w[t1,t2],HDν3,ϑ3;ς;ψ[HDν4,ϑ4;ς;ψp1(w)mj=1pI¯ηj,ς,ψGj(w,p1(w),p2(w))]=Υ2(w,p1(w),p2(w)),w[t1,t2],p1(t1)=HDν2,ϑ2;ς;ψp1(t1)=0,p1(t2)=θ1p2(ξ1),p2(t1)=HDν4,ϑ4;ς;ψp2(t1)=0,p2(t2)=θ2p1(ξ2).

    In obtaining the existence result of the problem (1.2), first the problem (1.2) is converted into a fixed-point problem and then a generalization of Krasnosel'ski˘i's fixed-point theorem due to Burton is applied.

    The structure of this article has been organized as follows: In Section 2, some necessary concepts and basic results concerning our problem are presented. The main result for the problem (1.2) is proved in Section 3, while Section 4 contains an example illustrating the obtained result.

    In this section, we summarize some known definitions and lemmas needed in our results.

    Definition 2.1. [17,18] Let ς(0,1] and ν>0. The fractional proportional integral of order ν of the continuous function F is defined by

    pIν,ς,ψF(w)=1ςνΓ(ν)wt1eς1ς(ψ(w)ψ(s))(ψ(w)ψ(s))ν1F(s)ψ(s)ds,t1>w.

    Definition 2.2. [17,18] Let ς(0,1], ν>0, and ψ(w) is a continuous function on [t1,t2], ψ(w)>0. The generalized proportional fractional derivative of order ν of the continuous function F is defined by

    (pDν,ς,ψF)(w)=(pDn,ς,ψ)ςnνΓ(nν)wt1eς1ς(ψ(w)ψ(s))(ψ(w)ψ(s))nν1F(s)ψ(s)ds,

    where n=[ρ]+1 and [ν] denotes the integer part of the real number ν, where Dn,ς,ψ=Dς,ψDς,ψntimes.

    Now the generalized Hilfer proportional fractional derivative of order ν of function F with respect to another function ψ is introduced.

    Definition 2.3. [27] Let ς(0,1], F,ψCm([t1,t2],R) in which ψ is positive and strictly increasing with ψ(w)0 for all w[t1,t2]. The ψ-Hilfer generalized proportional fractional derivative of order ν and type ϑ for F with respect to another function ψ is defined by

    (HDν,ϑ,ς,ψF)(w)=pIϑ(nν),ς,ψ[pDn,ς,ψ(pI(1ϑ)(nν),ς,ψF)](w),

    where n1<ν<n and 0ϑ1.

    Lemma 2.4. [27] Let m1<ν<m,nN, 0<ς1, 0ϑ1 and m1<γ<m such that γ=ν+mϑνϑ. If FC([t1,t2],R) and pI(mγ,ς,ψ)FCm([t1,t2],R), then

    (pIν,ς,ψHDν,ϑ,ς,ψF)(w)=F(w)nj=1eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γjςγjΓ(γj+1)(pIjγ,ς,ψF)(t1).

    To prove the main result we need the following lemma, which concerns a linear variant of the ψ-Hilfer sequential proportional coupled system (1.2). This lemma plays a pivotal role in converting the nonlinear problem in system (1.2) into a fixed-point problem.

    Lemma 2.5. Let 0<ν1,ν31, 1<ν2,ν42, 0ϑi1, γi=νi+ϑi(1νi), i=1,3 and γj=νj+ϑj(2νj), j=2,4, Θ=M1N2M2N10, ψ is a continuous function on [t1,t2], with ψ(w)>0, and Q1,Q2C([t1,t2],R), Φ1,Φ2C([t1,t2]×R×R,R{0}) and Hi,Gj,Q1,Q2C([t1,t2]×R×R,R), for i=1,2,,n and j=1,2,,m, and pI(1γi,ς,ψ)QjCm([t1,t2],R),i=1,2,3,4,j=1,2. Then the pair (p1,p2) is a solution of the system

    {HDν1,ϑ1;ς;ψ[HDν2,ϑ2;ς;ψp1(w)Φ1(w,p1(w),p2(w))ni=1pIηi,ς,ψHi(w,p1(w),p2(w))]=Q1(w),w[t1,t2],HDν3,ϑ3;ς;ψ[HDν4,ϑ4;ς;ψp1(w)Φ2(w,p1(w),p2(w))mj=1pI¯ηj,ς,ψGj(w,p1(w),p2(w))]=Q2(w),w[t1,t2],p1(t1)=HDν2,ϑ2;ς;ψp1(t1)=0,p1(t2)=θ1p2(ξ1),p2(t1)=HDν4,ϑ4;ς;ψp2(t1)=0,p2(t2)=θ2p1(ξ2),

    if and only if

    p1(w)=pIν2,ς,ψΦ1(w,p1(w),p2(w))(ni=1pIηi,ς,ψHi(w,p1(w),p2(w))+pIν1,ς,ψQ1(w))+eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ21Θςγ21Γ(γ2){N2[θ1pIν4,ς,ψΦ2(ξ1,p1(ξ1),p2(ξ1))
    ×(mj=1pI¯ηj,ς,ψGj(ξ1,p1(ξ1),p2(ξ1))+pIν3,ς,ψQ2(ξ1)))pIν2,ς,ψΦ1(t2,p1(t2),p2(t2))×(ni=1pIηi,ς,ψHi(t2,p1(t2),p2(t2))+pIν1,ς,ψQ1(t2))]+M2[θ2pIν2,ς,ψΦ1(ξ2,p1(ξ2),p2(ξ2))×(ni=1pIηi,ς,ψHi(ξ2,p1(ξ2),p2(ξ2))+pIν1,ς,ψQ1(ξ2)))pIν4,ς,ψΦ2(t2,p1(t2),p2(t2))×(mj=1pI¯ηj,ς,ψGj(t2,p1(t2),p2(t2))+pIν3,ς,ψQ2(t2))]} (2.1)

    and

    p2(w)=pIν4,ς,ψΦ2(w,p1(w),p2(w))(mj=1pIˉηj,ς,ψGj(w,p1(w),p2(w))+pIν3,ς,ψQ2(w))
    +eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ41Θςγ41Γ(γ4){N1[θ1pIν4,ς,ψΦ2(ξ1,p1(ξ1),p2(ξ1))×(mj=1pI¯ηj,ς,ψGj(ξ1,p1(ξ1),p2(ξ1))+pIν3,ς,ψQ2(ξ1)))pIν2,ς,ψΦ2(t2,p1(t2),p2(t2))×(ni=1pIηi,ς,ψHi(t2,p1(t2),p2(t2))+pIν1,ς,ψQ1(t2))]+M1[θ2pIν2,ς,ψΦ1(ξ2,p1(ξ2),p2(ξ2))×(ni=1pIηi,ς,ψHi(ξ2,p1(ξ2),p2(ξ2))+pIν1,ς,ψQ1(ξ2)))pIν4,ς,ψΦ2(t2,p1(t2),p2(t2))×(mj=1pI¯ηj,ς,ψGj(t2,p1(t2),p2(t2))+pIν3,ς,ψQ2(t2))]}, (2.2)

    where

    M1=eς1ς(ψ(t2)ψ(t1))(ψ(t2)ψ(t1))γ21ςγ21Γ(γ2),M2=θ1eς1ς(ψ(ξ1)ψ(t1))(ψ(ξ1)ψ(t1))γ41ςγ41Γ(γ4),N1=θ2eς1ς(ψ(ξ2)ψ(t1))(ψ(ξ2)ψ(t1))γ21ςγ21Γ(γ2),N2=eς1ς(ψ(t2)ψ(t1))(ψ(t2)ψ(t1))γ41ςγ41Γ(γ4). (2.3)

    Proof. Due to Lemma 2.4 with m=1, we get

    HDν2,ϑ2;ς;ψp1(w)Φ1(w,p1(w),p2(w))ni=1pIηi,ς,ψHi(w,p1(w),p2(w))=pIν1;ς;ψQ1(w)+c0eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ11ςγ11Γ(γ1),HDν4,ϑ4;ς;ψp2(w)Φ2(w,p1(w),p2(w))mj=1pI¯ηj,ς,ψGj(w,p1(w),p2(w))=pIν3;ς;ψQ2(w)+d0eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ31ςγ31Γ(γ3), (2.4)

    where c0,d0R. Now applying the boundary conditions

    HDν2,ϑ2;ς;ψp1(t1)=HDν4,ϑ4;ς;ψp1(t1)=0,

    we get c0=d0=0. Hence

    HDν2,ϑ2;ς;ψp1(w)=Φ1(w,p1(w),p2(w))(ni=1pIηi,ς,ψHi(w,p1(w),p2(w))+pIν1;ς;ψQ1(w)),HDν4,ϑ4;ς;ψp2(w)=Φ2(w,p1(w),p2(w))(mj=1pI¯ηj,ς,ψGj(w,p1(w),p2(w))+pIν3;ς;ψQ2(w)). (2.5)

    Now, by taking the operators pIν2,ς,ψ and pIν4,ς,ψ into both sides of (2.5) and using Lemma 2.4, we get

    p1(w)=pIν2;ς;ψΦ1(w,p1(w),p2(w))(ni=1pIηi,ς,ψHi(w,p1(w),p2(w))+pIν1;ς;ψQ1(w))+c1eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ21ςγ21Γ(γ2)+c2eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ22ςγ22Γ(γ21),p2(w)=pIν4;ς;ψΦ2(w,p1(w),p2(w))(mj=1pI¯ηj,ς,ψGj(w,p1(w),p2(w))+pIν3;ς;ψQ2(w))+d1eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ41ςγ41Γ(γ4)+d2eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ42ςγ42Γ(γ41). (2.6)

    Applying the conditions p1(t1)=p2(t1)=0 in (2.6), we get c2=d2=0 since γ2[ν2,2] and γ4[ν4,2]. Thus we have

    p1(w)=pIν2;ς;ψ(Φ1(w,p1(w),p2(w))(ni=1pIηi,ς,ψHi(w,p1(w),p2(w))+pIν1;ς;ψQ1(w)))+c1eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ21ςγ21Γ(γ2),p2(w)=pIν4;ς;ψ(Φ2(w,p1(w),p2(w))(mj=1pI¯ηj,ς,ψGj(w,p1(w),p2(w))+pIν3;ς;ψQ2(w)))+d1eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ41ςγ41Γ(γ4). (2.7)

    In view of (2.7) and the conditions p1(t2)=θ1p2(ξ1) and p2(t2)=θ2p1(ξ2), we get

    pIν2,ς,ψΦ1(t2,p1(t2),p2(t2))(ni=1pIηi,ς,ψHi(t2,p1(t2),p2(t2))+pIν1,ς,ψQ1(t2))+c1eς1ς(ψ(t2)ψ(t1))(ψ(t2)ψ(t1))γ21ςγ21Γ(γ2)=θ1pIν4,ς,ψΦ2(ξ1,p1(ξ1),p2(ξ1))(mj=1pI¯ηj,ς,ψGj(ξ1,p1(ξ1),p2(ξ1))+pIν3,ς,ψQ2(ξ1)))+d1θ1eς1ς(ψ(ξ1)ψ(t1))(ψ(ξ1)ψ(t1))γ41ςγ41Γ(γ4), (2.8)

    and

    pIν4,ς,ψΦ2(t2,p1(t2),p2(t2))(mj=1pI¯ηj,ς,ψGj(t2,p1(t2),p2(t2))+Iν3,ς,ψQ2(t2))+d1eς1ς(ψ(t2)ψ(t1))(ψ(t2)ψ(t1))γ41ςγ21Γ(γ2)=θ2pIν2,ς,ψΦ1(ξ2,p1(ξ2),p2(ξ2))(ni=1pIηi,ς,ψHi(ξ2,p1(ξ2),p2(ξ2))+pIν1,ς,ψQ1(ξ2)))+c1θ2eς1ς(ψ(ξ2)ψ(t1))(ψ(ξ2)ψ(t1))γ21ςγ21Γ(γ2). (2.9)

    Due to (2.3), (2.8), and (2.9), we have

    c1M1d1M2=M,c1N1+d1N2=N, (2.10)

    where

    M=θ1pIν4,ς,ψΦ2(ξ1,p1(ξ1),p2(ξ1))(mj=1pI¯ηj,ς,ψGj(ξ1,p1(ξ1),p2(ξ1))+pIν3,ς,ψQ2(ξ1)))pIν2,ς,ψΦ1(t2,p1(t2),p2(t2))(ni=1pIηi,ς,ψHi(t2,p1(t2),p2(t2))+pIν1,ς,ψQ1(t2)),N=θ2pIν2,ς,ψΦ1(ξ2,p1(ξ2),p2(ξ2))(ni=1pIηi,ς,ψHi(ξ2,p1(ξ2),p2(ξ2))+pIν1,ς,ψQ1(ξ2)))pIν4,ς,ψΦ2(t2,p1(t2),p2(t2))(mj=1pI¯ηj,ς,ψGj(t2,p1(t2),p2(t2))+pIν3,ς,ψQ2(t2)).

    By solving the above system, we conclude that

    c1=1Θ[N2M+M2N],d1=1Θ[M1N+N1M].

    Replacing the values c1 and d1 in Eq (2.7), we obtain the solutions (2.1) and (2.2). The converse is obtained by direct computation. The proof is complete.

    Let Y=C([t1,t2],R)={p:[t1,t2]Ris continuous}. The space Y is a Banach space with the norm p=supw[t1,t2]|p(w)|. Obviously, the space (Y×Y,(p1,p2)) is also a Banach space with the norm (p1,p2)=p1+p2.

    Due to Lemma 2.5, we define an operator V:Y×YY×Y by

    V(p1,p2)(w)=(V1(p1,p2)(w)V2(p1,p2)(w)), (3.1)

    where

    V1(p1,p2)(w)=pIν2,ς,ψΦ1(w,p1(w),p2(w))(ni=1pIηi,ς,ψHi(w,p1(w),p2(w))+pIν1,ς,ψΥ1(w,p1(w),p2(w)))+eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ21Θςγ21Γ(γ2){N2[θ1pIν4,ς,ψΦ2(ξ1,p1(ξ1),p2(ξ1))×(mj=1pI¯ηj,ς,ψGj(ξ1,p1(ξ1),p2(ξ1))+pIν3,ς,ψΥ2(ξ1,p1(ξ1),p2(ξ1)))pIν2,ς,ψΦ1(t2,p1(t2),p2(t2))(ni=1pIηi,ς,ψHi(t2,p1(t2),p2(t2))+pIν1,ς,ψΥ1(t2,p1(t2),p2(t2)))]+M2[θ2pIν2,ς,ψΦ1(ξ2,p1(ξ2),p2(ξ2))×(ni=1pIηi,ς,ψHi(ξ2,p1(ξ2),p2(ξ2))+pIν1,ς,ψΥ1(ξ2,p1(ξ1),p2(ξ2)))pIν4,ς,ψΦ2(t2,p1(t2),p2(t2))(mj=1pI¯ηj,ς,ψGj(t2,p1(t2),p2(t2))+pIν3,ς,ψΥ2(t2,p1(t2),p2(t2)))]},w[t1,t2],

    and

    V2(p1,p2)(w)=pIν4,ς,ψΦ2(w,p1(w),p2(w))(mj=1pI¯ηj,ς,ψGj(w,p1(w),p2(w))+pIν3,ς,ψΥ2(w,p1(w),p2(w)))+eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ41Θςγ41Γ(γ4){N1[θ1pIν4,ς,ψΦ2(ξ1,p1(ξ1),p2(ξ1))×(mj=1pI¯ηj,ς,ψGj(ξ1,p1(ξ1),p2(ξ1))+pIν3,ς,ψΥ2(ξ1,p1(ξ1),p2(ξ1))))pIν2,ς,ψΦ1(t2,p1(t2),p2(t2))(ni=1pIηi,ς,ψHi(t2,p1(t2),p2(t2))+pIν1,ς,ψΥ1(t2,p1(t2),p2(t2)))]+M1[θ2pIν2,ς,ψΦ1(ξ2,p1(ξ2),p2(ξ2))×(ni=1pIηi,ς,ψHi(ξ2,p1(ξ2),p2(ξ2))+pIν1,ς,ψΥ1(ξ2,p1(ξ2),p2(ξ2))))pIν4,ς,ψΦ2(t2,p1(t2),p2(t2))(mj=1pI¯ηj,ς,ψGj(t2,p1(t2),p2(t2))+pIν3,ς,ψΥ2(t2,p1(t2),p2(t2)))]},w[t1,t2].

    To prove our main result we will use the following Burton's version of Krasnosel'ski˘i's fixed-point theorem.

    Lemma 3.1. [28] Let S be a nonempty, convex, closed, and bounded set of a Banach space (X,) and let A:XX and B:SX be two operators which satisfy the following:

    (i) A is a contraction,

    (ii) B is completely continuous, and

    (iii) x=Ax+By,ySxS.

    Then there exists a solution of the operator equation x=Ax+Bx.

    Theorem 3.2. Assume that:

    (H1) The functions Φk:[t1,t2]×R2R{0}, Υk:[t1,t2]×R2R for k=1,2 and hi,gj:[t1,t2]×R2R for i=1,2,,n,j=1,2,,m, are continuous and there exist positive continuous functions ϕk, ωk:[t1,t2]R, k=1,2, hi:[t1,t2]R, gj:[t1,t2]R i=1,2,,nj=1,2,,m, with bounds ϕk, ωk, k=1,2, and hi, i=1,2,,m, gj,j=1,2,,m, respectively, such that

    |Φ1(w,u1,u2)Φ1(w,¯u1,¯u2)|ϕ1(w)(|u1¯u1|+|u2¯u2|),|Φ2(w,u1,u2)Φ2(w,¯u1,¯u2)|ϕ2(w)(|u1¯u1|+|u2¯u2|),|Υ1(w,u1,u2)Υ1(w,¯u1,¯u2|ω1(w)(|u1¯u1|+|u2¯u2|),|Υ2(w,u1,u2)Υ2(w,¯u1,¯u2|ω2(w)(|u1¯u1|+|u2¯u2|),|Hi(w,u1,u2)Hi(w,¯u1,¯u2)|hi(w)(|u1¯u1|+|u2¯u2|),|Gj(w,u1,u2)Gj(w,¯u1,¯u2)|gj(w)(|u1¯u1|+|u2¯u2|), (3.2)

    for all w[t1,t2] and ui,¯uiR, i=1,2.

    (H2) There exist continuous functions Fk,Lk,k=1,2, λi,μj,i=1,2,,n,j=1,2,,m such that

    |Φ1(w,u1,u2)|F1(w),|Φ2(w,u1,u2)|F2(w),|Hi(w,u1,u2)|λi(w),|Gj(w,u1,u2)|μj(w),|Υ1(w,u1,u2)|L1(w),|Υ2(w,u1,u2)|L2(w), (3.3)

    for all w[t1,t2] and u1,u2R.

    (H3) Assume that

    K:={(ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)[1+(N2+M2|θ2|)(ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2)]+(N1+M1|θ2|)(ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)(ψ(t2)ψ(t1))γ41Θςγ41Γ(γ4)}×[F1ni=1hi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)+ni=1λi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)ϕ1]+{(N2|θ1|+M2)(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)(ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2)+(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)[1+(N1|θ1|+M1)(ψ(t2)ψ(t1))γ41Θςγ41Γ(γ4)]}×[F2mj=1gj(ψ(t2)ψ(t1))¯ηjς¯ηjΓ(¯ηj+1)+mj=1μj(ψ(t2)ψ(t1))¯ηjς¯ηjΓ(¯ηj+1)ϕ2]<1,

    where Fk=supt[t1,t2]|Fk(t)|, Lk=supt[t1,t2],k=1,2, λi=supt[t1,t2], i=1,2,,n, and μj=supt[t1,t2], j=1,2,,m.

    Then the ψ-Hilfer sequential proportional coupled system (1.2) has at least one solution on [t1,t2].

    Proof. First, we consider a subset S of Y×Y defined by S={(p1,p2)Y×Y:(p1,p2)r}, where r is given by

    r=R1+R2 (3.4)

    where

    R1=[1+(ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2)(N2+M2|θ2|)]F1(ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)×(ni=1λi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)+ni=1L1(ψ(t2)ψ(t1))ν1ςν1Γ(ν1+1))+[N2|θ1|+M2](ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2)F2(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)×(mj=1μj(ψ(t2)ψ(t1))¯ηjς¯ηΓ(¯η+1)+mj=1L2(ψ(t2)ψ(t1))ν3ςν3Γ(ν3+1))

    and

    R2=[1+(ψ(t2)ψ(t1))γ41Θςγ41Γ(γ4)(N1|θ1|+M1)]F2(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)×(mj=1μj(ψ(t2)ψ(t1))¯ηjς¯ηΓ(¯η+1)+mj=1L2(ψ(t2)ψ(t1))ν3ςν3Γ(ν3+1))+[N1+M1|θ2|](ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)F1(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)×(ni=1λi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)+ni=1L1(ψ(t2)ψ(t1))ν1ςν1Γ(ν1+1)).

    Let us define the operators:

    Hi(p1,p2)(w)=ni=1pIηi,ς,ψHi(w,p1(w),p2(w)),w[t1,t2],
    Gj(p1,p2)(w)=mj=1pI¯ηj,ς,ψGj(w,p1(w),p2(w)),w[t1,t2],
    Y1(p1,p2)(w)=pIν1,ς,ψΥ1(w,p1(w),p2(w)),w[t1,t2],
    Y2(p1,p2)(w)=pIν3,ς,ψΥ2(w,p1(w),p2(w)),w[t1,t2],

    and

    F1(p1,p2)(w)=Φ1(w,p1(w),p2(w)),w[t1,t2],
    F2(p1,p2)(w)=Φ2(w,p1(w),p2(w)),w[t1,t2].

    Then we have

    |Hi(¯p1,¯p2)(w)Hi(p1,p2)(w)|ni=1pIηi,ς,ψ|Hi(w,¯p1(w),¯p2(w))Hi(w,p1(w),p2(w))|ni=1hi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)(¯p1p1+¯p2p2)

    and

    |Hi(p1,p2)(w)|ni=1pIηi,ς,ψ|Hi(w,p1(w),p2(w))|ni=1λi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1).

    Also, we obtain

    |Gj(¯p1,¯p2)(w)Gj(p1,p2)(w)|mj=1pI¯ηj,ς,ψ|Gj(w,¯p1(w),¯p2(w))Gj(w,p1(w),p2(w))|mj=1gj(ψ(t2)ψ(t1))¯ηjςηiΓ(¯ηj+1)(¯p1p1+¯p2p2)

    and

    |Gj(p1,p2)(w)|mj=1pI¯ηi,ς,ψ|Hi(w,p1(w),p2(w))|mj=1μj(ψ(t2)ψ(t1))¯ηiς¯ηiΓ(¯ηi+1).

    Moreover, we have

    |Y1(¯p1,¯p2)(w)Y1(p1,p2)(w)|pIν1,ς,ψ|Υ1(w,¯p1(w),¯p2(w))Υ1(w,p1(w),p2(w))|ω1(ψ(t2)ψ(t1))ν1ςν1Γ(ν1+1)(¯p1p1+¯p2p2),
    |Y1(p1,p2)(w)|pIν1,ς,ψ|Υ1(w,p1(w),p2(w))|L1(ψ(t2)ψ(t1))ν1ςν1Γ(ν1+1),

    and

    |Y2(¯p1,¯p2)(w)Y2(p1,p2)(w)|pIν3,ς,ψ|Υ2(w,¯p1(w),¯p2(w))Υ2(w,p1(w),p2(w))|ω2(ψ(t2)ψ(t1))ν3ςν3Γ(ν3+1)(¯p1p1+¯p2p2),
    |Y2(p1,p2)(w)|pIν1,ς,ψ|Υ2(w,p1(w),p2(w))|L2(ψ(t2)ψ(t1))ν3ςν3Γ(ν3+1).

    Finally, we get

    |F1(¯p1,¯p2)(w)F1(p1,p2)(w)||Φ1(w,¯p1(w),¯p2(w))Φ1(w,p1(w),p2(w))|ϕ1(¯p1p1+¯p2p2),
    |F1(p1,p2)(w)||Φ1(w,p1(w),p2(w))|F1,

    and

    |F2(¯p1,¯p2)(w)F2(p1,p2)(w)||Φ2(w,¯p1(w),¯p2(w))Φ2(w,p1(w),p2(w))|ϕ2(¯p1p1+¯p2p2),
    |F2(p1,p2)(w)||Φ2(w,p1(w),p2(w))|F2.

    Now we split the operator V as

    V1(p1,p2)(w)=V1,1(p1,p2)(w)+V1,2(p1,p2)(w),V2(p1,p2)(w)=V2,1(p1,p2)(w)+V2,2(p1,p2)(w),

    with

    V1,1(p1,p2)(w)=pIν2,ς,ψF1(p1,p2)(w)Hi(p1,p2)(w)+eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ21Θςγ21Γ(γ2)×{N2[θ1pIν4,ς,ψF2(p1,p2)(w)Gj(p1,p2)(w)pIν2,ς,ψF1(p1,p2)(w)Hi(p1,p2)(w)]+M2[θ2pIν2,ς,ψF1(p1,p2)(w)Hi(p1,p2)(w)pIν4,ς,ψF2(p1,p2)(w)Gj(p1,p2)(w)]},V1,2(p1,p2)(w)=pIν2,ς,ψF1(p1,p2)(w)Y1(p1,p2)(w)+eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ21Θςγ21Γ(γ2)×{N2[θ1pIν4,ς,ψF2(p1,p2)(w)Y2(p1,p2)(w)pIν2,ς,ψF1(p1,p2)(w)Y1(p1,p2)(w)]+M2[θ2pIν2,ς,ψF1(p1,p2)(w)Y1(p1,p2)(w)pIν4,ς,ψF2(p1,p2)(w)Y2(p1,p2)(w)]},V2,1(p1,p2)(w)=pIν4,ς,ψF2(p1,p2)(w)Gj(p1,p2)(w)+eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ41Θςγ41Γ(γ4)×{N1[θ1pIν4,ς,ψF2(p1,p2)(w)Gj(p1,p2)(w)pIν2,ς,ψF1(p1,p2)(w)Hi(p1,p2)(w)]+M1[θ2pIν2,ς,ψF1(p1,p2)(w)Hi(p1,p2)(w)pIν4,ς,ψF2(p1,p2)(w)Gj(p1,p2)(w)]},

    and

    V2,2(p1,p2)(w)=pIν4,ς,ψF2(p1,p2)(w)Y2(p1,p2)(w)+eς1ς(ψ(w)ψ(t1))(ψ(w)ψ(t1))γ41Θςγ41Γ(γ4)×{N1[θ1pIν4,ς,ψF2(p1,p2)(w)Y2(p1,p2)(w)pIν2,ς,ψF1(p1,p2)(w)Y1(p1,p2)(w)]+M1[θ2pIν2,ς,ψF1(p1,p2)(w)Y1(p1,p2)(w)pIν4,ς,ψF2(p1,p2)(w)Y2(p1,p2)(w)]}.

    In the following, we will show that the operators V1 and V2 fulfill the assumptions of Lemma 3.1. We divide the proof into three steps:

    Step 1. The operators V1,1 and V2,1 are contraction mappings. For all (p1,p2),(¯p1,¯p2)Y×Y we have

    |V1,1(¯p1,¯p2)(w)V1,1(p1,p2)(w)|(ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)|F1(¯p1,¯p2)(w)Hi(¯p1,¯p2)(w)F1(p1,p2)(w)Hi(p1,p2)(w)|
    +(ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2){N1|θ1|(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)|F2(¯p1,¯p2)(w)Gj(¯p1,¯p2)(w)
    F2(p1,p2)(w)Gj(p1,p2)(w)|+(N2+M2|θ2|)(ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)|F1(¯p1,¯p2)(w)Hi(¯p1,¯p2)(w)F1(p1,p2)(w)Hi(p1,p2)(w)|
    +M2(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)|F2(¯p1,¯p2)(w)Gj(¯p1,¯p2)(w)F2(p1,p2)(w)Gj(p1,p2)(w)|}(ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)[1+(N2+M2|θ2|)(ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2)]
    ×|F1(¯p1,¯p2)(w)Hi(¯p1,¯p2)(w)F1(p1,p2)(w)Hi(p1,p2)(w)|+(N2|θ1|+M2)(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)(ψ(t2)ψ(t1))γ21Θςγ21×|F2(¯p1,¯p2)(w)Gj(¯p1,¯p2)(w)F2(p1,p2)(w)Gj(p1,p2)(w)|
    (ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)[1+(N2+M2|θ2|)(ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2)]×[|F1(¯p1,¯p2)(w)||Hi(¯p1,¯p2)(w)Hi(p1,p2)(w)|+|Hi(p1,p2)(w)||F1(¯p1,¯p2)(w)F1(p1,p2)(w)]+(N2|θ1|+M2)(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)(ψ(t2)ψ(t1))γ21Θςγ21×[|F2(¯p1,¯p2)(w)||Gj(¯p1,¯p2)(w)Gj(p1,p2)(w)|
    +|Gj(p1,p2)(w)||F2(¯p1,¯p2)(w)F2(p1,p2)(w)|](ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)[1+(N2+M2|θ2|)(ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2)]×[F1ni=1hi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)(¯p1p1+¯p2p2)+ni=1λi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)ϕ1(¯p1p1+¯p2p2)]
    +(N2|θ1|+M2)(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)(ψ(t2)ψ(t1))γ21Θςγ21×[F2mj=1gj(ψ(t2)ψ(t1))¯ηjςηiΓ(¯ηj+1)(¯p1p1+¯p2p2)+mj=1μj(ψ(t2)ψ(t1))¯ηjς¯ηjΓ(¯ηj+1)ϕ2(¯p1p1+¯p2p2)]
    ={(ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)[1+(N2+M2|θ2|)(ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2)]×[F1ni=1hi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)+ni=1λi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)ϕ1]+(N2|θ1|+M2)(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)(ψ(t2)ψ(t1))γ21Θςγ21Γ(γ2)×[F2mj=1gj(ψ(t2)ψ(t1))¯ηjς¯ηiΓ(¯ηj+1)+mj=1μj(ψ(t2)ψ(t1))¯ηjς¯ηjΓ(¯ηj+1)ϕ2]}×(¯p1p1+¯p2p2).

    Similarly we can find

    |V2,1(¯p1,¯p2)(w)V2,1(p1,p2)(w)|{(ψ(t2)ψ(t1))ν4ςν4Γ(ν4+1)[1+(N1|θ1|+M1)(ψ(t2)ψ(t1))γ41Θςγ41Γ(γ4)]×[F2mj=1gj(ψ(t2)ψ(t1))¯ηjςηiΓ(¯ηj+1)+nj=1μj(ψ(t2)ψ(t1))¯ηjς¯ηjΓ(¯ηj+1)ϕ2]+(N1+M1|θ2|)(ψ(t2)ψ(t1))ν2ςν2Γ(ν2+1)(ψ(t2)ψ(t1))γ41Θςγ41Γ(γ4)×[F1ni=1hi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)+ni=1λi(ψ(t2)ψ(t1))ηiςηiΓ(ηi+1)ϕ1]}×(¯p1p1+¯p2p2).

    Consequently, we get

    \begin{eqnarray*} \|(\mathbb{V}_{1, 1} , \mathbb{V}_{2, 1})(\overline{p}_{1} , \overline{p}_{2})-(\mathbb{V}_{1, 1} , \mathbb{V}_{2, 1})(p_{1} , p_{2}) \|\le K (\| \overline{p}_{1}-p_{1}\|+ \| \overline{p}_{2}-p_{2}\|), \end{eqnarray*}

    which means that (\mathbb{V}_{1, 1}, \mathbb{V}_{2, 1}) is a contraction.

    Step 2. The operator \mathbb{V}_{2} = (\mathbb{V}_{1, 2}, \mathbb{V}_{2, 2}) is completely continuous on S. For continuity of \mathbb{V}_{1, 2} , take any sequence of points (p_n, q_n) in S converging to a point (p, q) \in S. Then, by the Lebesgue dominated convergence theorem, we have

    \begin{eqnarray*} \lim\limits_{n\to \infty}\mathbb{V}_{1, 2}(p_n, q_n)(w) & = & {}^{p}I^{\nu_{2}, \varsigma, \psi} \lim\limits_{n\to \infty}\mathcal{F}_1(p_n, q_n) (w) \lim\limits_{n\to \infty}\mathcal{Y}_1(p_n, q_n)(w) \\ &&+ \frac{e^{\frac{\varsigma-1}{\varsigma}(\psi(w)-\psi(t_{1}))}(\psi(w)-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})} \\ &&\times \Bigg\{N_{2}\bigg[\theta_{1} {}^{p}I^{\nu_{4}, \varsigma, \psi}\lim\limits_{n\to \infty}\mathcal{F}_2(p_n, q_n)(w) \lim\limits_{n\to \infty}\mathcal{Y}_2(p_n, q_n)(w) \\ &&- {}^{p}I^{\nu_{2}, \varsigma, \psi} \lim\limits_{n\to \infty}\mathcal{F}_1(p_n, q_n) (w) \lim\limits_{n\to \infty}\mathcal{Y}_1(p_n, q_n)(w)\bigg]\\ &&+ M_{2}\bigg[\theta_{2} {}^{p}I^{\nu_{2}, \varsigma, \psi} \lim\limits_{n\to \infty}\mathcal{F}_1(p_n, q_n) (w) \lim\limits_{n\to \infty}\mathcal{Y}_1(p_n, q_n)(w) \\ &&- {}^{p}I^{\nu_{4}, \varsigma, \psi}\lim\limits_{n\to \infty}\mathcal{F}_2(p_n, q_n)(w) \lim\limits_{n\to \infty}\mathcal{Y}_2(p_n, q_n)(w) \bigg]\Bigg\} \\ & = & {}^{p}I^{\nu_{2}, \varsigma, \psi} \mathcal{F}_1(p, q) (w) \mathcal{Y}_1(p, q)(w) \\ &&+ \frac{e^{\frac{\varsigma-1}{\varsigma}(\psi(w)-\psi(t_{1}))}(\psi(w)-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})} \\ &&\times \Bigg\{N_{2}\bigg[\theta_{1} {}^{p}I^{\nu_{4}, \varsigma, \psi}\mathcal{F}_2(p, q)(w) \mathcal{Y}_2(p, q)(w) \\ &&- {}^{p}I^{\nu_{2}, \varsigma, \psi} \mathcal{F}_1(p, q) (w) \mathcal{Y}_1(p, q)(w)\bigg]\\ &&+ M_{2}\bigg[\theta_{2} {}^{p}I^{\nu_{2}, \varsigma, \psi} \mathcal{F}_1(p, q) (w) \mathcal{Y}_1(p, q)(w) \\ &&- {}^{p}I^{\nu_{4}, \varsigma, \psi}\mathcal{F}_2(p, q)(w) \mathcal{Y}_2(p, q)(w) \bigg]\Bigg\}\\ & = &\mathbb{V}_{1, 2}(p, q)(w), \end{eqnarray*}

    for all w\in [t_1, t_2]. Similarly, we prove \lim_{n\to \infty}\mathbb{V}_{2, 2}(p_n, q_n)(w) = \mathbb{V}_{2, 2}(p, q)(w) for all w\in [t_1, t_2]. Thus \mathbb{V}_{2}(p_n, q_n) = (\mathbb{V}_{1, 2}(p_n, q_n), \mathbb{V}_{2, 2}(p_n, q_n)) converges to \mathbb{V}_{2}(p, q) on [t_1, t_2], which shows that \mathbb{V}_{2} is continuous.

    Next, we show that the operator (\mathbb{V}_{1, 2}, \mathbb{V}_{2, 2}) is uniformly bounded on S. For any (p_1, p_2)\in S we have

    \begin{eqnarray*} |\mathbb{V}_{1, 2}(p_1, p_2)(w)| &\le& {}^{p}I^{\nu_{2}, \varsigma, \psi}|\mathcal{F}_1(p_1, p_2)(w) \mathcal{Y}_1(p_1, p_2)(w)| \\ &&+\frac{(\psi(w)-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})} \Bigg\{N_{2}\bigg[|\theta_{1}| {}^{p}I^{\nu_{4}, \varsigma, \psi}|\mathcal{F}_2(p_1, p_2)(w) \mathcal{Y}_2(p_1, p_2)(w)|\\ &&+ {}^{p}I^{\nu_{2}, \varsigma, \psi}|\mathcal{F}_1(p_1, p_2) (w) \mathcal{Y}_1 (p_1, p_2)(w)| \bigg]\\ &&+ M_{2}\bigg[|\theta_{2}| {}^{p}I^{\nu_{2}, \varsigma, \psi}|\mathcal{F}_1(p_1, p_2) (w) \mathcal{Y}_1 (p_1, p_2)(w)| \\ &&+ {}^{p}I^{\nu_{4}, \varsigma, \psi}|\mathcal{F}_2(p_1, p_2)(w) \mathcal{Y}_2(p_1, p_2)(w)| \bigg]\Bigg\}\\ &\le&\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{2}}} {\varsigma^{\nu_{2}}\Gamma(\nu_{2}+1)}\|F_1\| \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}+\frac{(\psi(t_{2})-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})}\\ &&\times\Bigg\{N_2|\theta_1|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\|F_2\| \|L_2 \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}\\ &&+N_2\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{2}}} {\varsigma^{\nu_{2}}\Gamma(\nu_{2}+1)}\|F_1\| \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\\ &&+M_2|\theta_2|\|F_1\| \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\\ &&+M_2\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\| F_2 \| \|L_2\|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}\Bigg\}: = \Lambda_1. \end{eqnarray*}

    Similarly we can prove that

    \begin{eqnarray*} |\mathbb{V}_{2, 2}(p_1, p_2)(w)|&\le&\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4}+1)}\|F_2\| \|L_2\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}+\frac{(\psi(t_{2})-\psi(t_{1}))^{\gamma_{4}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})}\\ &&\times\Bigg\{N_1|\theta_1|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\|F_2\| \|L_2 \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}\\ &&+N_1\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{2}}} {\varsigma^{\nu_{2}}\Gamma(\nu_{2}+1)}\|F_1\| \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\\ &&+M_1|\theta_2|\|F_1\|\sum\limits_{i = 1}^{n} \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\\ &&+M_1\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\| F_2 \| \|L_2\|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}\Bigg\}: = \Lambda_2. \end{eqnarray*}

    Therefore \|\mathbb{V}_{1, 2}\|+\|\mathbb{V}_{2, 2}\|\le \Lambda_1+\Lambda_2, (p_1, p_2)\in S, which shows that the operator (\mathbb{V}_{1, 2}, \mathbb{V}_{2, 2}) is uniformly bounded on S. Finally we show that the operator (\mathbb{V}_{1, 2}, \mathbb{V}_{2, 2}) is equicontinuous. Let \tau_1 < \tau_2 and (p_1, p_2)\in S. Then, we have

    \begin{eqnarray*} && |\mathbb{V}_{1, 2}(p_1, p_2)(\tau_2)-\mathbb{V}_{1, 2}(p_1, p_2)(\tau_1)|\\ &\le& \Bigg|\frac{1}{\varsigma^{\nu_{2}}\Gamma (\nu_2)}\int_{t_1}^{\tau_1}{\psi}'(s) \left[\left(\psi(\tau_2) - \psi(s)\right)^{\nu_2 -1} - \left(\psi(\tau_1) - \psi(s)\right)^{\nu_2 -1}\right]\\ &&\times |\mathcal{F}_1(p_1, p_2) (s) \mathcal{Y}_1 (p_1, p_2)(s) |ds\\ && + \frac{1}{\varsigma^{\nu_{2}}\Gamma (\nu_2 )}\int_{\tau_1}^{\tau_2}{{\psi }'(s) \left(\psi(\tau_2) - \psi(s)\right)^{\nu_2 -1}} |\mathcal{F}_1(p_1, p_2) (s) \mathcal{Y}_1 (p_1, p_2)(s) |ds\Bigg|\\ && + \frac{\left\vert\left(\psi(\tau_2)-\psi(t_1)\right)^{\gamma_2-1} - \left(\psi(\tau_1)-\psi(t_1)\right)^{\gamma_2-1}\right\vert}{\Theta\varsigma^{\gamma_{2}-1}\Gamma (\gamma_2)}\mathbb{W} \\ &\le& \frac{1}{\varsigma^{\nu_{2}}\Gamma (\nu_2+1 )}\sum\limits_{i = 1}^{n} \|\lambda_i\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\eta_{i}}} {\varsigma^{\eta_{i}}\Gamma(\eta_{i}+1)}\Big[\left|\left(\psi(\tau_2) - \psi(t_1)\right)^{\nu_2} - \left(\psi(\tau_1) - \psi(t_1)\right)^{\nu_2 }\right|\\ &&+2(\psi(\tau_2) - \psi(\tau_1))^{\nu_2}\Big] + \frac{\left\vert\left(\psi(\tau_2)-\psi(t_1)\right)^{\gamma_2-1} - \left(\psi(\tau_1)-\psi(t_1)\right)^{\gamma_2-1}\right\vert}{\Theta\varsigma^{\gamma_{2}-1}\Gamma (\gamma_2)}\mathbb{W}, \end{eqnarray*}

    where

    \begin{eqnarray*} \mathbb{W}& = & N_2|\theta_1|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\|F_2\| \|L_2 \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}\\ &&+N_2\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{2}}} {\varsigma^{\nu_{2}}\Gamma(\nu_{2}+1)}\|F_1\| \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\\ &&+M_2|\theta_2|\|F_1\| \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\\ &&+M_2\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\| F_2 \| \|L_2\|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}. \end{eqnarray*}

    As \tau_2-\tau_1\to 0 , the right-hand side of the above inequality tends to zero, independently of (p_1, p_2) . Similarly we have |\mathbb{V}_{2, 2}(p_1, p_2)(\tau_2)-\mathbb{V}_{2, 2}(p_1, p_2)(\tau_1)|\to 0 as \tau_2-\tau_1\to 0. Thus (\mathbb{V}_{1, 2}, \mathbb{V}_{2, 2}) is equicontinuous. Therefore, it follows by the Arzelá-Ascoli theorem that (\mathbb{V}_{1, 2}, \mathbb{V}_{2, 2}) is a completely continuous operator on S.

    Step 3. We show that the third condition (iii) of Lemma 3.1 is fulfilled. Let (p_1, p_2)\in \mathbb{Y}\times \mathbb{Y} be such that, for all (\overline{p}_1, \overline{p}_2)\in S

    (p_1, p_2) = (\mathbb{V}_{1, 1}(p_1, p_2), \mathbb{V}_{2, 1}(p_1, p_2))+(\mathbb{V}_{1, 2}(\overline{p}_1, \overline{p}_2, \mathbb{V}_{2, 2}(\overline{p}_1, \overline{p}_2)).

    Then, we have

    \begin{eqnarray*} |p_1(w)|&\le&|\mathbb{V}_{1, 1}(p_1, p_2)(w)|+|\mathbb{V}_{1, 2}(\overline{p}_1, \overline{p}_2)(w)|\\ &\le& {}^{p}I^{\nu_{2}, \varsigma, \psi}|\mathcal{F}_1(p_1, p_2) (w) \mathcal{H}_i (p_1, p_2)(w)| \\ &&+ \frac{(\psi(t_2)-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})} \Bigg\{N_{2}\bigg[|\theta_{1}| {}^{p}I^{\nu_{4}, \varsigma, \psi}|\mathcal{F}_2(p_1, p_2)(w) \mathcal{G}_j(p_1, p_2)(w)|\\ &&+ {}^{p}I^{\nu_{2}, \varsigma, \psi}|\mathcal{F}_1(p_1, p_2) (w) \mathcal{H}_i (p_1, p_2)(w)| \bigg]\\ &&+ M_{2}\bigg[|\theta_{2}| {}^{p}I^{\nu_{2}, \varsigma, \psi}|\mathcal{F}_1(p_1, p_2) (w) \mathcal{H}_i (p_1, p_2)(w)| \\ &&+ {}^{p}I^{\nu_{4}, \varsigma, \psi}|\mathcal{F}_2(p_1, p_2)(w) \mathcal{G}_j(p_1, p_2)(w)| \bigg]\Bigg\} \\ &&+ {}^{p}I^{\nu_{2}, \varsigma, \psi} |\mathcal{F}_1(\overline{p}_1, \overline{p}_2) (w) \mathcal{Y}_1(\overline{p}_1, \overline{p}_2)(w)| \\ &&+ \frac{(\psi(t_2)-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})} \Bigg\{N_{2}\bigg[|\theta_{1}| {}^{p}I^{\nu_{4}, \varsigma, \psi}|\mathcal{F}_2(\overline{p}_1, \overline{p}_2)(w) \mathcal{Y}_2(\overline{p}_1, \overline{p}_2)(w)| \\ &&+ {}^{p}I^{\nu_{2}, \varsigma, \psi} |\mathcal{F}_1(\overline{p}_1, \overline{p}_2) (w) \mathcal{Y}_1(\overline{p}_1, \overline{p}_2)(w)|\bigg]\\ &&+ M_{2}\bigg[|\theta_{2}| {}^{p}I^{\nu_{2}, \varsigma, \psi} |\mathcal{F}_1(\overline{p}_1, \overline{p}_2) (w) \mathcal{Y}_1(\overline{p}_1, \overline{p}_2)(w)| \\ &&+ {}^{p}I^{\nu_{4}, \varsigma, \psi}|\mathcal{F}_2(\overline{p}_1, \overline{p}_2)(w) \mathcal{Y}_2(\overline{p}_1, \overline{p}_2)(w)| \bigg]\Bigg\}\\ &\le&\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{2}}} {\varsigma^{\nu_{2}}\Gamma(\nu_{2}+1)}\|F_1\|\sum\limits_{i = 1}^{n} \|\lambda_i\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\eta_{i}}} {\varsigma^{\eta_{i}}\Gamma(\eta_{i}+1)}+\frac{(\psi(t_{2})-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})}\\ &&\times\Bigg\{N_2|\theta_1|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\sum\limits_{j = 1}^{m} \| \mu_{j} \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\overline{\eta}_{j}}} {\varsigma^{\overline{\eta}}\Gamma(\overline{\eta}+1)}\|F_2\|\\ &&+N_2\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{2}}} {\varsigma^{\nu_{2}}\Gamma(\nu_{2}+1)}\|F_1\|\sum\limits_{i = 1}^{n} \|\lambda_i\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\eta_{i}}} {\varsigma^{\eta_{i}}\Gamma(\eta_{i}+1)}\\ &&+M_2|\theta_2|\|F_1\|\sum\limits_{i = 1}^{n} \|\lambda_i\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\eta_{i}}} {\varsigma^{\eta_{i}}\Gamma(\eta_{i}+1)}\\ &&+M_2\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\|F_2\|\sum\limits_{j = 1}^{m} \| \mu_{j} \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\overline{\eta}_{j}}} {\varsigma^{\overline{\eta}}\Gamma(\overline{\eta}+1)}\Bigg\}\\ &&+\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{2}}} {\varsigma^{\nu_{2}}\Gamma(\nu_{2}+1)}\|F_1\|\sum\limits_{i = 1}^{n} \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}+\frac{(\psi(t_{2})-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})}\\ &&\times\Bigg\{N_2|\theta_1|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\|F_2\|\sum\limits_{j = 1}^{m} \|L_2 \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}\\ &&+N_2\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{2}}} {\varsigma^{\nu_{2}}\Gamma(\nu_{2}+1)}\|F_1\|\sum\limits_{i = 1}^{n} \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\\ &&+M_2|\theta_2|\|F_1\|\sum\limits_{i = 1}^{n} \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\\ &&+M_2\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\| F_2 \|\sum\limits_{j = 1}^{m} \|L_2\|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}\Bigg\}\\ & = &\Bigg[1+\frac{(\psi(t_{2})-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})}(N_2+M_2|\theta_2|)\Bigg]\|F_1\|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_2}} {\varsigma^{\nu_2}\Gamma(\nu_2+1)}\\ &&\times\Bigg(\sum\limits_{i = 1}^{n} \|\lambda_i\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\eta_{i}}} {\varsigma^{\eta_{i}}\Gamma(\eta_{i}+1)}+\sum\limits_{i = 1}^{n} \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\Bigg)\\ &&+ [N_2|\theta_1|+M_2]\frac{(\psi(t_{2})-\psi(t_{1}))^{\gamma_{2}-1}} {\Theta\varsigma^{\gamma_{2}-1}\Gamma(\gamma_{2})}\|F_2\|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\\ &&\times\Bigg(\sum\limits_{j = 1}^{m} \| \mu_{j} \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\overline{\eta}_{j}}} {\varsigma^{\overline{\eta}}\Gamma(\overline{\eta}+1)}+\sum\limits_{j = 1}^{m} \|L_2 \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}\Bigg) = R_1. \end{eqnarray*}

    In a similar way, we find

    \begin{eqnarray*} |p_2(w)|&\le&|\mathbb{V}_{2, 1}(p_1, p_2)(w)|+|\mathbb{V}_{2, 2}(\overline{p}_1, \overline{p}_2)(w)|\\ &\le&\Bigg[1+\frac{(\psi(t_2)-\psi(t_{1}))^{\gamma_{4}-1}} {\Theta\varsigma^{\gamma_{4}-1}\Gamma(\gamma_{4})}(N_1|\theta_1|+M_1)\Bigg]\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{4}}} {\varsigma^{\nu_{4}}\Gamma(\nu_{4} +1)}\|F_2\|\\ &&\times\Bigg(\sum\limits_{j = 1}^{m} \| \mu_{j} \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\overline{\eta}_{j}}} {\varsigma^{\overline{\eta}}\Gamma(\overline{\eta}+1)}+\sum\limits_{j = 1}^{m} \|L_2 \|\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_3}} {\varsigma^{\nu_3}\Gamma(\nu_3+1)}\Bigg)\\ &&+[N_1+M_1|\theta_2|]\frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_{2}}} {\varsigma^{\nu_{2}}\Gamma(\nu_{2}+1)}\|F_1\|\frac{(\psi(t_{2})-\psi(t_{1}))^{\gamma_{4}-1}} {\varsigma^{\gamma_{4}-1}\Gamma(\gamma_{4})}\\ &&\times\Bigg(\sum\limits_{i = 1}^{n} \|\lambda_i\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\eta_{i}}} {\varsigma^{\eta_{i}}\Gamma(\eta_{i}+1)}+\sum\limits_{i = 1}^{n} \|L_1\| \frac{(\psi(t_{2})-\psi(t_{1}))^{\nu_1}} {\varsigma^{\nu_1}\Gamma(\nu_1+1)}\Bigg) = R_2. \end{eqnarray*}

    Adding the previous inequalities, we obtain

    \|p_1\|+\|p_2\| \le R_1+R_2 = r.

    As \|(p_1, p_2)\| = \|p_1\|+\|p_2\|, we have that \|(p_1, p_2)\|\le r and so condition (iii) of Lemma 3.1 holds.

    By Lemma 3.1, the \psi -Hilfer sequential proportional coupled system (1.2) has at least one solution on [t_1, t_2]. The proof is finished.

    Let us consider the following coupled system of nonlinear sequential proportional Hilfer fractional differential equations with multi-point boundary conditions:

    \begin{equation} \begin{cases} {}^{H}D^{\frac{1}{3}, \frac{1}{5}; \frac{3}{7}; \log w} \bigg[\frac{ {}^{H}D^{\frac{5}{4}, \frac{2}{5}; \frac{3}{7}; \log w}p_{1}(w)}{\Phi_{1}(w, p_{1}(w), p_{2}(w))} -\sum\limits_{i = 1}^{2}{} {}^{p}I^{\eta_{i}, \varsigma, \psi}H_{i}(w, p_{1}(w), p_{2}(w))\bigg] = \Upsilon_{1}(w, p_{1}(w), p_{2}(w)), \; w\in \left[\frac{1}{2}, \frac{7}{2}\right], \\ {}^{H}D^{\frac{2}{3}, \frac{3}{5}; \frac{3}{7}; \log w} \bigg[\frac{ {}^{H}D^{\frac{7}{4}, \frac{4}{5}; \frac{3}{7}; \log w}p_{1}(w)}{\Phi_{2}(w, p_{1}(w), p_{2}(w))} -\sum\limits_{j = 1}^{2}{} {}^{p}I^{\overline{\eta}_{j}, \varsigma, \psi}G_{j}(w, p_{1}(w), p_{2}(w))\bigg] = \Upsilon_{2}(w, p_{1}(w), p_{2}(w)), \; w\in \left[\frac{1}{2}, \frac{7}{2}\right], \\[0.4cm] p_{1}\left(\frac{1}{2}\right) = {}^{H}D^{\frac{5}{4}, \frac{2}{5}; \frac{3}{7}; \log w}p_{1}\left(\frac{1}{2}\right) = 0, \; \; p_{1}\left(\frac{7}{2}\right) = \frac{2}{5}p_{2}\left(\frac{3}{2}\right), \\[0.4cm] p_{2}\left(\frac{1}{2}\right) = {}^{H}D^{\frac{7}{4}, \frac{4}{5}; \frac{3}{7}; \log w}p_{2}\left(\frac{1}{2}\right) = 0, \; \; p_{2}\left(\frac{7}{2}\right) = \frac{2}{3}p_{1}\left(\frac{5}{2}\right), \end{cases} \end{equation} (4.1)

    where

    \begin{eqnarray*} \sum\limits_{i = 1}^{2}{^p}I^{\eta_{i}, \varsigma, \psi}H_{i}(w, p_{1}, p_{2})& = & \sum\limits_{i = 1}^{2}{^p}I^{\frac{2(i+1)}{5}, \frac{3}{7}, \log w}\left(\frac{|p_1|}{(w+i^2)(i+|p_1|)}+\frac{|p_2|}{(w+i^3)(i+|p_2|)}\right), \\ \sum\limits_{j = 1}^{2} {^p}I^{\overline{\eta}_{j}, \varsigma, \psi}G_{j}(w, p_{1}, p_{2})& = &\sum\limits_{j = 1}^{2}{^p}I^{\frac{2(j+1)}{7}, \frac{3}{7}, \log w}\left(\frac{|p_1|}{(w^2+j^2)(j+|p_1|)}+\frac{|p_2|}{(w^2+j^3)(j+|p_2|)}\right), \\ \Phi_{1}(w, p_{1}, p_{2})& = & \frac{1}{100(10w+255)}\left(\frac{|p_1|}{1+|p_1|}+\frac{|p_2|}{1+|p_2|}+\frac{1}{2}\right), \\ \Phi_{2}(w, p_{1}, p_{2})& = & \frac{2}{5(2w+99)^2}\left(\frac{|p_1|}{1+|p_1|}+\frac{|p_2|}{1+|p_2|}+\frac{1}{4}\right), \\ \Upsilon_{1}(w, p_{1}, p_{2})& = &\frac{1}{\sqrt{w}+2}\left(\frac{|p_1|}{3+|p_1|}\right)+\frac{1}{2(\sqrt{w}+1)}\sin|p_2|+\frac{1}{3}, \\ \Upsilon_{2}(w, p_{1}, p_{2})& = &\frac{1}{w^2+4}\left(\frac{1}{2}\tan^{-1}|p_1|+\frac{|p_2|}{2+|p_2|}\right)+\frac{1}{5}. \end{eqnarray*}

    Next, we can choose \nu_{1} = 1/3 , \nu_{2} = 5/4 , \nu_{3} = 2/3 , \nu_{4} = 7/4 , \vartheta_{1} = 1/5 , \vartheta_{2} = 2/5 , \vartheta_{3} = 3/5 , \vartheta_{4} = 4/5 , \varsigma = 3/7 , \psi(w): = \log w = \log_e w , t_1 = 1/2 , t_2 = 7/2 , \theta_{1} = 2/5 , and \theta_{2} = 2/3 . Then, we have \gamma_1 = 7/15 , \gamma_{2} = 31/20 , \gamma_{3} = 13/15 , \gamma_{4} = 39/20 , M_1\approx0.1930945138 , M_2\approx0.2307306625 , N_1\approx0.1816223751 , N_2\approx0.3208292984 , and \Theta\approx0.02004452646 . Now, we analyse the nonlinear functions in the fractional integral terms. We have

    \begin{equation*} | H_{i}(w, p_{1}, p_{2})-H_{i}(w, \overline{p}_{1}, \overline{p}_{2})| \leq \frac{1}{i(w+i^2)} \big(| p_{1}- \overline{p}_{1}| + | p_{2} - \overline{p}_{2}|\big) \end{equation*}

    and

    \begin{equation*} | G_{j}(w, p_{1}, p_{2})-G_{j}(w, \overline{p}_{1}, \overline{p}_{2})| \leq \frac{1}{j(w^2+j^2)} \big(| p_{1}- \overline{p}_{1}| + | p_{2} - \overline{p}_{2}|\big), \end{equation*}

    from which h_i(w) = 1/(i(w+i^2)) and g_j(w) = 1/(j(w^2+j^2)) , respectively. Both of them are bounded as

    \begin{equation*} |H_{i}(w, p_{1}, p_{2})|\leq \frac{2}{w+i^2}\quad\text{and}\quad|G_{j}(w, p_{1}, p_{2})|\leq \frac{2}{w^2+j^2}. \end{equation*}

    Therefore \lambda_i(w) = 2/(w+i^2) and \mu_{j} = 2/(w^2+j^2) . Moreover, we have

    \sum\limits_{i = 1}^{n}\|h_i\|\frac{(\psi(t_2)-\psi(t_1))^{\eta_{i}}}{\varsigma^{\eta_{i}}\Gamma(\eta_{i}+1)}\approx 3.021061781,
    \sum\limits_{i = 1}^{n}\|\lambda_i\|\frac{(\psi(t_2)-\psi(t_1))^{\eta_{i}}}{\varsigma^{\eta_{i}}\Gamma(\eta_{i}+1)}\approx 7.281499952,
    \sum\limits_{j = 1}^{m}\|g_j\|\frac{(\psi(t_2)-\psi(t_1))^{\overline{\eta}_{j}}}{\varsigma^{\overline{\eta}_{j}}\Gamma(\overline{\eta}_{i}+1)}\approx 2.776491121

    and

    \sum\limits_{j = 1}^{m}\|\mu_j\|\frac{(\psi(t_2)-\psi(t_1))^{\overline{\eta}_{j}}}{\varsigma^{\overline{\eta}_{j}}\Gamma(\overline{\eta}_{i}+1)}\approx 7.220966978.

    For the two non-zero functions \Phi_1 and \Phi_2 we have

    \begin{eqnarray*} | \Phi_{1}(w, p_{1}, p_{2})-\Phi_{1}(w, \overline{p}_{1}, \overline{p}_{2})|&\leq& \frac{1}{100(10w+255)}\left(|p_1-\overline{p}_{1}|+|p_2-\overline{p}_{2}|\right), \\ | \Phi_{2}(w, p_{1}, p_{2})-\Phi_{2}(w, \overline{p}_{1}, \overline{p}_{2})|&\leq& \frac{2}{5(2w+99)^2}\left(|p_1-\overline{p}_{1}|+|p_2-\overline{p}_{2}|\right), \end{eqnarray*}
    \begin{equation*} |\Phi_{1}(w, p_{1}, p_{2})|\leq \frac{1}{40(10w+255)}, \quad\text{and}\quad |\Phi_{2}(w, p_{1}, p_{2})|\leq \frac{9}{10(2w+99)^2}, \end{equation*}

    from which we get \|\phi_1\| = 1/26000 , \|\phi_{2}\| = 1/25000 , \|F_1\| = 1/10400 , \|F_2\| = 9/100000, by setting \phi_{1}(w) = 1/(100(10w+255)) , \phi_{2}(w) = 2/(5(2w+99)^2) , F_1(w) = 1/(40(10w+255)), and F_2(w) = 9/(10(2w+99)^2) , respectively.

    Finally, for the nonlinear functions of the right sides in problem (4.1) we have

    \begin{eqnarray*} |\Upsilon_{1}(w, p_{1}, p_{2})-\Upsilon_{1}(w, \overline{p}_{1}, \overline{p}_{2})|&\leq&\frac{1}{2(\sqrt{w}+1)}\left(|p_1-\overline{p}_1|+|p_2-\overline{p}_2|\right), \\ |\Upsilon_{2}(w, p_{1}, p_{2})-\Upsilon_{2}(w, \overline{p}_{1}, \overline{p}_{2})|&\leq&\frac{1}{2(w^2+4)}\left(|p_1-\overline{p}_1|+|p_2-\overline{p}_2|\right), \end{eqnarray*}

    which give \omega_1(w) = 1/(2(\sqrt{w}+1)) , \omega_2(w) = 1/(2(w^2+4)) and

    \begin{equation*} |\Upsilon_{1}(w, p_{1}, p_{2})|\leq \frac{1}{\sqrt{w}+2}+\frac{1}{2(\sqrt{w}+1)}+\frac{1}{3}: = L_1(w), \end{equation*}

    and

    \begin{equation*} |\Upsilon_{2}(w, p_{1}, p_{2})|\leq \frac{1}{w^2+4}\left(\frac{\pi}{4}+1\right)+\frac{1}{5}: = L_2(w). \end{equation*}

    Therefore, using all of the information to compute a constant K in assumption (H_3) of Theorem 3.2, we obtain

    \begin{equation*} K\approx 0.9229566975 < 1. \end{equation*}

    Hence, the given coupled system of nonlinear proportional Hilfer-type fractional differential equations with multi-point boundary conditions (4.1), satisfies all assumptions in Theorem 3.2. Then, by its conclusion, there exists at least one solution (p_1, p_2)(w) to the problem (4.1) where w\in[1/2, 7/2] .

    In this paper, we have presented the existence result for a new class of coupled systems of \psi -Hilfer proportional sequential fractional differential equations with multi-point boundary conditions. The proof of the existence result was based on a generalization of Krasnosel'ski\breve{{\rm{i}}}'s fixed-point theorem due to Burton. An example was presented to illustrate our main result. Some special cases were also discussed. In future work, we can implement these techniques on different boundary value problems equipped with complicated integral multi-point boundary conditions.

    The authors declare that they have not used artificial intelligence (AI) tools in the creation of this article.

    This research was funded by the National Science, Research and Innovation Fund (NSRF) and King Mongkut's University of Technology North Bangkok with contract no. KMUTNB-FF-66-11.

    Professor Sotiris K. Ntouyas is an editorial board member for AIMS Mathematics and was not involved in the editorial review or the decision to publish this article. The authors declare no conflicts of interest.



    [1] Z. Raizah, U. K. K. Nanjappa, H. U. A. Shankar, U. Khan, S. M. Eldin, R. Kumar, et al., Windmill global sourcing in an initiative using a spherical fuzzy multiple-criteria decision prototype, Energies, 15 (2022), 8000. https://doi.org/10.3390/en15218000 doi: 10.3390/en15218000
    [2] M. M. Lashin, M. I. Khan, N. B. Khedher, S. M. Eldin, Optimization of display window design for females clothes for fashion stores through artificial intelligence and fuzzy system, Appl. Sci., 12 (2022), 11594. https://doi.org/10.3390/app122211594 doi: 10.3390/app122211594
    [3] G. Shahzadi, F. Zafar, M. A. Alghamdi, Multiple-attribute decision-making using Fermatean fuzzy Hamacher interactive geometric operators, Math. Prob. Eng., 2021, 1–20. https://doi.org/10.1155/2021/5150933 doi: 10.1155/2021/5150933
    [4] S. Ashraf, S. N. Abbasi, M. Naeem, S. M. Eldin, Novel decision aid model for green supplier selection based on extended EDAS approach under pythagorean fuzzy Z-numbers, Front. Env. Sci., 11 (2023), 342. https://doi.org/10.3389/fenvs.2023.1137689 doi: 10.3389/fenvs.2023.1137689
    [5] Attaullah, S. Ashraf, N. Rehman, A. Khan, M. Naeem, C. Park, A wind power plant site selection algorithm based on q-rung orthopair hesitant fuzzy rough Einstein aggregation information, Sci. Rep., 12 (2022), 5443. https://doi.org/10.1038/s41598-022-09323-5 doi: 10.1038/s41598-022-09323-5
    [6] Attaullah, S. Ashraf, N. Rehman, H. AlSalman, A. H. Gumaei, A decision-making framework using q-rung orthopair probabilistic hesitant fuzzy rough aggregation information for the drug selection to treat COVID-19, Complexity, 2022. https://doi.org/10.1155/2022/5556309 doi: 10.1155/2022/5556309
    [7] Attaullah, S. Ashraf, N. Rehman, A. Khan, C. Park, A decision making algorithm for wind power plant based on q-rung orthopair hesitant fuzzy rough aggregation information and TOPSIS, AIMS Math., 7 (2022), 5241–5274. https://doi.org/10.3934/math.2022292 doi: 10.3934/math.2022292
    [8] Attaullah, N. Rehman, A. Khan, G. Santos-Garcia, Fermatean hesitant fuzzy rough aggregation operators and their applications in multiple criteria group decision-making, Sci. Rep., 13 (2023), 6676. https://doi.org/10.1038/s41598-023-28722-w doi: 10.1038/s41598-023-28722-w
    [9] V. Nevrlý, R. Šomplák, O. Putna, M. Pavlas, Location of mixed municipal waste treatment facilities: Cost of reducing greenhouse gas emissions, J. Clean. Prod., 239 (2019), 118003. https://doi.org/10.1016/j.jclepro.2019.118003 doi: 10.1016/j.jclepro.2019.118003
    [10] A. Kumar, S. R. Samadder, A review on technological options of waste to energy for effective management of municipal solid waste, Waste Manage., 69 (2017), 407–422. https://doi.org/10.1016/j.wasman.2017.08.046 doi: 10.1016/j.wasman.2017.08.046
    [11] J. Song, Y. Sun, L. Jin, PESTEL analysis of the development of the waste-to-energy incineration industry in China, Renew. Sust. Energ. Rev., 80 (2017), 276–289. https://doi.org/10.1016/j.rser.2017.05.066 doi: 10.1016/j.rser.2017.05.066
    [12] H. Jiang, J. Zhan, D. Chen, PROMETHEE-Ⅱ method based on variable precision fuzzy rough sets with fuzzy neighborhoods, Artif. Intell. Rev., 54 (2021), 1281–1319. https://doi.org/10.1007/s10462-020-09878-7 doi: 10.1007/s10462-020-09878-7
    [13] L. A. Zadeh, Fuzzy sets, Inform. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
    [14] K. T. Atanassov, Intuitionistic fuzzy sets, In: Intuitionistic fuzzy sets, 1999, 1–137, Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1870-3_1
    [15] S. Singh, S. Sharma, S. Lalotra, Generalized correlation coefficients of intuitionistic fuzzy sets with application to MAGDM and clustering analysis, Int. J. Fuzzy Syst., 22 (2020), 1582–1595. https://doi.org/10.3390/e23050563 doi: 10.3390/e23050563
    [16] R. R. Yager, A. M. Abbasov, Pythagorean membership grades, complex numbers, and decision making, Int. J. Intell. Syst., 28 (2013), 436–452. https://doi.org/10.1002/int.21584 doi: 10.1002/int.21584
    [17] O. Yanmaz, Y. Turgut, E. N. Can, C. Kahraman, Interval-valued Pythagorean fuzzy EDAS method: An application to car selection problem, J. Intell. Fuzzy Syst., 38 (2020), 4061–4077. https://doi.org/10.3233/JIFS-182667 doi: 10.3233/JIFS-182667
    [18] R. R. Yager, Generalized orthopair fuzzy sets, IEEE T. Fuzzy Syst., 25 (2016), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005 doi: 10.1109/TFUZZ.2016.2604005
    [19] C. Zhang, J. Ding, D. Li, J. Zhan, A novel multi-granularity three-way decision making approach in q-rung orthopair fuzzy information systems, Int. J. Approx. Reason., 138 (2021), 161–187. https://doi.org/10.1016/j.ijar.2021.08.004 doi: 10.1016/j.ijar.2021.08.004
    [20] C. Zhang, J. Ding, J. Zhan, A. K. Sangaiah, D. Li, Fuzzy intelligence learning based on bounded rationality in IoMT systems: A case study in Parkinson Disease, IEEE T. Comput. Soc. Syst., 2022. https://doi.org/10.1109/TCSS.2022.3221933 doi: 10.1109/TCSS.2022.3221933
    [21] C. Zhang, W. Bai, D. Li, J. Zhan, Multiple attribute group decision making based on multigranulation probabilistic models, MULTIMOORA and TPOP in incomplete q-rung orthopair fuzzy information systems, Int. J. Approx. Reason., 143 (2022), 102–120. https://doi.org/10.1016/j.ijar.2022.01.002 doi: 10.1016/j.ijar.2022.01.002
    [22] A. Hussain, M. I. Ali, T. Mahmood, Covering based q-rung orthopair fuzzy rough set model hybrid with TOPSIS for multi-attribute decision making, J. Intell. Fuzzy Syst., 37 (2019), 981–993. https://doi.org/10.3233/JIFS-181832 doi: 10.3233/JIFS-181832
    [23] S. Chakraborty, TOPSIS and modified TOPSIS: A comparative analysis, Decis. Anal. J., 2 (2022), 100021. https://doi.org/10.1016/j.dajour.2021.100021 doi: 10.1016/j.dajour.2021.100021
    [24] M. Hanine, O. Boutkhoum, A. Tikniouine, T. Agouti, Application of an integrated multi-criteria decision making AHP-TOPSIS methodology for ETL software selection, SpringerPlus, 5 (2016), 1–17. https://doi.org/10.1186/s40064-016-2233-2 doi: 10.1186/s40064-016-2233-2
    [25] P. Gupta, M. K. Mehlawat, N. Grover, Intuitionistic fuzzy multi-attribute group decision-making with an application to plant location selection based on a new extended VIKOR method, Inform. Sci., 370 (2016), 184–203. https://doi.org/10.1016/j.ins.2016.07.058 doi: 10.1016/j.ins.2016.07.058
    [26] A. Hafezalkotob, A. Hafezalkotob, Interval target-based VIKOR method supported on interval distance and preference degree for machine selection, Eng. Appl. Artif. Intel., 57 (2017), 184–196. https://doi.org/10.1016/j.engappai.2016.10.018 doi: 10.1016/j.engappai.2016.10.018
    [27] O. Soner, E. Celik, E. Akyuz, Application of AHP and VIKOR methods under interval type 2 fuzzy environment in maritime transportation, Ocean Eng., 129 (2017), 107–116. https://doi.org/10.3390/math11102249 doi: 10.3390/math11102249
    [28] M. Gul, E. Celik, N. Aydin, A. T. Gumus, A. F. Guneri, A state of the art literature review of VIKOR and its fuzzy extensions on applications, Appl. Soft Comput., 46 (2016), 60–89. https://doi.org/10.1016/j.asoc.2016.04.040 doi: 10.1016/j.asoc.2016.04.040
    [29] S. Opricovic, G. H. Tzeng, Extended VIKOR method in comparison with outranking methods, Eur. J. Oper. Res., 178 (2007), 514–529. https://doi.org/10.1016/j.ejor.2006.01.020 doi: 10.1016/j.ejor.2006.01.020
    [30] Z. Pawlak, Rough sets, Int. J. Comput. Inform. Sci., 11 (1982), 341–356. https://doi.org/10.1007/BF01001956 doi: 10.1016/j.ins.2021.04.016
    [31] T. M. Al-shami, An improvement of rough sets accuracy measure using containment neighborhoods with a medical application, Inform. Sci., 569 (2021), 110–124. https://doi.org/10.1016/j.ins.2021.04.016 doi: 10.1016/j.ins.2021.04.016
    [32] Z. Pawlak, A. Skowron, Rudiments of rough sets, Inform. Sci., 177 (2007), 3–27. https://doi.org/10.1016/j.ins.2006.06.003 doi: 10.1016/j.ins.2006.06.003
    [33] S. Sadek, M. El-Fadel, F. Freiha, Compliance factors within a GIS-based framework for landfill siting, Int. J. Env. Stud., 63 (2006), 71–86. https://doi.org/10.1080/00207230600562213 doi: 10.1080/00207230600562213
    [34] A. M. Radzikowska, E. E. Kerre, A comparative study of fuzzy rough sets, Fuzzy Set. Syst., 126 (2002), 137–155. https://doi.org/10.1016/S0165-0114(01)00032-X doi: 10.1016/S0165-0114(01)00032-X
    [35] W. Pan, K. She, P. Wei, Multi-granulation fuzzy preference relation rough set for ordinal decision system, Fuzzy Set. Syst., 312 (2017), 87–108. https://doi.org/10.1016/j.fss.2016.08.002 doi: 10.1016/j.fss.2016.08.002
    [36] Y. Li, S. Wu, Y. Lin, J. Liu, Different classes' ratio fuzzy rough set based robust feature selection, Knowl.-Based Syst., 120 (2017), 74–86. https://doi.org/10.1155/2021/6685396 doi: 10.1155/2021/6685396
    [37] T. Zhan, Granular-based state estimation for nonlinear fractional control systems and its circuit cognitive application, Int. J. Cogn. Comput. Eng., 4 (2023), 1–5. https://doi.org/10.1016/j.ijcce.2022.12.001 doi: 10.1016/j.ijcce.2022.12.001
    [38] X. Ren, D. Li, Y. Zhai, Research on mixed decision implications based on formal concept analysis, Int. J. Cogn. Comput. Eng., 4 (2023), 71–77. https://doi.org/10.1016/j.ijcce.2023.02.007 doi: 10.1016/j.ijcce.2023.02.007
    [39] K. Lian, T. Wang, B. Wang, M. Wang, W. Huang, J. Yang, The research on relative knowledge distances and their cognitive features, Int. J. Cogn. Comput. Eng., 2023. https://doi.org/10.1016/j.ijcce.2023.03.004 doi: 10.1016/j.ijcce.2023.03.004
    [40] T. Feng, H. T. Fan, J. S. Mi, Uncertainty and reduction of variable precision multigranulation fuzzy rough sets based on three-way decisions, Int. J. Approx. Reason., 85 (2017), 36–58. https://doi.org/10.1016/j.ins.2022.05.122 doi: 10.1016/j.ins.2022.05.122
    [41] B. Sun, W. Ma, X. Chen, X. Zhang, Multigranulation vague rough set over two universes and its application to group decision making, Soft Comput., 23 (2019), 8927–8956. https://doi.org/10.1007/s00500-018-3494-1 doi: 10.1007/s00500-018-3494-1
    [42] C. Y. Wang, B. Q. Hu, Granular variable precision fuzzy rough sets with general fuzzy relations, Fuzzy Set. Syst., 275 (2015), 39–57. https://doi.org/10.1007/s40314-023-02245-6 doi: 10.1007/s40314-023-02245-6
    [43] S. Vluymans, D. S. Tarragó, Y. Saeys, C. Cornelis, F. Herrera, Fuzzy rough classifiers for class imbalanced multi-instance data, Pattern Recog., 53 (2016), 36–45. https://doi.org/10.1016/j.patcog.2015.12.002 doi: 10.1016/j.patcog.2015.12.002
    [44] C. Y. Wang, B. Q. Hu, Fuzzy rough sets based on generalized residuated lattices, Inform. Sci., 248 (2013), 31–49. https://doi.org/10.1016/j.ins.2013.03.051 doi: 10.1016/j.ins.2013.03.051
    [45] H. Zhang, L. Shu, S. Liao, C. Xiawu, Dual hesitant fuzzy rough set and its application, Soft Comput., 21 (2017), 3287–3305. https://doi.org/10.1007/s00500-015-2008-7 doi: 10.1007/s00500-015-2008-7
    [46] D. Peng, J. Wang, D. Liu, Y. Cheng, The interactive fuzzy linguistic term set and its application in multi-attribute decision making, Artif. Intell. Medicine, 131 (2022), 102345. https://doi.org/10.1016/j.artmed.2022.102345 doi: 10.1016/j.artmed.2022.102345
    [47] D. Peng, J. Wang, D. Liu, Z. Liu, An improved EDAS method for the multi-attribute decision making based on the dynamic expectation level of decision makers, Symmetry, 14 (2022), 979. https://doi.org/10.3390/sym14050979 doi: 10.3390/sym14050979
    [48] G. Tang, F. Chiclana, P. Liu, A decision-theoretic rough set model with q-rung orthopair fuzzy information and its application in stock investment evaluation, Appl. Soft Comput., 91 (2020), 106212. https://doi.org/10.1016/j.asoc.2020.106212 doi: 10.1016/j.asoc.2020.106212
    [49] D. Liang, W. Cao, q-Rung orthopair fuzzy sets-based decision-theoretic rough sets for three-way decisions under group decision making, Int. J. Intell. Syst., 34 (2019), 3139–3167. https://doi.org/10.1002/int.22187 doi: 10.1002/int.22187
    [50] Z. Zhang, S. M. Chen, Group decision making with incomplete q-rung orthopair fuzzy preference relations, Inform. Sci., 553 (2021), 376–396.
    [51] D. Peng, J. Wang, D. Liu, Z. Liu, The similarity measures for linguistic q-rung orthopair fuzzy multi-criteria group decision making using projection method, IEEE Access, 7 (2019), 176732–176745. https://doi.org/10.1109/ACCESS.2019.2957916 doi: 10.1109/ACCESS.2019.2957916
    [52] K. Charnpratheep, Q. Zhou, B. Garner, Preliminary landfill site screening using fuzzy geographical information systems, Waste Manag. Res., 15 (1997), 197–215. https://doi.org/10.1177/0734242X9701500207 doi: 10.1177/0734242X9701500207
    [53] O. E. Demesouka, A. P Vavatsikos, K. P. Anagnostopoulos, Suitability analysis for siting MSW landfills and its multicriteria spatial decision support system: Method, implementation and case study, Waste Manage., 33 (2013), 1190–1206. https://doi.org/10.1016/j.wasman.2013.01.030 doi: 10.1016/j.wasman.2013.01.030
    [54] M. Ekmekçioǧlu, T. Kaya, C. Kahraman, Fuzzy multicriteria disposal method and site selection for municipal solid waste, Waste Manage., 30 (2010), 1729–1736. https://doi.org/10.1016/j.wasman.2010.02.031 doi: 10.1016/j.wasman.2010.02.031
    [55] S. Ashraf, N. Rehman, A. Hussain, H. AlSalman, A. H. Gumaei, q-Rung orthopair fuzzy 380 rough Einstein aggregation information-based EDAS method: Applications in robotic agrifarming, Comput. Intell. Neurosci., 2021. https://doi.org/10.1155/2021/5520264 doi: 10.1155/2021/5520264
    [56] P. F. Hsu, M. G. Hsu, Optimizing the information outsourcing practices of primary care medical organizations using entropy and TOPSIS, Qual. Quant., 42 (2008), 181–201. https://doi.org/10.1007/s11135-006-9040-8 doi: 10.1007/s11135-006-9040-8
    [57] C. L. Hwang, K. Yoon, Methods for multiple attribute decision making, In: Multiple attribute decision making, 1981, 58–191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-48318-9_3
    [58] D. Liu, D. Peng, Z. Liu, The distance measures between q-rung orthopair hesitant fuzzy sets and their application in multiple criteria decision making, Int. J. Intell. Syst., 34 (2019), 2104–2121. https://doi.org/10.1002/int.22133 doi: 10.1002/int.22133
    [59] G. H. Tzeng, J. J. Huang, Multiple attribute decision making: Methods and applications, CRC Press, 2011. https://doi.org/10.1201/b11032
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