In today's supply chain management, there is a growing emphasis on transitioning to environmentally sustainable practices. This paper aimed to identify and rank the barriers to the implementation of eco-regenerative supply chains. A novel integrated approach was proposed based on stepwise weighted assessment ratio analysis (SWARA) and the multi-attributive border approximation area (MABAC) method using ZE-fuzzy numbers. This approach aimed to address some of the limitations of the failure mode and effects analysis (FMEA) method, including lack of thorough prioritization and inability to make decisions about the importance of various failure factors in an uncertain environment. By combining fuzzy sets and considering the reliability levels of two distinct groups of decision-makers and experts, this proposed method offers a comprehensive evaluation framework. Following the determination of the risk priority number (RPN) by the FMEA method, risk factors were evaluated using ZE-SWARA, and barriers were ranked using the ZE-MABAC method to identify critical barriers and propose corrective actions. Furthermore, sensitivity analysis was conducted in this study to demonstrate the viability of the proposed method. This research contributes to the advancement of eco-regenerative supply chain management practices by offering a systematic and innovative approach to addressing environmental concerns and improving decision-making processes in uncertain environments.
Citation: Zeynab Rezazadeh Salteh, Saeed Fazayeli, Saeid Jafarzadeh Ghoushchi. Evaluation and prioritization of barriers to the implementation of the eco-regenerative supply chains using fuzzy ZE-numbers framework in group decision-making[J]. AIMS Environmental Science, 2024, 11(4): 516-550. doi: 10.3934/environsci.2024026
[1] | Ali Ahmad, Humera Rashid, Hamdan Alshehri, Muhammad Kamran Jamil, Haitham Assiri . Randić energies in decision making for human trafficking by interval-valued T-spherical fuzzy Hamacher graphs. AIMS Mathematics, 2025, 10(4): 9697-9747. doi: 10.3934/math.2025446 |
[2] | Doaa Al-Sharoa . (α1, 2, β1, 2)-complex intuitionistic fuzzy subgroups and its algebraic structure. AIMS Mathematics, 2023, 8(4): 8082-8116. doi: 10.3934/math.2023409 |
[3] | Tahir Mahmood, Ubaid Ur Rehman, Muhammad Naeem . A novel approach towards Heronian mean operators in multiple attribute decision making under the environment of bipolar complex fuzzy information. AIMS Mathematics, 2023, 8(1): 1848-1870. doi: 10.3934/math.2023095 |
[4] | Jun Jiang, Junjie Lv, Muhammad Bilal Khan . Visual analysis of knowledge graph based on fuzzy sets in Chinese martial arts routines. AIMS Mathematics, 2023, 8(8): 18491-18511. doi: 10.3934/math.2023940 |
[5] | Tareq M. Al-shami, José Carlos R. Alcantud, Abdelwaheb Mhemdi . New generalization of fuzzy soft sets: (a,b)-Fuzzy soft sets. AIMS Mathematics, 2023, 8(2): 2995-3025. doi: 10.3934/math.2023155 |
[6] | Muhammad Qiyas, Muhammad Naeem, Saleem Abdullah, Neelam Khan . Decision support system based on complex T-Spherical fuzzy power aggregation operators. AIMS Mathematics, 2022, 7(9): 16171-16207. doi: 10.3934/math.2022884 |
[7] | Shichao Li, Zeeshan Ali, Peide Liu . Prioritized Hamy mean operators based on Dombi t-norm and t-conorm for the complex interval-valued Atanassov-Intuitionistic fuzzy sets and their applications in strategic decision-making problems. AIMS Mathematics, 2025, 10(3): 6589-6635. doi: 10.3934/math.2025302 |
[8] | Tahir Mahmood, Azam, Ubaid ur Rehman, Jabbar Ahmmad . Prioritization and selection of operating system by employing geometric aggregation operators based on Aczel-Alsina t-norm and t-conorm in the environment of bipolar complex fuzzy set. AIMS Mathematics, 2023, 8(10): 25220-25248. doi: 10.3934/math.20231286 |
[9] | Atiqe Ur Rahman, Muhammad Saeed, Mazin Abed Mohammed, Alaa S Al-Waisy, Seifedine Kadry, Jungeun Kim . An innovative fuzzy parameterized MADM approach to site selection for dam construction based on sv-complex neutrosophic hypersoft set. AIMS Mathematics, 2023, 8(2): 4907-4929. doi: 10.3934/math.2023245 |
[10] | Muhammad Arshad, Muhammad Saeed, Khuram Ali Khan, Nehad Ali Shah, Wajaree Weera, Jae Dong Chung . A robust MADM-approach to recruitment-based pattern recognition by using similarity measures of interval-valued fuzzy hypersoft set. AIMS Mathematics, 2023, 8(5): 12321-12341. doi: 10.3934/math.2023620 |
In today's supply chain management, there is a growing emphasis on transitioning to environmentally sustainable practices. This paper aimed to identify and rank the barriers to the implementation of eco-regenerative supply chains. A novel integrated approach was proposed based on stepwise weighted assessment ratio analysis (SWARA) and the multi-attributive border approximation area (MABAC) method using ZE-fuzzy numbers. This approach aimed to address some of the limitations of the failure mode and effects analysis (FMEA) method, including lack of thorough prioritization and inability to make decisions about the importance of various failure factors in an uncertain environment. By combining fuzzy sets and considering the reliability levels of two distinct groups of decision-makers and experts, this proposed method offers a comprehensive evaluation framework. Following the determination of the risk priority number (RPN) by the FMEA method, risk factors were evaluated using ZE-SWARA, and barriers were ranked using the ZE-MABAC method to identify critical barriers and propose corrective actions. Furthermore, sensitivity analysis was conducted in this study to demonstrate the viability of the proposed method. This research contributes to the advancement of eco-regenerative supply chain management practices by offering a systematic and innovative approach to addressing environmental concerns and improving decision-making processes in uncertain environments.
Let Ω={z∈Rn:R1<|z|<R2,R1,R2>0}. In this work we study the existence of positive radial solutions for the following system of boundary value problems with semipositone second order elliptic equations:
{Δφ+k(|z|)f(φ,ϕ)=0, z∈Ω,Δϕ+k(|z|)g(φ,ϕ)=0, z∈Ω,αφ+β∂φ∂n=0, αϕ+β∂ϕ∂n=0, |z|=R1,γφ+δ∂φ∂n=0, γϕ+δ∂ϕ∂n=0, |z|=R2, | (1.1) |
where α,β,γ,δ,k,f,g satisfy the conditions:
(H1) α,β,γ,δ≥0 with ρ≡γβ+αγ+αδ>0;
(H2) k∈C([R1,R2],R+), and k is not vanishing on [R1,R2];
(H3) f,g∈C(R+×R+,R), and there is a positive constant M such that
f(u,v),g(u,v)≥−M, ∀u,v∈R+. |
Elliptic equations have attracted a lot of attention in the literature since they are closely related to many mathematical and physical problems, for instance, incineration theory of gases, solid state physics, electrostatic field problems, variational methods and optimal control. The existence of solutions for this type of equation in annular domains has been discussed in the literature, see for example, [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18] and the references therein. In [1] the authors used the fixed point index to study positive solutions for the elliptic system:
{Δu+a(|x|)f(u,v)=0,Δv+b(|x|)g(u,v)=0, |
with one of the following boundary conditions
u=v=0,|x|=R1,|x|=R2,u=v=0,|x|=R1,∂u∂r=∂v∂r=0,|x|=R2,∂u∂r=∂v∂r=0,|x|=R1,u=v=0,|x|=R2. |
In [2] the authors used the method of upper and lower solutions to establish the existence of positive radial solutions for the elliptic equation
{−Δu=f(|x|,u,|∇u|), x∈Ω,u|∂Ω=0, |
where Ω={x∈RN: |x|<1},N≥2, and f:[0,1]×R+×R+→R is a continuous function.
However, we note that in most of the papers on nonlinear differential equations the nonlinear term is usually assumed to be nonnegative. In recent years boundary value problems for semipositone equations (f(t,x)≥−M,M>0) has received some attention (see [19,20,21,22,23,24,25,26,27,28,29,30,31,32]), and these equations describe and solve many natural phenomena in engineering and technical problems in real life, for example in mechanical systems, suspension bridge design, astrophysics and combustion theoretical models. In [19] the authors used a fixed point theorem to study the system for HIV-1 population dynamics in the fractional sense
{Dα0+u(t)+λf(t,u(t),Dβ0+u(t),v(t))=0,t∈(0,1),Dγ0+v(t)+λg(t,u(t))=0,t∈(0,1),Dβ0+u(0)=Dβ+10+u(0)=0,Dβ0+u(1)=∫10Dβ0+u(s)dA(s),v(0)=v′(0)=0,v(1)=∫10v(s)dB(s), |
where Dα0+,Dβ0+,Dγ0+ are the standard Riemann-Liouville derivatives, and f, g are two semipositone nonlinearities. In [28] the authors used the nonlinear alternative of Leray-Schauder type and the Guo-Krasnosel'skii fixed point theorem to study the existence of positive solutions for a system of nonlinear Riemann-Liouville fractional differential equations
{Dα0+u(t)+λf(t,v(t))=0,0<t<1, λ>0,Dα0+v(t)+λg(t,u(t))=0,0<t<1, λ>0,u(j)(0)=v(j)(0)=0,0⩽j⩽n−2,u(1)=μ∫10u(s)ds,v(1)=μ∫10v(s)ds, |
where f,g satisfy some superlinear or sublinear conditions:
(HZ)1 There exist M>0 such that lim supz→0g(t,z)z<M uniformly for t∈[0,1] (sublinear growth condition).
(HZ)2 There exists [θ1,θ2]⊂(0,1) such that lim infz→+∞f(t,z)z=+∞ and lim infz→+∞g(t,z)z=+∞ uniformly for t∈[θ1,θ2] (superlinear growth condition).
Inspired by the aforementioned work, in particular [31,32,33,34], we study positive radial solutions for (1.1) when the nonlinearities f,g satisfy the semipositone condition (H3). Moreover, some appropriate concave and convex functions are utilized to characterize coupling behaviors of our nonlinearities. Note that our conditions (H4) and (H6) (see Section 3) are more general than that in (HZ)1 and (HZ)2.
Using the methods in [1,4], we transform (1.1) into a system of ordinary differential equations involving Sturm-Liouville boundary conditions. Let φ=φ(r),ϕ=ϕ(r),r=|z|=√n∑i=1z2i. Then (1.1) can be expressed by the following system of ordinary differential equations:
{φ′′(r)+n−1rφ′(r)+k(r)f(φ(r),ϕ(r))=0, R1<r<R2,ϕ′′(r)+n−1rϕ′(r)+k(r)g(φ(r),ϕ(r))=0, R1<r<R2,αφ(R1)−βφ′(R1)=0, γφ(R2)+δφ′(R2)=0,αϕ(R1)−βϕ′(R1)=0, γϕ(R2)+δϕ′(R2)=0. | (2.1) |
Then if we let s=−∫R2r(1/tn−1)dt,t=(m−s)/m,m=−∫R2R1(1/tn−1)dt, (2.1) can be transformed into the system
{φ′′(t)+h(t)f(φ(t),ϕ(t))=0,0<t<1,ϕ′′(t)+h(t)g(φ(t),ϕ(t))=0,0<t<1,αφ(0)−βφ′(0)=0,γφ(1)+δφ′(1)=0,αϕ(0)−βϕ′(0)=0,γϕ(1)+δϕ′(1)=0, | (2.2) |
where h(t)=m2r2(n−1)(m(1−t))k(r(m(1−t))). Consequently, (2.2) is equivalent to the following system of integral equations
{φ(t)=∫10G(t,s)h(s)f(φ(s),ϕ(s))ds,ϕ(t)=∫10G(t,s)h(s)g(φ(s),ϕ(s))ds, | (2.3) |
where
G(t,s)=1ρ{(γ+δ−γt)(β+αs),0≤s≤t≤1,(γ+δ−γs)(β+αt),0≤t≤s≤1, | (2.4) |
and ρ is defined in (H1).
Lemma 2.1. Suppose that (H1) holds. Then
(i)
ρ(γ+δ)(β+α)G(t,t)G(s,s)≤G(t,s)≤G(s,s), t,s∈[0,1]; |
(ii)
G(t,s)≤G(t,t), t,s∈[0,1]. |
Proof. (i) In G(t,s), we fix the second variable s, we have
G(t,s)=1ρ{(γ+δ−γt)(β+αs)≤(γ+δ−γs)(β+αs),0≤s≤t≤1,(γ+δ−γs)(β+αt)≤(γ+δ−γs)(β+αs),0≤t≤s≤1. |
This implies that
G(t,s)≤G(s,s),t,s∈[0,1]. |
When t≥s, we have
1ρ(γ+δ−γt)(β+αs)ρ⋅1ρ⋅1ρ(γ+δ−γt)(β+αt)(γ+δ−γs)(β+αs)≥1(β+α)(γ+δ). |
When t≤s, we have
1ρ(γ+δ−γs)(β+αt)ρ⋅1ρ⋅1ρ(γ+δ−γt)(β+αt)(γ+δ−γs)(β+αs)≥1(β+α)(γ+δ). |
Combining the above we obtain
G(t,s)G(t,t)G(s,s)≥ρ(β+α)(γ+δ). |
(ii) In G(t,s) we fix the first variable t, and we obtain
G(t,s)=1ρ{(γ+δ−γt)(β+αs)≤(γ+δ−γt)(β+αt),0≤s≤t≤1,(γ+δ−γs)(β+αt)≤(γ+δ−γt)(β+αt),0≤t≤s≤1. |
Thus
G(t,s)≤G(t,t),t,s∈[0,1]. |
Lemma 2.2. Suppose that (H1) holds. Let ϑ(t)=G(t,t)h(t),t∈[0,1]. Then
κ1ϑ(s)≤∫10G(t,s)h(s)ϑ(t)dt≤κ2ϑ(s), |
where
κ1=ρ(γ+δ)(β+α)∫10G(t,t)ϑ(t)dt, κ2=∫10ϑ(t)dt. |
Proof. From (H1) and Lemma 2.1(i) we have
∫10G(t,s)h(s)ϑ(t)dt≤∫10G(s,s)h(s)ϑ(t)dt=κ2ϑ(s) |
and
∫10G(t,s)h(s)ϑ(t)dt≥∫10ρ(γ+δ)(β+α)G(t,t)G(s,s)h(s)ϑ(t)dt=κ1ϑ(s). |
Note we study (2.3) to obtain positive solutions for (1.1). However here the nonlinear terms f,g can be sign-changing (see (H3)). Therefore we study the following auxiliary problem:
u(t)=∫10G(t,s)h(s)˜f(u(s))ds, | (2.5) |
where G is in (2.4) and ˜f satisfies the condition:
(H2′) ˜f∈C(R+,R), and there exists a positive constant M such that
˜f(u)≥−M, ∀u∈R+. |
Let w(t)=M∫10G(t,s)h(s)ds,∀t∈[0,1]. Then w is a solution of the following boundary value problem:
{u′′(t)+h(t)M=0,0<t<1,αu(0)−βu′(0)=0,γu(1)+δu′(1)=0. | (2.6) |
Lemma 2.3. (i) If u∗ satisfies (2.5), then u∗+w is a solution of the equation:
u(t)=∫10G(t,s)h(s)˜F(u(s)−w(s))ds, | (2.7) |
where
˜F(u)={˜f(u)+M,u≥0,˜f(0)+M,u<0. | (2.8) |
(ii) If u∗∗ satisfies (2.7) with u∗∗(t)≥w(t),t∈[0,1], then u∗∗−w is a positive solution for (2.5).
Proof. We omit its proof since it is immediate.
Let E=C[0,1], ‖u‖=maxt∈[0,1]|u(t)|. Then (E,‖⋅‖) is a Banach space. Define a set on E as follows:
P={u∈E:u(t)≥0,∀t∈[0,1]}, |
and note P is a cone on E. Note, E2=E×E is also a Banach space with the norm: ‖(u,v)‖=‖u‖+‖v‖, and P2=P×P a cone on E2. In order to obtain positive radial solutions for (1.1), combining with (2.5)–(2.7), we define the following operator equation:
A(φ,ϕ)=(φ,ϕ), | (2.9) |
where A(φ,ϕ)=(A1,A2)(φ,ϕ), Ai(i=1,2) are
{A1(φ,ϕ)(t)=∫10G(t,s)h(s)F1(φ(s)−w(s),ϕ(s)−w(s))ds,A2(φ,ϕ)(t)=∫10G(t,s)h(s)F2(φ(s)−w(s),ϕ(s)−w(s))ds, | (2.10) |
and
F1(φ,ϕ)={f(φ,ϕ)+M,φ,ϕ≥0,f(0,ϕ)+M,φ<0,ϕ≥0,f(φ,0)+M,φ≥0,ϕ<0,f(0,0)+M,φ,ϕ<0, |
F2(φ,ϕ)={g(φ,ϕ)+M,φ,ϕ≥0,g(0,ϕ)+M,φ<0,ϕ≥0,g(φ,0)+M,φ≥0,ϕ<0,g(0,0)+M,φ,ϕ<0. |
Lemma 2.4. Define P0={φ∈P:φ(t)≥ρ(γ+δ)(β+α)G(t,t)‖φ‖,t∈[0,1]}. Then Ai(P×P)⊂P0,i=1,2.
Proof. We only prove it for A1. If φ,ϕ∈P, note the non-negativity of F1(denoted by F1(⋅,⋅)), from Lemma 2.1(i) we have
∫10ρ(γ+δ)(β+α)G(t,t)G(s,s)h(s)F1(⋅,⋅)ds≤A1(φ,ϕ)(t)≤∫10G(s,s)h(s)F1(⋅,⋅)ds. |
This implies that
A1(φ,ϕ)(t)≥∫10ρ(γ+δ)(β+α)G(t,t)G(s,s)h(s)F1(⋅,⋅)ds≥ρ(γ+δ)(β+α)G(t,t)‖A1(φ,ϕ)‖. |
Remark 2.1. (i) w(t)=M∫10G(t,s)h(s)ds∈P0;
(ii) Note (see Corollary 1.5.1 in [35]):
If k(x,y,u):˜GטG×R→R is continuous (˜G is a bounded closed domain in Rn), then K is a completely continuous operator from C(˜G) into itself, where
Kψ(x)=∫˜Gk(x,y,ψ(y))dy. |
Note that G(t,s),h(s),Fi(i=1,2) are continuous, and also Ai, A are completely continuous operators, i=1,2.
From Lemma 2.3 if there exists (φ,ϕ)∈P2∖{(0,0)} such that (2.9) holds with (φ,ϕ)≥(w,w), then φ(t),ϕ(t)≥w(t),t∈[0,1], and (φ−w,ϕ−w) is a positive solution for (2.3), i.e., we obtain positive radial solutions for (1.1). Note that φ,ϕ∈P0, and from Lemma 2.1(ii) we have
φ(t)−w(t)≥ρ(γ+δ)(β+α)G(t,t)‖φ‖−M∫10G(t,t)h(s)ds, |
ϕ(t)−w(t)≥ρ(γ+δ)(β+α)G(t,t)‖ϕ‖−M∫10G(t,t)h(s)ds. |
Hence, if
‖φ‖,‖ϕ‖≥M(γ+δ)(β+α)ρ∫10h(s)ds, |
we have (φ,ϕ)≥(w,w). As a result, we only need to seek fixed points of (2.9), when their norms are greater than M(γ+δ)(β+α)ρ∫10h(s)ds.
Let E be a real Banach space. A subset X⊂E is called a retract of E if there exists a continuous mapping r:E→X such that r(x)=x, x∈X. Note that every cone in E is a retract of E. Let X be a retract of real Banach space E. Then, for every relatively bounded open subset U of X and every completely continuous operator A:¯U→X which has no fixed points on ∂U, there exists an integer i(A,U,X) satisfying the following conditions:
(i) Normality: i(A,U,X)=1 if Ax≡y0∈U for any x∈¯U.
(ii) Additivity: i(A,U,X)=i(A,U1,X)+i(A,U2,X) whenever U1 and U2 are disjoint open subsets of U such that A has no fixed points on ¯U∖(U1∪U2).
(iii) Homotopy invariance: i(H(t,⋅),U,X) is independent of t (0≤t≤1) whenever H:[0,1]ׯU→X is completely continuous and H(t,x)≠x for any (t,x)∈[0,1]×∂U.
(iv) Permanence: i(A,U,X)=i(A,U∩Y,Y) if Y is a retract of X and A(¯U)⊂Y.
Then i(A,U,X) is called the fixed point index of A on U with respect to X.
Lemma 2.5. (see [35,36]). Let E be a real Banach space and P a cone on E. Suppose that Ω⊂E is a bounded open set and that A:¯Ω∩P→P is a continuous compact operator. If there exists ω0∈P∖{0} such that
ω−Aω≠λω0,∀λ≥0,ω∈∂Ω∩P, |
then i(A,Ω∩P,P)=0, where i denotes the fixed point index on P.
Lemma 2.6. (see [35,36]). Let E be a real Banach space and P a cone on E. Suppose that Ω⊂E is a bounded open set with 0∈Ω and that A:¯Ω∩P→P is a continuous compact operator. If
ω−λAω≠0,∀λ∈[0,1],ω∈∂Ω∩P, |
then i(A,Ω∩P,P)=1.
Denote OM,h=M(γ+δ)(β+α)ρ∫10h(s)ds, Bζ={u∈E:‖u‖<ζ},ζ>0,B2ζ=Bζ×Bζ. We list our assumptions as follows:
(H4) There exist p,q∈C(R+,R+) such that
(i) p is a strictly increasing concave function on R+;
(ii) lim infv→∞f(u,v)p(v)≥1, lim infu→∞g(u,v)q(u)≥1;
(iii) there exists e1∈(κ−21,∞) such that lim infz→∞p(LG,hq(z))z≥e1LG,h, where LG,h=maxt,s∈[0,1]G(t,s)h(s).
(H5) There exists Qi∈(0,OM,hκ2) such that
Fi(u−w,v−w)≤Qi,u,v∈[0,OM,h],i=1,2. |
(H6) There exist ζ,η∈C(R+,R+) such that
(i) ζ is a strictly increasing convex function on R+;
(ii) lim supv→∞f(u,v)ζ(v)≤1, lim supu→∞g(u,v)η(u)≤1;
(iii) there exists e2∈(0,κ−22) such that lim supz→∞ζ(LG,hη(z))z≤e2LG,h.
(H7) There exists ˜Qi∈(OM,hκ2LG,∞) such that
Fi(u−w,v−w)≥˜Qi,u,v∈[0,OM,h],i=1,2, |
where LG=maxt∈[0,1]ρ(γ+δ)(β+α)G(t,t).
Remark 3.1. Condition (H4) implies that f grows p(v)-superlinearly at ∞ uniformly on u∈R+, g grows q(u)-superlinearly at ∞ uniformly on v∈R+; condition (H6) implies that f grows ζ(v)-sublinearly at ∞ uniformly on u∈R+, g grows η(u)-sublinearly at ∞ uniformly on v∈R+.
Theorem 3.1. Suppose that (H1)–(H5) hold. Then (1.1) has at least one positive radial solution.
Proof. Step 1. When φ,ϕ∈∂BOM,h∩P, we have
(φ,ϕ)≠λA(φ,ϕ),λ∈[0,1]. | (3.1) |
Suppose the contrary i.e., if (3.1) is false, then there exist φ0,ϕ0∈∂BOM,h∩P and λ0∈[0,1] such that
(φ0,ϕ0)=λ0A(φ0,ϕ0). |
This implies that
φ0,ϕ0∈P0 | (3.2) |
and
‖φ0‖≤‖A1(φ0,ϕ0)‖, ‖ϕ0‖≤‖A2(φ0,ϕ0)‖. | (3.3) |
From (H5) we have
Ai(φ0,ϕ0)(t)=∫10G(t,s)h(s)Fi(φ0(s)−w(s),ϕ0(s)−w(s))ds≤∫10ϑ(s)Qids<OM,h,i=1,2. |
Thus
‖A1(φ0,ϕ0)‖+‖A2(φ0,ϕ0)‖<2OM,h=‖φ0‖+‖ϕ0‖(φ0,ϕ0∈∂BOM,h∩P), |
which contradicts (3.3), and thus (3.1) holds. From Lemma 2.6 we have
i(A,B2OM,h∩P2,P2)=1. | (3.4) |
Step 2. There exists a sufficiently large R>OM,h such that
(φ,ϕ)≠A(φ,ϕ)+λ(ϱ1,ϱ1),φ,ϕ∈∂BR∩P,λ≥0, | (3.5) |
where ϱ1∈P0 is a given element. Suppose the contrary. Then there are φ1,ϕ1∈∂BR∩P,λ1≥0 such that
(φ1,ϕ1)=A(φ1,ϕ1)+λ1(ϱ1,ϱ1). | (3.6) |
This implies that
φ1(t)=A1(φ1,ϕ1)(t)+λ1ϱ1(t), ϕ1(t)=A2(φ1,ϕ1)(t)+λ1ϱ1(t),t∈[0,1]. |
From Lemma 2.4 and ϱ1∈P0 we have
φ1,ϕ1∈P0. | (3.7) |
Note that ‖φ1‖=‖ϕ1‖=R>OM,h, and thus φ1(t)≥w(t),ϕ1(t)≥w(t),t∈[0,1].
By (H4)(ii) we obtain
lim infϕ→∞F1(φ,ϕ)p(ϕ)=lim infϕ→∞f(φ,ϕ)+Mp(ϕ)≥1, lim infφ→∞F2(φ,ϕ)q(φ)=lim infφ→∞g(φ,ϕ)+Mq(φ)≥1. |
This implies that there exist c1,c2>0 such that
F1(φ,ϕ)≥p(ϕ)−c1, F2(φ,ϕ)≥q(φ)−c2, φ,ϕ∈R+. |
Therefore, we have
φ1(t)=A1(φ1,ϕ1)(t)+λ1ϱ1(t)≥A1(φ1,ϕ1)(t)≥∫10G(t,s)h(s)[p(ϕ1(s)−w(s))−c1]ds≥∫10G(t,s)h(s)p(ϕ1(s)−w(s))ds−c1κ2 | (3.8) |
and
ϕ1(t)=A2(φ1,ϕ1)(t)+λ1ϱ1(t)≥A2(φ1,ϕ1)(t)≥∫10G(t,s)h(s)[q(φ1(s)−w(s))−c2]ds≥∫10G(t,s)h(s)q(φ1(s)−w(s))ds−c2κ2. | (3.9) |
Consequently, we have
ϕ1(t)−w(t)≥∫10G(t,s)h(s)q(φ1(s)−w(s))ds−c2κ2−w(t)≥∫10G(t,s)h(s)q(φ1(s)−w(s))ds−(c2+M)κ2. |
From (H4)(iii), there is a c3>0 such that
p(LG,hq(z))≥e1LG,hz−LG,hc3,z∈R+. |
Combining with (H4)(i), we have
p(ϕ1(t)−w(t))≥p(ϕ1(t)−w(t)+(c2+M)κ2)−p((c2+M)κ2)≥p(∫10G(t,s)h(s)q(φ1(s)−w(s))ds)−p((c2+M)κ2)=p(∫10G(t,s)h(s)LG,hLG,hq(φ1(s)−w(s))ds)−p((c2+M)κ2)≥∫10p(G(t,s)h(s)LG,hLG,hq(φ1(s)−w(s)))ds−p((c2+M)κ2)≥∫10G(t,s)h(s)LG,hp(LG,hq(φ1(s)−w(s)))ds−p((c2+M)κ2)≥∫10G(t,s)h(s)LG,h(e1LG,h(φ1(s)−w(s))−LG,hc3)ds−p((c2+M)κ2)≥e1∫10G(t,s)h(s)(φ1(s)−w(s))ds−p((c2+M)κ2)−c3κ2. |
Substituting this inequality into (3.8) we have
φ1(t)−w(t)≥∫10G(t,s)h(s)[e1∫10G(s,τ)h(τ)(φ1(τ)−w(τ))dτ−p((c2+M)κ2)−c3κ2]ds −(c1+M)κ2≥e1∫10∫10G(t,s)h(s)G(s,τ)h(τ)(φ1(τ)−w(τ))dτds −p((c2+M)κ2)κ2−c3κ22−(c1+M)κ2. |
Multiply by ϑ(t) on both sides of the above and integrate over [0,1] and use Lemma 2.2 to obtain
∫10(φ1(t)−w(t))ϑ(t)dt≥e1∫10ϑ(t)∫10∫10G(t,s)h(s)G(s,τ)h(τ)(φ1(τ)−w(τ))dτdsdt −p((c2+M)κ2)κ22−c3κ32−(c1+M)κ22≥e1κ21∫10(φ1(t)−w(t))ϑ(t)dt−p((c2+M)κ2)κ22−c3κ32−(c1+M)κ22. |
From this inequality we have
∫10(φ1(t)−w(t))ϑ(t)dt≤p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22e1κ21−1 |
and thus
∫10φ1(t)ϑ(t)dt≤p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22e1κ21−1+∫10w(t)ϑ(t)dt≤p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22e1κ21−1+Mκ22. |
Note that (3.7), φ1∈P0, and we have
‖φ1‖≤p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22κ1(e1κ21−1)+Mκ22κ1. |
On the other hand, multiply by ϑ(t) on both sides of (3.8) and integrate over [0,1] and use Lemma 2.2 to obtain
κ1∫10ϑ(t)p(ϕ1(t)−w(t))dt≤∫10φ1(t)ϑ(t)dt+c1κ22≤p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22e1κ21−1+Mκ22+c1κ22. |
From Remark 2.1 we have w∈P0, note that ‖ϕ1‖=R>M(γ+δ)(β+α)ρ∫10h(s)ds≥‖w‖ and ϕ1∈P0, then ϕ1−w∈P0. By the concavity of p we have
‖ϕ1−w‖≤κ−11∫10(ϕ1(t)−w(t))ϑ(t)dt=‖ϕ1−w‖κ1p(‖ϕ1−w‖)∫10ϕ1(t)−w(t)‖ϕ1−w‖p(‖ϕ1−w‖)ϑ(t)dt≤‖ϕ1−w‖κ1p(‖ϕ1−w‖)∫10p(ϕ1(t)−w(t)‖ϕ1−w‖‖ϕ1−w‖)ϑ(t)dt≤‖ϕ1−w‖κ21p(‖ϕ1−w‖)[p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22e1κ21−1+Mκ22+c1κ22]. |
This implies that
p(‖ϕ1−w‖)≤1κ21[p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22e1κ21−1+Mκ22+c1κ22]. |
From (H4)(i) we have
p(‖ϕ1‖)=p(‖ϕ1−w+w‖)≤p(‖ϕ1−w‖+‖w‖)≤p(‖ϕ1−w‖)+p(‖w‖)≤1κ21[p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22e1κ21−1+Mκ22+c1κ22]+p(‖w‖)≤1κ21[p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22e1κ21−1+Mκ22+c1κ22]+p(Mκ2)<+∞. |
Therefore, there exists Oϕ1>0 such that ‖ϕ1‖≤Oϕ1.
We have prove the boundedness of φ1,ϕ1 when (3.6) holds, i.e., when φ1,ϕ1∈∂BR∩P, there exist a positive constant to control the norms of φ1,ϕ1. Now we choose a sufficiently large
R1>max{OM,h,Oϕ1,p((c2+M)κ2)κ22+c3κ32+(c1+M)κ22κ1(e1κ21−1)+Mκ22κ1}. |
Then when φ1,ϕ1∈∂BR1∩P, (3.6) is not satisfied, and thus (3.5) holds. From Lemma 2.5 we have
i(A,B2R1∩P2,P2)=0. | (3.10) |
Combining (3.4) with (3.10) we have
i(A,(B2R1∖¯B2OM,h)∩P2,P2)=i(A,B2R1∩P2,P2)−i(A,B2OM,h∩P2,P2)=0−1=−1. |
Then the operator A has at least one fixed point (denoted by (φ∗,ϕ∗)) on (B2R1∖¯B2OM,h)∩P2 with φ∗(t),ϕ∗(t)≥w(t),t∈[0,1]. Therefore, (φ∗−w,ϕ∗−w) is a positive solution for (2.2), and (1.1) has at least one positive radial solution.
Theorem 3.2. Suppose that (H1)–(H3), (H6) and (H7) hold. Then (1.1) has at least one positive radial solution.
Proof. Step 1. When φ,ϕ∈∂BOM,h∩P, we have
(φ,ϕ)≠A(φ,ϕ)+λ(ϱ2,ϱ2),λ≥0, | (3.11) |
where ϱ2∈P is a given element. Suppose the contrary. Then there exist φ2,ϕ2∈∂BOM,h∩P,λ2≥0 such that
(φ2,ϕ2)=A(φ2,ϕ2)+λ2(ϱ2,ϱ2). |
This implies that
‖φ2‖≥φ2(t)≥A1(φ2,ϕ2)(t)+λ2ϱ2(t)≥A1(φ2,ϕ2)(t),t∈[0,1], |
‖ϕ2‖≥ϕ2(t)≥A2(φ2,ϕ2)(t)+λ2ϱ2(t)≥A2(φ2,ϕ2)(t),t∈[0,1]. |
Then we have
‖φ2‖+‖ϕ2‖≥‖A1(φ2,ϕ2)‖+‖A2(φ2,ϕ2)‖. | (3.12) |
From (H7) we have
‖Ai(φ2,ϕ2)‖=maxt∈[0,1]Ai(φ2,ϕ2)(t)≥maxt∈[0,1]ρ(γ+δ)(β+α)G(t,t)∫10G(s,s)h(s)Fi(φ2(s)−w(s),ϕ2(s)−w(s))ds≥LG∫10G(s,s)h(s)˜Qids=˜Qiκ2LG,i=1,2. |
By the condition on ˜Qi we have
‖A1(φ2,ϕ2)‖+‖A2(φ2,ϕ2)‖>2OM,h=‖φ2‖+‖ϕ2‖, |
and this contradicts (3.12), so (3.11) holds. By Lemma 2.5 we have
i(A,B2OM,h∩P2,P2)=0. | (3.13) |
Step 2. There exists a sufficiently large R>OM,h such that
(φ,ϕ)≠λA(φ,ϕ),φ,ϕ∈∂BR∩P,λ∈[0,1]. | (3.14) |
Suppose the contrary. Then there exist φ3,ϕ3∈∂BR∩P,λ3∈[0,1] such that
(φ3,ϕ3)=λ3A(φ3,ϕ3). | (3.15) |
Combining with Lemma 2.4 we have
φ3,ϕ3∈P0. | (3.16) |
Note that φ3,ϕ3∈∂BR∩P, and then φ3(t)−w(t),ϕ3(t)−w(t)≥0,t∈[0,1]. Hence, from (H6) we have
lim supϕ→∞F1(φ,ϕ)ζ(ϕ)=lim supϕ→∞f(φ,ϕ)+Mζ(ϕ)≤1, lim supφ→∞F2(φ,ϕ)η(φ)=lim supφ→∞g(φ,ϕ)+Mη(φ)≤1. |
This implies that there exists ˜M>0 such that
F1(φ,ϕ)≤ζ(ϕ), F2(φ,ϕ)≤η(φ),φ,ϕ≥˜M. | (3.17) |
By similar methods as in Theorem 3.1, choosing R>˜M, and from (3.15) we obtain
φ3(t)=λ3A1(φ3,ϕ3)(t)≤∫10G(t,s)h(s)ζ(ϕ3(s)−w(s))ds | (3.18) |
and
ϕ3(t)=λ3A2(φ3,ϕ3)(t)≤∫10G(t,s)h(s)η(φ3(s)−w(s))ds. | (3.19) |
From (H6)(iii), there exists c4>0 such that
ζ(LG,hη(z))≤e2LG,hz+c4LG,h,z∈R+. |
By the convexity of ζ we have
ζ(ϕ3(t)−w(t))≤ζ(∫10G(t,s)h(s)η(φ3(s)−w(s))ds)≤∫10ζ[G(t,s)h(s)η(φ3(s)−w(s))]ds=∫10ζ[G(t,s)h(s)LG,hLG,hη(φ3(s)−w(s))]ds≤∫10G(t,s)h(s)LG,hζ[LG,hη(φ3(s)−w(s))]ds≤∫10G(t,s)h(s)LG,h[e2LG,h(φ3(s)−w(s))+c4LG,h]ds≤∫10G(t,s)h(s)[e2(φ3(s)−w(s))+c4]ds. | (3.20) |
Substituting this inequality into (3.18) we have
φ3(t)≤∫10G(t,s)h(s)∫10G(s,τ)h(τ)[e2(φ3(τ)−w(τ))+c4]dτds≤e2∫10∫10G(t,s)h(s)G(s,τ)h(τ)(φ3(τ)−w(τ))dτds+c4κ22. | (3.21) |
Consequently, we have
φ3(t)−w(t)≤∫10G(t,s)h(s)∫10G(s,τ)h(τ)[e2(φ3(τ)−w(τ))+c4]dτds≤e2∫10∫10G(t,s)h(s)G(s,τ)h(τ)(φ3(τ)−w(τ))dτds+c4κ22. | (3.22) |
Multiply by ϑ(t) on both sides of (3.22) and integrate over [0,1] and use Lemma 2.2 to obtain
∫10(φ3(t)−w(t))ϑ(t)dt≤e2κ22∫10(φ3(t)−w(t))ϑ(t)dt+c4κ32, |
and we have
∫10(φ3(t)−w(t))ϑ(t)dt≤c4κ321−e2κ22. |
Note that (3.16), w∈P0, and
‖φ3−w‖≤c4κ32κ1(1−e2κ22). |
By the triangle inequality we have
‖φ3‖=‖φ3−w+w‖≤‖φ3−w‖+‖w‖≤c4κ32κ1(1−e2κ22)+Mκ2. |
On the other hand, from (3.20) we have
ζ(ϕ3(t)−w(t))≤∫10G(t,s)h(s)[e2(φ3(s)−w(s))+c4]ds≤∫10ϑ(s)[e2(φ3(s)−w(s))+c4]ds≤c4e2κ321−e2κ22+c4κ2. |
Note that c4e2κ321−e2κ22+c4κ2 is independent to R, and using (H6)(i) there exists Oϕ3>0 such that
‖ϕ3−w‖≤Oϕ3, |
and then
‖ϕ3‖=‖ϕ3−w+w‖≤‖ϕ3−w‖+‖w‖≤Oϕ3+Mκ2. |
Therefore, when φ3,ϕ3∈∂BR∩P, we obtain there is a positive constant to control the norms of φ3,ϕ3. Then if we choose
R2>{OM,h,Oϕ3+Mκ2,˜M,c4κ32κ1(1−e2κ22)+Mκ2}, |
then (3.14) holds, and from Lemma 2.6 we have
i(A,B2R2∩P2,P2)=1. | (3.23) |
From (3.13) and (3.23) we have
i(A,(B2R2∖¯B2OM,h)∩P2,P2)=i(A,B2R2∩P2,P2)−i(A,B2OM,h∩P2,P2)=1−0=1. |
Then the operator A has at least one fixed point (denoted by (u∗∗,v∗∗)) on (B2R2∖¯B2OM,h)∩P2 with u∗∗(t),v∗∗(t)≥w(t),t∈[0,1]. Therefore, (u∗∗−w,v∗∗−w) is a positive solution for (2.2), and (1.1) has at least one positive radial solution.
We now provide some examples to illustrate our main results. Let α=β=γ=δ=1, and k(|z|)=e|z|,z∈Rn. Then (H1) and (H2) hold.
Example 3.1. Let p(ϕ)=ϕ45,q(φ)=φ2,φ,ϕ∈R+. Then lim infz→∞p(LG,hq(z))z=lim infz→∞L45G,hz85z≥∞, and (H4)(i), (iii) hold. If we choose
f(φ,ϕ)=1β1κ2(|sinφ|+1)ϕ−M, g(φ,ϕ)=O1−β3M,hβ2κ2(|cosϕ|+1)φβ3−M,β1,β2>1,β3>2, |
then (H3) holds, and when φ,ϕ∈[0,OM,h], we have
F1(φ,ϕ)=f(φ,ϕ)+M≤OM,hβ1κ2:=Q1, F2(φ,ϕ)=g(φ,ϕ)+M≤O1−β3M,hβ2κ2Oβ3M,h=OM,hβ2κ2:=Q2. |
Hence, (H5) holds. Also we have
lim infϕ→∞f(φ,ϕ)p(ϕ)=lim infϕ→∞1β1κ2(|sinφ|+1)ϕ−Mϕ45=∞, lim infφ→∞g(φ,ϕ)q(φ)=lim infφ→∞O1−β3M,hβ2κ2(|cosϕ|+1)φβ3−Mφ2=∞. |
Then (H4)(ii) holds. As a result, all the conditions in Theorem 3.1 hold, and (1.1) has at least one positive radial solution.
Example 3.2. Let ζ(ϕ)=ϕ2,η(φ)=φ25, φ,ϕ∈R+. Then lim supz→∞ζ(LG,hη(z))z=lim supz→∞L2G,hz45z = 0≤e2LG,h, and (H7)(i), (iii) hold. If we choose
f(φ,ϕ)=˜Q1+(ϕ+|cosφ|)α1−M, g(φ,ϕ)=˜Q2+(|sinϕ|+φ)α2−M,φ,ϕ∈R+, |
where α1∈(0,2),α2∈(0,25). Then (H3) holds. Moreover, we have
F1(φ,ϕ)=f(φ,ϕ)+M≥˜Q1, F2(φ,ϕ)=g(φ,ϕ)+M≥˜Q2, |
and
lim supϕ→∞˜Q1−M+(ϕ+|cosφ|)α1ϕ2=0,lim supφ→∞˜Q2−M+(|sinϕ|+φ)α2φ25=0. |
Therefore, (H6) and (H7) (ii) hold. As a result, all the conditions in Theorem 3.2 hold, and (1.1) has at least one positive radial solution.
Remark 3.2. Note that condition (HZ)2 is often used to study various kinds of semipositone boundary value problems (for example, see [19,22,23,26,28,29,30]). However, in Example 3.1 we have
lim infϕ→+∞f(φ,ϕ)φ=lim infϕ→+∞1β1κ2(|sinφ|+1)ϕ−Mϕ=12β1κ2,∀φ∈R+. |
Comparing with (HZ)2 we see that our theory gives new results for boundary value problem with semipositone nonlinearities.
This research was supported by the National Natural Science Foundation of China (12101086), Changzhou Science and Technology Planning Project (CJ20210133), Natural Science Foundation of Chongqing (cstc2020jcyj-msxmX0123), and Technology Research Foundation of Chongqing Educational Committee (KJQN202000528).
The authors declare no conflict of interest.
[1] | Ghoushchi SJ (2018) Qualitative and quantitative analysis of Green Supply Chain Management (GSCM) literature from 2000 to 2015. Int J Supply Chain Manag 7: 77–86. |
[2] |
Galvin R, Healy N (2020) The Green New Deal in the United States: What it is and how to pay for it. Energy Res Soc Sci 67: 101529. https://doi.org/10.1016/j.erss.2020.101529 doi: 10.1016/j.erss.2020.101529
![]() |
[3] |
Howard M, Hopkinson P, Miemczyk J (2019) The regenerative supply chain: A framework for developing circular economy indicators. Int J Prod Res 57: 7300–7318. https://doi.org/10.1080/00207543.2018.1524166 doi: 10.1080/00207543.2018.1524166
![]() |
[4] |
Choudhury NA, Ramkumar M, Schoenherr T, et al. (2023) The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities. Transport Res E-Log 175: 103139. https://doi.org/10.1016/j.tre.2023.103139 doi: 10.1016/j.tre.2023.103139
![]() |
[5] |
Govindan K, Kaliyan M, Kannan D, et al. (2014) Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. Int J Prod Econ 147: 555–568. https://doi.org/10.1016/j.ijpe.2013.08.018 doi: 10.1016/j.ijpe.2013.08.018
![]() |
[6] |
Alhamali RM (2019) Critical success factors for green supply chain management practices: An empirical study on data collected from food processing companies in Saudi Arabia. Afr J Bus Manag 13: 160–167. https://doi.org/10.5897/AJBM2018.8709 doi: 10.5897/AJBM2018.8709
![]() |
[7] |
Ghoushchi SJ, Asghari M, Mardani A, et al. (2023) Designing an efficient humanitarian supply chain network during an emergency: A scenario-based multi-objective model. Socio-Econ Plan Sci 90: 101716. https://doi.org/10.1016/j.seps.2023.101716 doi: 10.1016/j.seps.2023.101716
![]() |
[8] |
Davis KF, Downs S, Gephart JA (2021) Towards food supply chain resilience to environmental shocks. Nat Food 2: 54–65. https://doi.org/10.1038/s43016-020-00196-3 doi: 10.1038/s43016-020-00196-3
![]() |
[9] |
Baloch N, Rashid A (2022) Supply chain networks, complexity, and optimization in developing economies: A systematic literature review and meta-analysis. South Asian J Oper Log 1: 1–13. https://doi.org/10.57044/SAJOL.2022.1.1.2202 doi: 10.57044/SAJOL.2022.1.1.2202
![]() |
[10] |
Azam W, Khan I, Ali SA (2023) Alternative energy and natural resources in determining environmental sustainability: A look at the role of government final consumption expenditures in France. Environ Sci Pollut R 30: 1949–1965. https://doi.org/10.1007/s11356-022-22334-z doi: 10.1007/s11356-022-22334-z
![]() |
[11] |
Feng Y, Lai KH, Zhu Q (2022) Green supply chain innovation: Emergence, adoption, and challenges. Int J Prod Econ 248: 108497. https://doi.org/10.1016/j.ijpe.2022.108497 doi: 10.1016/j.ijpe.2022.108497
![]() |
[12] |
Lis A, Sudolska A, Tomanek M (2020) Mapping research on sustainable supply-chain management. Sustainability 12: 3987. https://doi.org/10.3390/su12103987 doi: 10.3390/su12103987
![]() |
[13] |
Ghadge A, Jena SK, Kamble S, et al. (2021) Impact of financial risk on supply chains: A manufacturer-supplier relational perspective. Int J Prod Res 59: 7090–7105. https://doi.org/10.1080/00207543.2020.1834638 doi: 10.1080/00207543.2020.1834638
![]() |
[14] |
Ngo VM, Quang HT, Hoang TG, et al. (2024) Sustainability‐related supply chain risks and supply chain performances: The moderating effects of dynamic supply chain management practices. Bus Strateg Environ 33: 839–857. https://doi.org/10.1002/bse.3512 doi: 10.1002/bse.3512
![]() |
[15] |
Eftekharzadeh S, Ghoushchi S, Momayezi F (2024) Enhancing safety and risk management through an integrated spherical fuzzy approach for managing laboratory errors. Decision Sci Lett 13: 545–564. https://doi.org/10.5267/j.dsl.2024.5.006 doi: 10.5267/j.dsl.2024.5.006
![]() |
[16] |
Soleimani H, Mohammadi M, Fadaki M, et al. (2021) Carbon-efficient closed-loop supply chain network: An integrated modeling approach under uncertainty. Environ Sci Pollut R 1–16. https://doi.org/10.1007/s11356-021-15100-0 doi: 10.1007/s11356-021-15100-0
![]() |
[17] |
Azarkamand S, niloufar S (2014) Investigating green supply chain management in Isfahan iron smelting industry and its impact on the development of green performance. Appl Stud Manag Develop Sci 4: 15–28. https://doi.org/10.1016/j.spc.2024.06.006 doi: 10.1016/j.spc.2024.06.006
![]() |
[18] |
Alinejad A, Javad K (2014) Presenting a combined method of ANP and VIKOR in the green supply chain under the gray environment in order to prioritize customers (Case of Study: Fars Oil Products Distribution Company). Bus Manag 10. https://doi.org/10.1007/s11356-020-09092-6 doi: 10.1007/s11356-020-09092-6
![]() |
[19] |
Soon A, Heidari A, Khalilzadeh M, et al. (2022) Multi-objective sustainable closed-loop supply chain network design considering multiple products with different quality levels. Systems 10: 94. https://doi.org/10.3390/systems10040094 doi: 10.3390/systems10040094
![]() |
[20] |
Hafezalkotob A (2015) Competition of two green and regular supply chains under environmental protection and revenue seeking policies of government. Comput Ind Eng 82: 103–114. https://doi.org/10.1016/j.cie.2015.01.016 doi: 10.1016/j.cie.2015.01.016
![]() |
[21] |
Sheng X, Chen L, Yuan X, et al. (2023) Green supply chain management for a more sustainable manufacturing industry in China: A critical review. Environ Dev Sustain 25: 1151–1183. https://doi.org/10.1007/s10668-022-02109-9 doi: 10.1007/s10668-022-02109-9
![]() |
[22] |
Oudani M, Sebbar A, Zkik K, et al. (2023) Green Blockchain based IoT for secured supply chain of hazardous materials. Comput Ind Eng 175: 108814. https://doi.org/10.1016/j.cie.2022.108814 doi: 10.1016/j.cie.2022.108814
![]() |
[23] |
Esfahbodi A, Zhang Y, Watson G (2016) Sustainable supply chain management in emerging economies: Trade-offs between environmental and cost performance. Int J Prod Econ 181: 350–366. https://doi.org/10.1016/j.ijpe.2016.02.013 doi: 10.1016/j.ijpe.2016.02.013
![]() |
[24] |
Alghababsheh M, Butt AS, Moktadir MA (2022) Business strategy, green supply chain management practices, and financial performance: A nuanced empirical examination. J Clean Prod 380: 134865. https://doi.org/10.1016/j.jclepro.2022.134865 doi: 10.1016/j.jclepro.2022.134865
![]() |
[25] |
Falcó JM, García ES, Tudela LAM, et al. (2023) The role of green agriculture and green supply chain management in the green intellectual capital-sustainable performance relationship: A structural equation modeling analysis applied to the Spanish wine industry. Agriculture 13: 425. https://doi.org/10.3390/agriculture13020425 doi: 10.3390/agriculture13020425
![]() |
[26] |
Ecer F, Ögel İY, Krishankumar R, et al. (2023) The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era. Artif Intell Rev 56: 13373–13406. https://doi.org/10.1007/s10462-023-10476-6 doi: 10.1007/s10462-023-10476-6
![]() |
[27] |
Karimi A, Ghoushchi SJ, Bonab MM (2020) Presenting a new model for performance measurement of the sustainable supply chain of Shoa Panjereh Company in different provinces of Iran (case study). Int J Sys Assur Eng 11: 140–154. https://doi.org/10.1007/s13198-019-00932-4 doi: 10.1007/s13198-019-00932-4
![]() |
[28] |
Chatterjee K, Pamucar D, Zavadskas EK (2018) Evaluating the performance of suppliers based on using the R'AMATEL-MAIRCA method for green supply chain implementation in electronics industry. J Clean Prod 184: 101–129. https://doi.org/10.1016/j.jclepro.2018.02.186 doi: 10.1016/j.jclepro.2018.02.186
![]() |
[29] |
Mondal A, Giri BK, Roy SK, et al. (2024) Sustainable-resilient-responsive supply chain with demand prediction: An interval type-2 robust programming approach. Eng Appl Artif Intel 133: 108133. https://doi.org/10.1016/j.engappai.2024.108133 doi: 10.1016/j.engappai.2024.108133
![]() |
[30] |
Riese J, Fasel H, Pannok M, Lier S. (2024) Decentralized production concepts for bio-based polymers-implications for supply chains, costs, and the carbon footprint. Sustain Prod Consump 46: 460–475. https://doi.org/10.1016/j.spc.2024.03.001 doi: 10.1016/j.spc.2024.03.001
![]() |
[31] |
Ferreira IA, Oliveira J, Antonissen J, et al. (2023) Assessing the impact of fusion-based additive manufacturing technologies on green supply chain management performance. J Manuf Technol Mana 34: 187–211. https://doi.org/10.1108/JMTM-06-2022-0235 doi: 10.1108/JMTM-06-2022-0235
![]() |
[32] |
Hiloidhari M, Sharno MA, Baruah D, et al. (2023) Green and sustainable biomass supply chain for environmental, social and economic benefits. Biomass Bioenerg 175: 106893. https://doi.org/10.1016/j.biombioe.2023.106893 doi: 10.1016/j.biombioe.2023.106893
![]() |
[33] |
Zhang Z, Yu L (2023) Dynamic decision-making and coordination of low-carbon closed-loop supply chain considering different power structures and government double subsidy. Clean Technol Envir 25: 143–171. https://doi.org/10.1007/s10098-022-02394-y doi: 10.1007/s10098-022-02394-y
![]() |
[34] |
de Souza V, Ruwaard JB, Borsato M (2019) Towards regenerative supply networks: A design framework proposal. J Clean Prod 221: 145–156. https://doi.org/10.1016/j.jclepro.2019.02.178 doi: 10.1016/j.jclepro.2019.02.178
![]() |
[35] |
Khalilpourazari S, Soltanzadeh S, Weber GW, et al. (2020) Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study. Ann Oper Res 289: 123–152. https://doi.org/10.1007/s10479-019-03437-2 doi: 10.1007/s10479-019-03437-2
![]() |
[36] |
Fragkos P (2022) Analysing the systemic implications of energy efficiency and circular economy strategies in the decarbonisation context. AIMS Energy 10. https://doi.org/10.3934/energy.2022011 doi: 10.3934/energy.2022011
![]() |
[37] |
Tirkolaee EB, Torkayesh AE (2022) A cluster-based stratified hybrid decision support model under uncertainty: Sustainable healthcare landfill location selection. Appl Intell 52: 13614–13633. https://doi.org/10.1007/s10489-022-03335-4 doi: 10.1007/s10489-022-03335-4
![]() |
[38] |
Tirkolaee EB, Sadeghi S, Mooseloo FM, et al. (2021) Application of machine learning in supply chain management: A comprehensive overview of the main areas. Math Probl Eng 2021: 1–14. https://doi.org/10.1155/2021/1476043 doi: 10.1155/2021/1476043
![]() |
[39] |
Bai C, Rezaei J, Sarkis J (2017) Multicriteria green supplier segmentation. IEEE T Eng Manage 64: 515–528. https://doi.org/10.1109/TEM.2017.2723639 doi: 10.1109/TEM.2017.2723639
![]() |
[40] |
Muthuswamy M, Ali AM (2023) Sustainable supply chain management in the age of machine intelligence: Addressing challenges, capitalizing on opportunities, and shaping the future landscape. Sustain Machine Intell J 3: 1–14. https://doi.org/10.61185/SMIJ.2023.33103 doi: 10.61185/SMIJ.2023.33103
![]() |
[41] |
Kumar V, Pallathadka H, Sharma SK, et al. (2022) Role of machine learning in green supply chain management and operations management. Mater Today Proc 51: 2485–2489. https://doi.org/10.1016/j.matpr.2021.11.625 doi: 10.1016/j.matpr.2021.11.625
![]() |
[42] |
Wu T, Zuo M (2023) Green supply chain transformation and emission reduction based on machine learning. Sci Prog 106. https://doi.org/10.1177/00368504231165679 doi: 10.1177/00368504231165679
![]() |
[43] |
Priore P, Ponte B, Rosillo R (2018) Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. Int J Prod Res 57. https://doi.org/10.1080/00207543.2018.1552369 doi: 10.1080/00207543.2018.1552369
![]() |
[44] |
Ali SS, Kaur R, Ersö z F, et al. (2020) Measuring carbon performance for sustainable green supply chain practices: A developing country scenario. Cent Eur J Oper Res 28: 1389–1416. https://doi.org/10.1007/s10100-020-00673-x doi: 10.1007/s10100-020-00673-x
![]() |
[45] |
Barman H, Pervin M, Roy SK, et al. (2023) Analysis of a dual-channel green supply chain game-theoretical model under carbon policy. Int J Syst Sci-Oper 10: 2242770. https://doi.org/10.1080/23302674.2023.2242770 doi: 10.1080/23302674.2023.2242770
![]() |
[46] |
Lotfi R, Kargar B, Hoseini SH, et al. (2021) Resilience and sustainable supply chain network design by considering renewable energy. Int J Energ Res 45: 17749–17766. https://doi.org/10.1002/er.6943 doi: 10.1002/er.6943
![]() |
[47] |
Goli A, Tirkolaee EB, Golmohammadi AM, et al. (2023) A robust optimization model to design an IoT-based sustainable supply chain network with flexibility. Cent Eur J Oper Res 1–22. https://doi.org/10.1007/s10100-023-00870-4 doi: 10.1007/s10100-023-00870-4
![]() |
[48] |
Aytekin A, Okoth BO, Korucuk S, et al. (2022) A neutrosophic approach to evaluate the factors affecting performance and theory of sustainable supply chain management: Application to textile industry. Manage Decis 61: 506–529. https://doi.org/10.1108/MD-05-2022-0588 doi: 10.1108/MD-05-2022-0588
![]() |
[49] | Thakur AS (2022) Contextualizing urban sustainability: Limitations, tensions in Indian sustainable-smart urbanism perceived through intranational, international comparisons, and district city Ambala study, Sustainable Urbanism in Developing Countries, CRC. Press, 19–39. https://doi.org/10.1201/9781003131922 |
[50] |
Dhull S, Narwal M (2016) Drivers and barriers in green supply chain management adaptation: A state-of-art review. Uncertain Supply Chain Manag 4: 61–76. https://doi.org/10.5267/j.uscm.2015.7.003 doi: 10.5267/j.uscm.2015.7.003
![]() |
[51] |
Bag S, Viktorovich DA, Sahu AK, et al. (2020) Barriers to adoption of blockchain technology in green supply chain management. J Glob Oper Strateg 14: 104–133. https://doi.org/10.1108/JGOSS-06-2020-0027 doi: 10.1108/JGOSS-06-2020-0027
![]() |
[52] |
Rahman T, Ali SM, Moktadir MA, et al. (2020) Evaluating barriers to implementing green supply chain management: An example from an emerging economy. Prod Plan Control 31: 673–698. https://doi.org/10.1080/09537287.2019.1674939 doi: 10.1080/09537287.2019.1674939
![]() |
[53] | Alfina KN, Ratnayake RC, Wibisono D, et al. (2022) Analyzing barriers towards implementing circular economy in healthcare supply chains, In: 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE 827–831. https://doi.org/10.1109/IEEM55944.2022.9989999 |
[54] | Khiewnavawongsa S, Schmidt EK (2013) Barriers to green supply chain implementation in the electronics industry, In: 2013 IEEE international conference on industrial engineering and engineering management, IEEE 226–230. https://doi.org/10.1109/IEEM.2013.6962408 |
[55] |
Heeres TJ, Tran TM, Noort BA (2023) Drivers and barriers to implementing the internet of things in the health care supply chain: Mixed methods multicase study. J Med Internet Res 25: e48730. https://doi.org/10.2196/48730 doi: 10.2196/48730
![]() |
[56] |
Li J, Sarkis J (2022) Product eco-design practice in green supply chain management: A china-global examination of research. Nankai Bu Rev Int 13: 124–153. https://doi.org/10.1108/NBRI-02-2021-0006 doi: 10.1108/NBRI-02-2021-0006
![]() |
[57] |
Okanlawon TT, Oyewobi LO, Jimoh RA (2023) Evaluation of the drivers to the implementation of blockchain technology in the construction supply chain management in Nigeria. J Financ Manag Prop 28: 459–476. https://doi.org/10.1108/JFMPC-11-2022-0058 doi: 10.1108/JFMPC-11-2022-0058
![]() |
[58] |
Shrivastav M (2021) Barriers related to AI implementation in supply chain management. J Glob Inf Manag 30: 1–19. https://doi.org/10.4018/JGIM.296725 doi: 10.4018/JGIM.296725
![]() |
[59] |
Mathiyazhagan K, Datta U, Bhadauria R, et al. (2018) Identification and prioritization of motivational factors for the green supply chain management adoption: Case from Indian construction industries. Opsearch 55: 202–219. https://doi.org/10.1007/s12597-017-0316-7 doi: 10.1007/s12597-017-0316-7
![]() |
[60] |
Bey N, Hauschild MZ, McAloone TC (2013) Drivers and barriers for implementation of environmental strategies in manufacturing companies. Cirp Ann 62: 43–46. https://doi.org/10.1016/j.cirp.2013.03.001 doi: 10.1016/j.cirp.2013.03.001
![]() |
[61] |
Zadeh LA (1965) Fuzzy sets. Inf Control 8: 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
![]() |
[62] |
Wang F (2021) Preference degree of triangular fuzzy numbers and its application to multi-attribute group decision making. Expert Syst App 178: 114982. https://doi.org/10.1016/j.eswa.2021.114982 doi: 10.1016/j.eswa.2021.114982
![]() |
[63] |
Tešić D, Božanić D, Khalilzadeh M (2024) Enhancing multi-criteria decision-making with fuzzy logic: An advanced defining interrelationships between ranked Ⅱ method incorporating triangular fuzzy numbers. J Intel Manag Decis 3: 56–67. https://doi.org/10.56578/jimd030105 doi: 10.56578/jimd030105
![]() |
[64] |
Zadeh LA (2011) A note on Z-numbers. Inf Sci 181: 2923–2932. https://doi.org/10.1016/j.ins.2011.02.022 doi: 10.1016/j.ins.2011.02.022
![]() |
[65] |
Tian Y, Mi X, Ji Y, et al. (2021) ZE-numbers: A new extended Z-numbers and its application on multiple attribute group decision making. Eng Appl Artif Intel 101: 104225. https://doi.org/10.1016/j.engappai.2021.104225 doi: 10.1016/j.engappai.2021.104225
![]() |
[66] |
Stanujkic D, Karabasevic D, Zavadskas EK (2015) A framework for the selection of a packaging design based on the SWARA method. Eng Econ 26: 181–187. https://doi.org/10.5755/j01.ee.26.2.8820 doi: 10.5755/j01.ee.26.2.8820
![]() |
[67] |
Roy SK, Maity G, Weber GW (2017) Multi-objective two-stage grey transportation problem using utility function with goals. Cent Eur J Oper Res 25: 417–439. https://doi.org/10.1007/s10100-016-0464-5 doi: 10.1007/s10100-016-0464-5
![]() |
[68] |
Savku E, Weber GW (2018) A stochastic maximum principle for a Markov regime-switching jump-diffusion model with delay and an application to finance. J Optimiz Theory App 179: 696–721. https://doi.org/10.1007/s10957-017-1159-3 doi: 10.1007/s10957-017-1159-3
![]() |
[69] |
Özmen A, Kropat E, Weber GW (2017) Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty. Optimization 66: 2135–2155. https://doi.org/10.1080/02331934.2016.1209672 doi: 10.1080/02331934.2016.1209672
![]() |
1. | Ala Amourah, Abdullah Alsoboh, Daniel Breaz, Sheza M. El-Deeb, A Bi-Starlike Class in a Leaf-like Domain Defined through Subordination via q̧-Calculus, 2024, 12, 2227-7390, 1735, 10.3390/math12111735 |