
With the fast growth of the economy and rapid urbanization, the waste produced by the urban population also rises as the population increases. Due to communal, ecological, and financial constrictions, indicating a landfill site has become perplexing. Also, the choice of the landfill site is oppressed with vagueness and complexity due to the deficiency of information from experts and the existence of indeterminate data in the decision-making (DM) process. The neutrosophic hypersoft set (NHSS) is the most generalized form of the neutrosophic soft set, which deals with the multi-sub-attributes of the alternatives. The NHSS accurately judges the insufficiencies, concerns, and hesitation in the DM process compared to IFHSS and PFHSS, considering the truthiness, falsity, and indeterminacy of each sub-attribute of given parameters. This research extant the operational laws for neutrosophic hypersoft numbers (NHSNs). Furthermore, we introduce the aggregation operators (AOs) for NHSS, such as neutrosophic hypersoft weighted average (NHSWA) and neutrosophic hypersoft weighted geometric (NHSWG) operators, with their necessary properties. Also, a novel multi-criteria decision-making (MCDM) approach has been developed for site selection of solid waste management (SWM). Moreover, a numerical description is presented to confirm the reliability and usability of the proposed technique. The output of the advocated algorithm is compared with the related models already established to regulate the favorable features of the planned study.
Citation: Rana Muhammad Zulqarnain, Wen Xiu Ma, Imran Siddique, Shahid Hussain Gurmani, Fahd Jarad, Muhammad Irfan Ahamad. Extension of aggregation operators to site selection for solid waste management under neutrosophic hypersoft set[J]. AIMS Mathematics, 2023, 8(2): 4168-4201. doi: 10.3934/math.2023208
[1] | Zeliha Korpinar, Mustafa Inc, Dumitru Baleanu . On the fractional model of Fokker-Planck equations with two different operator. AIMS Mathematics, 2020, 5(1): 236-248. doi: 10.3934/math.2020015 |
[2] | Ali Khalouta, Abdelouahab Kadem . A new computational for approximate analytical solutions of nonlinear time-fractional wave-like equations with variable coefficients. AIMS Mathematics, 2020, 5(1): 1-14. doi: 10.3934/math.2020001 |
[3] | Hayman Thabet, Subhash Kendre, James Peters . Travelling wave solutions for fractional Korteweg-de Vries equations via an approximate-analytical method. AIMS Mathematics, 2019, 4(4): 1203-1222. doi: 10.3934/math.2019.4.1203 |
[4] | Xingang Zhang, Zhe Liu, Ling Ding, Bo Tang . Global solutions to a nonlinear Fokker-Planck equation. AIMS Mathematics, 2023, 8(7): 16115-16126. doi: 10.3934/math.2023822 |
[5] | Musawa Yahya Almusawa, Hassan Almusawa . Numerical analysis of the fractional nonlinear waves of fifth-order KdV and Kawahara equations under Caputo operator. AIMS Mathematics, 2024, 9(11): 31898-31925. doi: 10.3934/math.20241533 |
[6] | Zahra Pirouzeh, Mohammad Hadi Noori Skandari, Kamele Nassiri Pirbazari, Stanford Shateyi . A pseudo-spectral approach for optimal control problems of variable-order fractional integro-differential equations. AIMS Mathematics, 2024, 9(9): 23692-23710. doi: 10.3934/math.20241151 |
[7] | Amjid Ali, Teruya Minamoto, Rasool Shah, Kamsing Nonlaopon . A novel numerical method for solution of fractional partial differential equations involving the ψ-Caputo fractional derivative. AIMS Mathematics, 2023, 8(1): 2137-2153. doi: 10.3934/math.2023110 |
[8] | Shu-Nan Li, Bing-Yang Cao . Anomalies of Lévy-based thermal transport from the Lévy-Fokker-Planck equation. AIMS Mathematics, 2021, 6(7): 6868-6881. doi: 10.3934/math.2021402 |
[9] | Krunal B. Kachhia, Jyotindra C. Prajapati . Generalized iterative method for the solution of linear and nonlinear fractional differential equations with composite fractional derivative operator. AIMS Mathematics, 2020, 5(4): 2888-2898. doi: 10.3934/math.2020186 |
[10] | Ramzi B. Albadarneh, Iqbal Batiha, A. K. Alomari, Nedal Tahat . Numerical approach for approximating the Caputo fractional-order derivative operator. AIMS Mathematics, 2021, 6(11): 12743-12756. doi: 10.3934/math.2021735 |
With the fast growth of the economy and rapid urbanization, the waste produced by the urban population also rises as the population increases. Due to communal, ecological, and financial constrictions, indicating a landfill site has become perplexing. Also, the choice of the landfill site is oppressed with vagueness and complexity due to the deficiency of information from experts and the existence of indeterminate data in the decision-making (DM) process. The neutrosophic hypersoft set (NHSS) is the most generalized form of the neutrosophic soft set, which deals with the multi-sub-attributes of the alternatives. The NHSS accurately judges the insufficiencies, concerns, and hesitation in the DM process compared to IFHSS and PFHSS, considering the truthiness, falsity, and indeterminacy of each sub-attribute of given parameters. This research extant the operational laws for neutrosophic hypersoft numbers (NHSNs). Furthermore, we introduce the aggregation operators (AOs) for NHSS, such as neutrosophic hypersoft weighted average (NHSWA) and neutrosophic hypersoft weighted geometric (NHSWG) operators, with their necessary properties. Also, a novel multi-criteria decision-making (MCDM) approach has been developed for site selection of solid waste management (SWM). Moreover, a numerical description is presented to confirm the reliability and usability of the proposed technique. The output of the advocated algorithm is compared with the related models already established to regulate the favorable features of the planned study.
The ordinary differential equations (ODEs) and partial differential equations (PDEs) are widely used to represent physical phenomena in mathematical language in science and technology. The mathematical form of the physical phenomena easily explains the whole scenario of the phenomena and makes them openly understandable and investigated straightforwardly. Initially, these phenomena were not only modeled accurately by using integer-order differential equations but later on fractional differential equations have over come the deficiencies and comparatively provide the best and adequate modelling of the given problems. Fractional order ODEs and PDEs describe some phenomena more accurately than non-fractional order ODES and PDEs and have numerous applications in applied sciences. It is shown that the fractional-order ODEs and PDEs are non-local and imply that the next state of a system depends on its current state and its previous states. Therefore, the fractional-order derivatives and integration have numerous applications such as the nonlinear oscillation of earth quack is molded with fractional-order derivatives [1], chaos theory [2], fractional diabetes model [3], fractional order Covid-19 Model [4], optics [5], fractional model of cancer chemotherapy [6], effect of fractional order on ferromagnetic fluid [7], the fractional-order fluid dynamic traffic model [2], signal processing phenomena [9], electrodynamics [10], fractional model for the dynamics of Hepatitis B Virus [11], fractional model for tuberculosis [12], fractional-order pine wilt disease model [13], and some others references therein [14,15,16,17,18].
The Korteweg-De Vries (KdV) equation is a mathematical model of waves on shallow water surfaces. It is particularly notable as the prototypical example of an exactly solvable model that is, a non-linear partial differential equation whose solutions can be exactly and precisely specified. KdV can be solved by means of the inverse scattering transform. The mathematical theory behind the KdV equation is a topic of active research. The KdV equation was first introduced by Boussinesq (1877) and rediscovered by Diederik Korteweg and Gustav de Vries (1895) [19].
Among these applications, we have considered Fokker-Planck equations of fractional order of the general from
ψδt(ϑ,τ)=L(ψϑ(ϑ,τ)+ψϑϑ(ϑ,τ))+Nψϑϑ(ϑ,τ), ϑ,τ>0, δ∈(0,1], | (1.1) |
with initial source
ψ(ϑ,0)=ℏ(ϑ). |
The Fokker-Planck equation (1.1) was introduced by Fokker and Planck to describe the brownian motion of partiles [20]. The Fokker-Plank equation represents the change in probability of a random function in time and space, which explain solute transport. Many phenomena, such as wave propagation, continuous random walk, charge carrier transport in amorphous semiconductors, anomalous diffusion, the motion of ribosomes along mRNA and pattern formation, polymeric networks are modelled by PDEs of both time and space fractional order [21].
Gorge Adomian was the first who proposed Adomian Decomposition Method (ADM) 1980. The crucial aspect of the method is employment of the "Adomian polynomials" which allow for solution convergence of the nonlinear portion of the equation, without simply linearizing the system. ADM is a type of method that uses a decomposition technique to generate approximation and even accurate solutions for non-linear systems with valid initial data. ADM have been used effectively for the solution of partial differential equations (PDE's) and fractional partial differential equations (FPDE's). In recent years, more and more researchers have applied this method to solving non-linear systems [24]. ADM is preferred over other techniques because of its simple implementation for wide range of nonlinear systems, including ordinary and partial differential equations with fractional derivatives. Moreover, ADM can easily handle the solutions of nonlinear fractional problems which is not the case with other techniques. It can be extended easily for higher dimensions problems.
The targeted solutions of fractional order ODEs and PDEs are also the main research focus of the mathematicians. There are many techniques that have been used for the solutions of fractional order Fokker-Planck equations such as Generalized finite differences method (GFDM) [22], radial basis functions finite difference (RBF-FD) [23], adomian decomposition method [25], homotopy perturbation transform method [26], iterative Laplace transform method [27], residual power method [28], finite element method [29], fractional variational iteration method [30], fractional reduced differential transform method [31].
In this article, we have implemented new approximate analytical method (NAAM) for the solution these type Fokker-Planck equations of fractional order. The NAAM is a analytical procedure which provide series form solution. The NAAM technique is easy and straight forward approach. The obtained results are fastly convergent towards the exact solution of each problem. The graphical analysis of NAAM solutions are demonstrated which has good agreement with exact solution of the problems.The remaining paper is systematized as Section 2, shows Preliminary concepts. In Section 3, signifies Procedure of NAAM. Section 4, we explained the implementation of NAAM on Fokker-Planck equations. Section 5, we summarized the obtained results.
In this section, the related definitions and preliminary concepts of fractional calculus and the procedure of the NAAM are presented.
The Riemann-Liouuille fractional partial integral denoted by Iδτ, where, δ∈N,δ≥0, which is define as under
Iδτψ(ϑ,τ)={1Γ(δ)∫τ0ψ(ϑ,τ)dτ, ϑ,τ>0,ψ(ϑ,τ), ϑ=0,τ>0, | (2.1) |
where, Γ is represent gamma function.
Let δ,β∈R,∖N, δ,γ>0,ρ>−1, then for the function ψ(ϑ,τ) the operator Iδτ has the following properties.
{Iδτψ(ϑ,τ)Iγtψ(ϑ,τ)=Iδ+γτψ(ϑ,τ),Iδτψ(ϑ,τ)Iγτψ(ϑ,τ)=Iγτψ(ϑ,τ)Iδτψ(ϑ,τ),Iδττρ=Γ(ρ+1)Γ(δ+ρ+1)τδ+ρ. | (2.2) |
Dδτψ(ϑ,τ)=∂δψ(ϑ,τ)∂τδ={In−δ[∂δψ(ϑ,τ)∂τδ], n−1<δ<n, n∈N,∂δψ(ϑ,τ)∂τδ, n=δ. | (2.3) |
Let ϑ,τ∈R, τ>0, and m−1<ρ<m∈N, then
IδτDδτψ(ϑ,τ)=ψ(ϑ,τ)−m−1∑k=0τkk!∂kψ(ϑ,0+)∂τk,DϑτIϑτψ(ϑ,τ)=ψ(ϑ,τ). | (2.4) |
If there exists a constant 0<γ<1suchthat:
||un+1(ϑ,τ)||≤γ||un(ϑ,τ)||, n∈N, ϑ∈I⊂R, 0≤t<R, | (2.5) |
then the sequence of approximate solution converges to the exact solution.
Proof. See [35].
Here, we have analyzed the analytical procedure for the solution of fractional order Fokker-Planck equations by introducing approximate analytical method.
Consider a general form of Fokker-Planck equation as
ψδt(ϑ,τ)=L(ψϑ(ϑ,τ)+ψϑϑ(ϑ,τ))+Nψϑϑ(ϑ,τ), ϑ,τ>0, δ∈(0,1], | (3.1) |
with initial source
ψ(ϑ,0)=ℏ(ϑ), |
where L,N are linear and non-linear operator respectively.
Before computational procedure, we have defined some mandatory procedural results below.
For Lψ(ϑ,τ)=∑∞0γkLψk(ϑ,τ), the linear term Lψ(ϑ,τ) satisfy the following property
Lψ(ϑ,τ)=(L∞∑k=0γkψk(ϑ,τ))=∞∑k=0(γkLψk(ϑ,τ)). | (3.2) |
The nonlinear operator Nψ(ϑ,τ), for the parameter λ, we define ψλ(ϑ,τ)=∑∞0λkψk(ϑ,τ), compensate the following property
N(ψλ)=N(∞∑0λkψk(ϑ,τ))=∞∑0[1n!dndλn[N(∞∑0λkψk(ϑ,τ))]λ=0]λn. | (3.3) |
The polynomial χn=χn(ψ0,ψ1,⋯,ψn), can be calculated as
χn(ψ0,ψ1,⋯,ψn)=1n!dndλn[ℵ(∞∑0λkψk(ϑ,τ))]λ=0 | (3.4) |
For χn=χn(ψ0,ψ1,⋯,ψn), the non-linear term N(ψλ), with using definition (3.4), is represented as
N(ψλ)=∞∑0λkχk | (3.5) |
The following result briefly explain the exitance of NAAM.
Theorem
Let N(ψλ),ψ(ϑ,τ) are define for ϵ−1<δ<ϵ, in (3.1). The Fokker-Planck model (3.1), the unique solution is given as
ψ(ϑ,τ)=ψ(ϑ,0)+∞∑k=1[L−δτ(ψ(k−1))+χ−δ(k−1)t], | (3.6) |
where, L−δτ(ψ(k−1)) and χ−δ(k−1)τ represent the fractional partial integral of order δ for L(ψk−1) and χ(k−1) with respect to τ.
Proof. The solution of Fokker-Planck equation ψ(ϑ,τ), is obtained by substituting the following expansion
ψ(ϑ,τ)=∞∑k=0ψk(ϑ,τ). | (3.7) |
To numerate the Eq (3.1), we can investigate as
ψδτλ(ϑ,τ)=λ[L(ψϑ(ϑ,τ)+ψϑϑ(ϑ,τ))+Nψϑϑ(ϑ,τ)], λ∈(0,1], | (3.8) |
along with initial sources
ψ(ϑ,0)=ℏ(ϑ). | (3.9) |
Additionally, the solution of Eq (3.6) is estimated as
ψλ(ϑ,τ)=∞∑0λkψλ(ϑ,τ), | (3.10) |
using the Caupto-Riemann property on Eq (3.8), we have
ψλ(ϑ,τ)=ψ(ϑ,0)+λ[L(ψϑ(ϑ,τ)+ψϑϑ(ϑ,τ))+Nψϑϑ(ϑ,τ)], | (3.11) |
assuming Eq (3.9), the initial source, Eq (3.11), become as
ψλ(ϑ,τ)=ℏ(ϑ)+λ[L(ψϑ(ϑ,τ)+ψϑϑ(ϑ,τ))+Nψϑϑ(ϑ,τ)]. | (3.12) |
By substituting, Eq (3.10), in Eq (3.12), we get
∞∑k=0λkψλ(ϑ,τ)=ℏ(ϑ)+λIδt[L(∞∑k=0λk(ψϑ(ϑ,τ)+ψϑϑ(ϑ,τ)))+N(∞∑k=0λkψϑϑ(ϑ,τ))], | (3.13) |
with the help of Theorem (3.4), and Lemma (3.1), the Eq (3.13) become as
∞∑k=0λkψλ(ϑ,τ)=ℏ(ϑ)+λIδt[L(∞∑k=0λk(ψϑ(ϑ,τ)+ψϑϑ(ϑ,τ)))]+λIδt[N(∞∑k=0λkχn)], | (3.14) |
the iterative scheme is obtained by comparing the identical power of λ in Eq (3.14), as
{ψ0(ϑ,τ)=ℏ(ϑ),ψ1(ϑ,τ)=L−δτψ0+χ−δτ0,ψk(ϑ,τ)=L−δτψ(k−1)+χ−δτ(k−1), k=2,3,⋯ | (3.15) |
In this section, we have tested the validity and applicability of NAAM by solving some Fokker-Planck equations.
Problem 4.1. Consider a Fokker-Planck equation of time fractional order in the form [36]:
∂δ∂τδ(ψ(ϑ,τ))+∂∂ϑ(ϑ6ψ(ϑ,τ))−∂2∂ϑ2(ϑ212ψ(ϑ,τ))=0, ϑ,τ>0, δ∈(0,1], | (4.1) |
with initial source
ψ(ϑ,0)=ϑ2, |
for special value δ=1, the exact form solution is
ψ(ϑ,τ)=ϑ2expτ2. |
To investigate the solution of the Fokker-Planck equation (21), we compare it with the Eq (6), we get
∂δ∂τδ(ψ(ϑ,τ))=∂∂ϑ(ϑ6ψ(ϑ,τ))−∂2∂ϑ2(ϑ212ψ(ϑ,τ)), ϑ,τ>0, δ∈(0,1], | (4.2) |
the genral NAAM solution of Eq (4.1), is assume as
ψ(ϑ,τ)=∞∑k=0ψk(ϑ,τ). | (4.3) |
For investigating the approximate solution of Eq (4.2), we process as
∂δ∂τδ(ψ(ϑ,τ))=λ[∂∂ϑ(ϑ6ψ(ϑ,τ))−∂2∂ϑ2(ϑ212ψ(ϑ,τ))], δ∈(0,1], | (4.4) |
where the initial condition is given as
ψ(ϑ,0)=ϑ2. | (4.5) |
The assume solution of Eq (4.4), is in the form
ψλ(ϑ,τ)=∞∑k=0λkψk(ϑ,τ). | (4.6) |
Using the Caupto-Riemann property on Eq (3.8), we have
ψλ(ϑ,τ)=ψ(ϑ,0)+λIδt[∂∂ϑ(ϑ6ψ(ϑ,τ))−∂2∂ϑ2(ϑ212ψ(ϑ,τ))], | (4.7) |
assuming the initial condition (3.5), Eq (4.6), become as
ψλ(ϑ,τ)=ϑ2+λIδt[∂∂ϑ(ϑ6ψ(ϑ,τ))−∂2∂ϑ2(ϑ212ψ(ϑ,τ))], | (4.8) |
by substituting Eq (3.10), in Eq (4.8), we get
∞∑k=0λkψk(ϑ,τ)=ϑ2+λIδt[∞∑k=0λk(∂∂ϑ(ϑ6ψk(ϑ,τ)))−∞∑k=0λk∂2∂ϑ2(ϑ212ψk(ϑ,τ))], | (4.9) |
the iterative scheme is obtained by comparing the identical power of λ in Eq (4.9), as
{ψ0(ϑ,τ)=ϑ2,ψ1(ϑ,τ)=Iδt[∂∂ϑ(ϑ6ψ0(ϑ,τ))−∂2∂ϑ2(ϑ212ψ0(ϑ,τ))],ψk(ϑ,τ)=Iδt[∂∂ϑ(ϑ6ψk−1(ϑ,τ))−∂2∂ϑ2(ϑ212ψk−1(ϑ,τ))]. | (4.10) |
Consequently, we get
ψ0(ϑ,τ)=ϑ2, | (4.11) |
ψ1(ϑ,τ)=ϑ2tδ2Γ(δ+1), | (4.12) |
ψ2(ϑ,τ)=ϑ2t2δ8Γ(2δ+1), | (4.13) |
ψ3(ϑ,τ)=ϑ2t3δ8Γ(3δ+1), | (4.14) |
⋮. |
The NAAM solution is
ψ(ϑ,τ)=ψ0(ϑ,τ)+ψ1(ϑ,τ)+ψ2(ϑ,τ)+ψ3(ϑ,τ)+⋯, | (4.15) |
subsisting Eqs (4.11)–(4.14), in Eq (4.15), we get
ψ(ϑ,τ)=ϑ2+ϑ2tδ2Γ(δ+1)+ϑ2t2δ8Γ(2δ+1)+ϑ2t3δ8Γ(3δ+1)+⋯, | (4.16) |
Specifically for δ=1, the solution converted to
ψ(ϑ,τ)=ϑ2+ϑ2t2Γ(2)+ϑ2t28Γ(3)+ϑ2t38Γ(4)+⋯, | (4.17) |
which converge to exact solution as
ψ(ϑ,τ)=ϑ2expτ2. | (4.18) |
Problem 4.2.
Consider a Fokker-Planck equation of time fractional order in the form [36];
∂∂τδ(ψ(ϑ,τ))+∂∂ϑ(ϑψ(ϑ,τ))−∂2∂ϑ2(ϑ22ψ(ϑ,τ))=0, ϑ,τ>0, δ∈(0,1], | (4.19) |
with initial source
ψ(ϑ,0)=ϑ, | (4.20) |
for special value δ=1, the exact form solution is
ψ(ϑ,τ)=ϑexpτ. |
To investigate the solution of the Fokker-Planck equation (4.19), we compare it with the Eq (3.1), we get
∂δ∂τδ(ψ(ϑ,τ))=−∂∂ϑ(ϑψ(ϑ,τ))+∂2∂ϑ2(ϑ22ψ(ϑ,τ)), ϑ,τ>0, δ∈(0,1], | (4.21) |
the general NAAM solution of Eq (4.19), is assume as
ψ(ϑ,τ)=∞∑k=0ψk(ϑ,τ). | (4.22) |
For investigating the approximate solution of Eq (4.20), we process as
∂δ∂τδ(ψ(ϑ,τ))=λ[−∂∂ϑ(ϑψ(ϑ,τ))+∂2∂ϑ2(ϑ22ψ(ϑ,τ))], δ∈(0,1], | (4.23) |
where the initial condition is given as
ψ(ϑ,0)=ϑ. | (4.24) |
The assume solution of Eq (4.23), is in the form
ψλ(ϑ,τ)=∞∑k=0λkψk(ϑ,τ). | (4.25) |
Using the Caupto-Riemann property on Eq (4.23), we have
ψλ(ϑ,τ)=ψ(ϑ,0)+λIδt[−∂∂ϑ(ϑψ(ϑ,τ))+∂2∂ϑ2(ϑ22ψ(ϑ,τ))], | (4.26) |
assuming the initial condition (4.20), Eq (4.26), become as
ψλ(ϑ,τ)=ϑ+λIδt[−∂∂ϑ(ϑψ(ϑ,τ))+∂2∂ϑ2(ϑ22ψ(ϑ,τ))], | (4.27) |
by substituting Eq (4.25), in Eq (4.27), we get
∞∑k=0λkψk(ϑ,τ)=ϑ+λIδt[∞∑k=0λk(−∂∂ϑ(ϑψ(ϑ,τ)))+∞∑k=0λk∂2∂ϑ2(∂2∂ϑ2(ϑ22ψ(ϑ,τ)))], | (4.28) |
the iterative scheme is obtained by comparing the identical power of λ in Eq (4.28), as
{ψ0(ϑ,τ)=ϑ,ψ1(ϑ,τ)=Iδt[−∂∂ϑ(ϑψ0(ϑ,τ))+∂2∂ϑ2(ϑ22ψ0(ϑ,τ))],ψk(ϑ,τ)=Iδt[−∂∂ϑ(ϑψk−1(ϑ,τ))+∂2∂ϑ2(ϑ22ψk−1(ϑ,τ))]. | (4.29) |
Consequently, we get
ψ0(ϑ,τ)=ϑ, | (4.30) |
ψ1(ϑ,τ)=ϑtδΓ(δ+1), | (4.31) |
ψ2(ϑ,τ)=ϑt2δΓ(2δ+1), | (4.32) |
ψ3(ϑ,τ)=ϑt3δΓ(3δ+1), | (4.33) |
⋮ |
The NAAM solution is
ψ(ϑ,τ)=ψ0(ϑ,τ)+ψ1(ϑ,τ)+ψ2(ϑ,τ)+ψ3(ϑ,τ)+⋯, | (4.34) |
subsisting Eqs (4.30)–(4.33), in Eq (4.34), we get
ψ(ϑ,τ)=ϑ+ϑtδΓ(δ+1)+ϑt2δΓ(2δ+1)+ϑt3δΓ(3δ+1)+⋯. | (4.35) |
Specifically for δ=1, the solution converted to
ψ(ϑ,τ)=ϑ+ϑtΓ(2)+ϑt2Γ(3)+ϑt3Γ(4)+⋯, | (4.36) |
which converge to exact solution as
ψ(ϑ,τ)=ϑexpτ. | (4.37) |
Problem 4.3.
Consider a Fokker-Planck equation of time fractional order in the form [36];
∂∂τδ(ψ(ϑ,τ))+∂∂ϑ(4ϑψ2(ϑ,τ))−∂∂ϑ(ϑ3ψ(ϑ,τ))−∂2∂ϑ2(ψ2(ϑ,τ))=0, ϑ,τ>0, δ∈(0,1], | (4.38) |
where initial condition, given as
ψ(ϑ,0)=ϑ2. | (4.39) |
For special value δ=1, the exact form solution is
ψ(ϑ,τ)=ϑ2expτ. |
To investigate the solution of the Fokker-Planck equation (4.38), we compare it with the Eq (3.1), we get
∂δ∂τδ(ψ(ϑ,τ))=∂∂ϑ(ϑ3ψ(ϑ,τ))+N(ψ(ϑ,τ)), ϑ,τ>0, δ∈(0,1], | (4.40) |
where, the non-linear term N(ψ(ϑ,τ))=∂2∂ϑ2(ψ2(ϑ,τ))−∂∂ϑ(4ϑψ2(ϑ,τ)).
The general NAAM solution of Eq (4.38), is assume as
ψ(ϑ,τ)=∞∑k=0ψk(ϑ,τ). | (4.41) |
For investigating the approximate solution of Eq (4.40), we process as
∂δ∂τδ(ψ(ϑ,τ))=λ[∂∂ϑ(ϑ3ψ(ϑ,τ))+N(ψ(ϑ,τ))], δ∈(0,1], | (4.42) |
The assume solution of Eq (4.42), is in the form
ψλ(ϑ,τ)=∞∑k=0λkψk(ϑ,τ). | (4.43) |
Using the Caupto-Riemann property on Eq (4.42), we have
ψλ(ϑ,τ)=ψ(ϑ,0)+λIδt[−∂∂ϑ(ϑψ(ϑ,τ))+∂2∂ϑ2(ϑ22ψ(ϑ,τ))], | (4.44) |
assuming the initial condition 4.39, Eq (4.44), become as
ψλ(ϑ,τ)=ϑ2+λIδt[∂∂ϑ(ϑ3ψ(ϑ,τ))+N(ψ(ϑ,τ))], | (4.45) |
by substituting Eq (4.43), in Eq (4.45), we get
∞∑k=0λkψk(ϑ,τ)=ϑ2+λIδt[∞∑k=0λk(∂∂ϑ(ϑ3ψ(ϑ,τ)))+∞∑k=0λk(N(ψ(ϑ,τ)))], | (4.46) |
the nonlinear operator N(ψ(ϑ,τ)) is evaluated by using definition (3.4).
The iterative scheme is obtained by comparing the identical power of λ in Eq (4.46), as
{ψ0(ϑ,τ)=ϑ2,ψ1(ϑ,τ)=L∂∂ϑτ(ϑ3ψ0(ϑ,τ))+χ−δτ0,ψk(ϑ,τ)=L−δτ∂∂ϑ(ϑ3ψk−1(ϑ,τ))+χ−δτ(k−1), k=2,3,⋯. | (4.47) |
Consequently, we get
ψ0(ϑ,τ)=ϑ2, | (4.48) |
ψ1(ϑ,τ)=ϑ2tδΓ(δ+1), | (4.49) |
ψ2(ϑ,τ)=ϑ2t2δΓ(2δ+1), | (4.50) |
ψ3(ϑ,τ)=ϑ2t3δΓ(3δ+1), | (4.51) |
⋮. |
The NAAM solution is
ψ(ϑ,τ)=ψ0(ϑ,τ)+ψ1(ϑ,τ)+ψ2(ϑ,τ)+ψ3(ϑ,τ)+⋯, | (4.52) |
subsisting Eqs (4.48)–(4.51), in Eq (4.52), we get
ψ(ϑ,τ)=ϑ2+ϑ2tδΓ(δ+1)+ϑ2t2δΓ(2δ+1)+ϑ2t3δΓ(3δ+1)+⋯. | (4.53) |
Specifically for δ=1, the solution converted to
ψ(ϑ,τ)=ϑ2+ϑ2tΓ(2)+ϑ2t2Γ(3)+ϑ2t3Γ(4)+⋯ | (4.54) |
which converge to exact solution as
ψ(ϑ,τ)=ϑ2expτ. | (4.55) |
Problem 4.4.
Consider a Fokker-Planck equation of time fractional order in the form [36];
∂∂τδ(ψ(ϑ,τ))−∂∂ϑψ(ϑ,τ)−∂2∂ϑ2ψ(ϑ,τ)=0,τ>0, δ∈(0,1], | (4.56) |
with initial source
ψ(ϑ,0)=ϑ, | (4.57) |
for special value δ=1, the exact form solution is
ψ(ϑ,τ)=ϑ+τ. |
To investigate the solution of the Fokker-Planck equation (4.56), we compare it with the Eq (3.1), we get
∂δ∂τδ(ψ(ϑ,τ))=∂∂ϑψ(ϑ,τ)+∂2∂ϑ2ψ(ϑ,τ), ϑ,τ>0, δ∈(0,1], | (4.58) |
the general NAAM solution of Eq (4.58), is assume as
ψ(ϑ,τ)=∞∑k=0ψk(ϑ,τ). | (4.59) |
For investigating the approximate solution of Eq (4.59), we process as
∂δ∂τδ(ψ(ϑ,τ))=λ[∂∂ϑψ(ϑ,τ)+∂2∂ϑ2ψ(ϑ,τ)], δ∈(0,1], | (4.60) |
where initial condition, given as
ψ(ϑ,0)=ϑ. | (4.61) |
The assume solution of Eq (4.60), is in the form
ψλ(ϑ,τ)=∞∑k=0λkψk(ϑ,τ). | (4.62) |
Using the Caupto-Riemann property on Eq (4.60), we have
ψλ(ϑ,τ)=ψ(ϑ,0)+λIδt[∂∂ϑψ(ϑ,τ)+∂2∂ϑ2ψ(ϑ,τ)], | (4.63) |
assuming the initial condition (4.57), Eq (4.63), become as
ψλ(ϑ,τ)=ϑ+λIδt[∂∂ϑψ(ϑ,τ)+∂2∂ϑ2ψ(ϑ,τ)], | (4.64) |
by substituting Eq (4.62), in Eq (4.64), we get
∞∑k=0λkψk(ϑ,τ)=ϑ2+λIδt[∞∑k=0λk(∂∂ϑψ(ϑ,τ))+∞∑k=0λk(∂2∂ϑ2ψ(ϑ,τ))], | (4.65) |
the iterative scheme is obtained by comparing the identical power of λ in Eq (4.65), as
{ψ0(ϑ,τ)=ϑ,ψ1(ϑ,τ)=I−δτ[∂∂ϑψ0(ϑ,τ)+∂2∂ϑ2ψ0(ϑ,τ)],ψk(ϑ,τ)=I−δτ[∂∂ϑψk−1(ϑ,τ)+∂2∂ϑ2ψk−1(ϑ,τ)], k=2,3,⋯ | (4.66) |
Consequently, we get
ψ0(ϑ,τ)=ϑ, | (4.67) |
ψ1(ϑ,τ)=tδΓ(δ+1), | (4.68) |
ψ2(ϑ,τ)=0, | (4.69) |
ψ3(ϑ,τ)=0, | (4.70) |
⋮. |
The NAAM solution is
ψ(ϑ,τ)=ψ0(ϑ,τ)+ψ1(ϑ,τ)+ψ2(ϑ,τ)+ψ3(ϑ,τ)+⋯, | (4.71) |
subsisting Eqs (4.67)–(4.70), in Eq (4.71), we get
ψ(ϑ,τ)=ϑ+tδΓ(δ+1)+0+0+⋯. | (4.72) |
Specifically for δ=1, the solution converted to
ψ(ϑ,τ)=ϑ+τΓ(2), | (4.73) |
which converge to exact solution as
ψ(ϑ,τ)=ϑ+τ. | (4.74) |
Problem 4.5.
Consider a Fokker-Planck equation of time fractional order in the form [36];
∂δ∂τδ(ψ(ϑ,τ))−(1−ϑ)∂∂ϑψ(ϑ,τ)−(eτϑ2)∂2∂ϑ2ψ(ϑ,τ)=0, τ>0, δ∈(0,1], | (4.75) |
with initial source
ψ(ϑ,0)=1+ϑ, | (4.76) |
for special value δ=1, the exact form solution is
ψ(ϑ,τ)=expτ(1+ϑ). |
To investigate the solution of the Fokker-Planck equation (4.75), we compare it with the Eq (3.1), we get
∂δ∂τδ(ψ(ϑ,τ))=(1−ϑ)∂∂ϑψ(ϑ,τ)+(eτϑ2)∂2∂ϑ2ψ(ϑ,τ), ϑ,τ>0, δ∈(0,1], | (4.77) |
the general NAAM solution of Eq (4.75), is assume as
ψ(ϑ,τ)=∞∑k=0ψk(ϑ,τ). | (4.78) |
For investigating the approximate solution of Eq (4.77), we process as
∂δ∂τδ(ψ(ϑ,τ))=λ[(1−ϑ)∂∂ϑψ(ϑ,τ)+(eτϑ2)∂2∂ϑ2ψ(ϑ,τ)], δ∈(0,1], | (4.79) |
where initial condition, given as
ψ(ϑ,0)=ϑ+1. | (4.80) |
The assume solution of Eq (4.79), is in the form
ψλ(ϑ,τ)=∞∑k=0λkψk(ϑ,τ). | (4.81) |
Using the Caupto-Riemann property on Eq (4.79), we have
ψλ(ϑ,τ)=ψ(ϑ,0)+λIδt[(1−ϑ)∂∂ϑψ(ϑ,τ)+(eτϑ2)∂2∂ϑ2ψ(ϑ,τ)], | (4.82) |
assuming the initial condition, Eq (4.82), become as
ψλ(ϑ,τ)=1+ϑ+λIδt[(1−ϑ)∂∂ϑψ(ϑ,τ)+(eτϑ2)∂2∂ϑ2ψ(ϑ,τ)], | (4.83) |
by substituting Eq (4.81), in Eq (4.83), we get
∞∑k=0λkψk(ϑ,τ)=1+ϑ+λIδt[∞∑k=0λk((1−ϑ)∂∂ϑψ(ϑ,τ))+∞∑k=0λk((eτϑ2)∂2∂ϑ2ψ(ϑ,τ))], | (4.84) |
the iterative scheme is obtained by comparing the identical power of λ in Eq (4.84), as
{ψ0(ϑ,τ)=1+ϑ,ψ1(ϑ,τ)=I−δτ[(1−ϑ)∂∂ϑψ0(ϑ,τ)+(eτϑ2)∂2∂ϑ2ψ0(ϑ,τ)],ψk(ϑ,τ)=I−δτ[(1−ϑ)∂∂ϑψk−1(ϑ,τ)+(eτϑ2)∂2∂ϑ2ψk−1(ϑ,τ)], k=2,3,⋯ | (4.85) |
Consequently, we get
ψ0(ϑ,τ)=1+ϑ, | (4.86) |
ψ1(ϑ,τ)=(1+ϑ)τδΓ(δ+1), | (4.87) |
ψ2(ϑ,τ)=(1+ϑ)τ2δΓ(2δ+1), | (4.88) |
ψ3(ϑ,τ)=(1+ϑ)τ3δΓ(3δ+1), | (4.89) |
⋮. |
The NAAM solution is
ψ(ϑ,τ)=ψ0(ϑ,τ)+ψ1(ϑ,τ)+ψ2(ϑ,τ)+ψ3(ϑ,τ)+⋯, | (4.90) |
subsisting Eqs (4.86)–(4.89), in Eq (4.90), we get
ψ(ϑ,τ)=1+ϑ+(1+ϑ)tδΓ(δ+1)+(1+ϑ)τ2δΓ(2δ+1)+(1+ϑ)τ3δΓ(3δ+1)+⋯. | (4.91) |
Specifically for δ=1, the solution converted to
ψ(ϑ,τ)=1+ϑ+(1+ϑ)τΓ(2)+(1+ϑ)τ2Γ(3)+(1+ϑ)τ3Γ(4)+⋯, | (4.92) |
which converge to exact solution as
ψ(ϑ,τ)=expτ(1+ϑ). | (4.93) |
Figures 1 and 2 show the comparison of exact and NAAM solutions while Figures 3 and 4 represent the 2D and 3D solutions graphs respectively of Problem 4.1 at different fractional orders. Figures 5 and 6 show the comparison of exact and NAAM solutions while Figures 7 and 8 represent the 2D and 3D solutions graphs respectively of Problem 4.2 at different fractional orders. Figures 9 and 10 show the comparison of exact and NAAM solutions while Figures 11 and 12 represent the 2D and 3D solutions graphs respectively of Problem 4.3 at different fractional orders. Figures 13 and 14 show the comparison of exact and NAAM solutions while Figures 15 and 16 represent the 2D and 3D solutions graphs respectively of Problem 4.4 at different fractional orders. Figures 17 and 18 show the comparison of exact and NAAM solutions while Figures 19 and 20 represent the 2D and 3D solutions graphs respectively of Problem 4.5 at different fractional orders. Table 1, display the solutions comparison of NAAM with HPM, ADM and exact solutions of Problem 4.2. Table 2, display the solutions comparison of NAAM with HPM, ADM and exact solutions of Problem 4.3. Table 3, display the solutions comparison of NAAM with HPM, ADM and exact solutions of Problem 4.4. Table 4, display the solutions comparison of NAAM with HPM, ADM and exact solutions of Problem 4.5.
τ | ϑ | NAAM | HPM [33] | ADM [34] | Exact |
0.2 | 0.25 | 0.305333 | 0.305333 | 0.305333 | 0.305351 |
0.2 | 0.50 | 0.610667 | 0.610667 | 0.610667 | 0.610701 |
0.2 | 0.75 | 0.916000 | 0.916000 | 0.916000 | 0.916052 |
0.2 | 1.00 | 1.221333 | 1.221333 | 1.221333 | 1.221403 |
0.4 | 0.25 | 0.372667 | 0.372667 | 0.372667 | 0.372956 |
0.4 | 0.50 | 0.745333 | 0.745333 | 0.745333 | 0.745912 |
0.4 | 0.75 | 1.118000 | 1.118000 | 1.118000 | 1.118869 |
0.4 | 1.00 | 1.490667 | 1.490667 | 1.490667 | 1.491825 |
0.6 | 0.25 | 0.454000 | 0.454000 | 0.454000 | 0.455530 |
0.6 | 0.50 | 0.908000 | 0.908000 | 0.908000 | 0.911059 |
0.6 | 0.75 | 1.362000 | 1.362000 | 1.362000 | 1.366589 |
0.6 | 1.00 | 1.816000 | 1.816000 | 1.816000 | 1.822119 |
τ | ϑ | NAAM | HPM [33] | ADM [34] | Exact |
0.2 | 0.25 | 0.076333 | 0.076333 | 0.076333 | 0.076338 |
0.50 | 0.305333 | 0.305333 | 0.305333 | 0.305351 | |
0.75 | 0.687000 | 0.687000 | 0.687000 | 0.687039 | |
1.00 | 1.221333 | 1.221333 | 1.221333 | 1.221403 | |
0.4 | 0.25 | 0.093167 | 0.093167 | 0.093167 | 0.093239 |
0.50 | 0.372667 | 0.372667 | 0.372667 | 0.372956 | |
0.75 | 0.838500 | 0.838500 | 0.838500 | 0.839151 | |
1.00 | 1.490667 | 1.490667 | 1.490667 | 1.491825 | |
0.6 | 0.25 | 0.113500 | 0.113500 | 0.11350 | 0 0.113882 |
0.50 | 0.454000 | 0.454000 | 0.454000 | 0.455530 | |
0.75 | 1.021500 | 1.021500 | 1.021500 | 1.024942 | |
1.00 | 1.816000 | 1.816000 | 1.816000 | 1.822119 |
In these research notes, we have applied an analytical procedure known as the new approximate analytical method (NAAM). The NAAM provides power series solutions in the form of convergent series with fractional derivatives. It is more powerful than other analytical methods due to its less computational work. It provides the fractional order solution directly using the Caputo-Riemann property. We have tested some Fokker-Planck equations in linear and non-linear cases. The series form solution and graphical representation reflect the applicability and validity. The main advantage of NAAM is that it significantly minimises the numerical computations required to find an analytical solution with fractional order. The fractional order solutions are also found and verified by 3D and 2D representation. Overall, the NAAM provides series form solution with fractional order, which have good agreement with the exact solutions of the problems.
Researchers Supporting Project number (RSP-2021/401), King Saud University, Riyadh, Saudi Arabia. This research was funded by National Science, Research and Innovation Fund (NSRF), and King Mongkut's University of Technology North Bangkok with Contract no. KMUTNB-FF-65-24.
The authors declare that they have no competing interests.
[1] | L. A. Zadeh, Fuzzy sets, Inform. Control, 8 (1965), 338–353. |
[2] | K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Set. Syst., 20 (1986), 87–96. |
[3] |
W. Wang, X. Liu, Intuitionistic fuzzy geometric aggregation operators based on Einstein operations, Int. J. Intell. Sys., 26 (2011), 1049–1075. https://doi.org/10.1109/TFUZZ.2013.2278989 doi: 10.1109/TFUZZ.2013.2278989
![]() |
[4] |
R. R. Yager, Pythagorean membership grades in multi-criteria decision making, IEEE T. Fuzzy Syst., 22 (2013), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989 doi: 10.1109/TFUZZ.2013.2278989
![]() |
[5] |
F. Xiao, W. Ding, Divergence measure of Pythagorean fuzzy sets and its application in medical diagnosis, Appl. Soft Comput., 79 (2019), 254–267. https://doi.org/10.1016/j.asoc.2019.03.043 doi: 10.1016/j.asoc.2019.03.043
![]() |
[6] |
N. X. Thao, F. Smarandache, A new fuzzy entropy on Pythagorean fuzzy sets, J. Intell. Fuzzy Syst., 37 (2019), 1065–1074. https://doi.org/10.3233/JIFS-182540 doi: 10.3233/JIFS-182540
![]() |
[7] |
Q. Zhang, J. Hu, J. Feng, A. Liu, Y. Li, New similarity measures of Pythagorean fuzzy sets and their applications, IEEE Access, 7 (2019), 138192–138202. https://doi.org/10.1109/ACCESS.2019.2942766 doi: 10.1109/ACCESS.2019.2942766
![]() |
[8] |
K. Rahman, S. Abdullah, R. Ahmed, M. Ullah, Pythagorean fuzzy Einstein weighted geometric aggregation operator and their application to multiple attribute group decision making, J. Intell. Fuzzy Syst., 33 (2017), 635–647. https://doi.org/10.3233/JIFS-16797 doi: 10.3233/JIFS-16797
![]() |
[9] |
M. Lin, J. Wei, Z. Xu, R. Chen, Multiattribute group decision-making based on linguistic pythagorean fuzzy interaction partitioned bonferroni mean aggregation operators, Complexity, 2018 (2018), 9531064. https://doi.org/10.1155/2018/9531064 doi: 10.1155/2018/9531064
![]() |
[10] |
X. Zhang, Z. Xu, Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets, Int. J. Intell. Syst., 29 (2014), 1061–1078. https://doi.org/10.1002/int.21676 doi: 10.1002/int.21676
![]() |
[11] |
G. Wei, M. Lu, Pythagorean fuzzy power aggregation operators in multiple attribute decision making, Int. J. Intell. Syst., 33 (2018), 169–186. https://doi.org/10.1002/int.21946 doi: 10.1002/int.21946
![]() |
[12] |
L. Wang, N. Li, Pythagorean fuzzy interaction power Bonferroni means aggregation operators in multiple attribute decision making, Int. J. Intell. Syst., 35 (2020), 150–183. https://doi.org/10.1002/int.22204 doi: 10.1002/int.22204
![]() |
[13] |
M. Lin, W. Xu, Z. Lin, R. Chen, Determine OWA operator weights using kernel density estimation, Econ. Res. Ekon. Istraz., 33 (2020), 1441–1464. https://doi.org/10.1080/1331677X.2020.1748509 doi: 10.1080/1331677X.2020.1748509
![]() |
[14] |
X. Zhang, A novel approach based on similarity measure for Pythagorean fuzzy multiple criteria group decision making. Int. J. Intell. Syst., 31 (2016), 593–611. https://doi.org/10.1002/int.21796 doi: 10.1002/int.21796
![]() |
[15] |
X. Peng, H. Yuan, Fundamental properties of Pythagorean fuzzy aggregation operators, Fund. Inform., 147 (2016), 415–446. https://doi.org/10.3233/FI-2016-1415 doi: 10.3233/FI-2016-1415
![]() |
[16] |
M. Lin, X. Li, L. Chen, Linguistic q‐rung orthopair fuzzy sets and their interactional partitioned Heronian mean aggregation operators, Int. J. Intell. Syst., 35 (2020), 217–249. https://doi.org/10.1002/int.22136 doi: 10.1002/int.22136
![]() |
[17] | R. M. Zulqarnain, X. L. Xin, M. Saqlain, F. Smarandache, M. I. Ahamad, An integrated model of neutrosophic TOPSIS with application in multi-criteria decision-making problem, Neutrosophic Sets Sy., 40 (2021), 253–269. |
[18] |
M. Lin, X. Li, R. Chen, H. Fujita, J. Lin, Picture fuzzy interactional partitioned Heronian mean aggregation operators: an application to MADM process, Artif. Intell. Rev., 55 (2022), 1171–1208. https://doi.org/10.1007/s10462-021-09953-7 doi: 10.1007/s10462-021-09953-7
![]() |
[19] | F. Smarandache, Neutrosophy: neutrosophic probability, set, and logic: analytic synthesis & synthetic analysis, American : American Research Press, 1998. |
[20] |
D. Molodtsov, Soft set theory-first results, Comput. Math. Appl., 37 (1999), 19–31. https://doi.org/10.1016/S0898-1221(99)00056-5 doi: 10.1016/S0898-1221(99)00056-5
![]() |
[21] |
P. K. Maji, R. Biswas, A. R. Roy, Soft set theory, Comput. Math. Appl., 45 (2003), 555–562. https://doi.org/10.1016/S0898-1221(03)00016-6 doi: 10.1016/S0898-1221(03)00016-6
![]() |
[22] | N. Cagman, S. Enginoglu, FP-soft set theory and its applications, Ann. Fuzzy Math. Inform., 2 (2011), 219–226. |
[23] |
M. I. Ali, F. Feng, X. Liu, W. K. Min, M. Shabir, On some new operations in soft set theory, Comput. Math. Appl., 57 (2009), 1547–1553. https://doi.org/10.1016/j.camwa.2008.11.009 doi: 10.1016/j.camwa.2008.11.009
![]() |
[24] | P. K. Maji, R. Biswas, A. R. Roy, Fuzzy soft sets, J. Fuzzy Math., 9 (2001), 589–602. |
[25] |
A. R. Roy, P. K. Maji, A fuzzy soft set theoretic approach to decision making problems, J. Comput. Appl. Math., 203 (2007), 412–418. https://doi.org/10.1016/j.cam.2006.04.008 doi: 10.1016/j.cam.2006.04.008
![]() |
[26] | N. Cagman, S. Enginoglu, F. Citak, Fuzzy soft set theory and its applications, Iran. J. Fuzzy Syst., 8 (2011), 137–147. |
[27] |
F. Feng, Y. B. Jun, X. Liu, L. Li, An adjustable approach to fuzzy soft set based decision making, J. Comput. Appl. Math., 234 (2010), 10–20. https://doi.org/10.1016/j.cam.2009.11.055 doi: 10.1016/j.cam.2009.11.055
![]() |
[28] | P. K. Maji, R. Biswas, A. Roy, Intuitionistic fuzzy soft sets, J. Fuzzy Math., 9 (2001), 677–692. |
[29] | R. Arora, H. Garg, A robust aggregation operators for multi-criteria decision-making with intuitionistic fuzzy soft set environment, Sci. Iran., 25 (2018), 931–942. |
[30] |
N. Çağman, S. Karataş, Intuitionistic fuzzy soft set theory and its decision making, J. Intell. Fuzzy Syst., 24 (2013), 829–836. https://doi.org/10.3233/IFS-2012-0601 doi: 10.3233/IFS-2012-0601
![]() |
[31] |
P. Muthukumar, G. S. S. Krishnan, A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis, Appl. Soft Comput., 41 (2016), 148–156. https://doi.org/10.1016/j.asoc.2015.12.002 doi: 10.1016/j.asoc.2015.12.002
![]() |
[32] | X. D. Peng, Y. Yang, J. Song, Y. Jiang, Pythagorean fuzzy soft set and its application, Comput Eng., 41 (2015), 224–229. |
[33] |
R. M. Zulqarnain, X. L. Xin, H. Garg, W. A. Khan, Aggregation operators of pythagorean fuzzy soft sets with their application for green supplier chain management, J. Intell. Fuzzy Syst., 40 (2021), 5545–5563. https://doi.org/10.3233/JIFS-202781 doi: 10.3233/JIFS-202781
![]() |
[34] |
T. M. Athira, S. J. John, H. Garg, Entropy and distance measures of pythagorean fuzzy soft sets and their applications, J. Intell. Fuzzy Syst., 37((2019), 4071–4084. https://doi.org/10.3233/JIFS-190217 doi: 10.3233/JIFS-190217
![]() |
[35] |
R. M. Zulqarnain, I. Siddique, F. Jarad, Y. S. Hamed, K. M. Abualnaja, A. Iampan, Einstein aggregation operators for Pythagorean fuzzy soft sets with their application in multiattribute group decision-making, J. Funct. Space., 2022 (2022), 21. https://doi.org/10.1155/2022/1358675 doi: 10.1155/2022/1358675
![]() |
[36] |
T. M. Athira, S. J. John, H. Garg, A novel entropy measure of Pythagorean fuzzy soft sets, AIMS Math., 5 (2020), 1050–1061. https://doi.org/10.3934/math.20200073 doi: 10.3934/math.20200073
![]() |
[37] |
R. M. Zulqarnain, I. Siddique, S. Ahmad, A. Iampan, G. Jovanov, Đ. Vranješ, et al., Pythagorean fuzzy soft Einstein ordered weighted average operator in sustainable supplier selection problem, Math. Probl. Eng., 2021 (2021), 16. https://doi.org/10.1155/2021/2559979 doi: 10.1155/2021/2559979
![]() |
[38] |
R. M. Zulqarnain, I. Siddique, S. EI-Morsy, Einstein-ordered weighted geometric operator for Pythagorean fuzzy soft set with its application to solve MAGDM problem, Math. Probl. Eng., 2022 (2022), 14. https://doi.org/10.1155/2022/5199427 doi: 10.1155/2022/5199427
![]() |
[39] |
K. Naeem, M. Riaz, X. Peng, D. Afzal, Pythagorean fuzzy soft MCGDM methods based on TOPSIS, VIKOR and aggregation operators, J. Intell. Fuzzy Syst., 37 (2019), 6937–6957. https://doi.org/10.3233/JIFS-190905 doi: 10.3233/JIFS-190905
![]() |
[40] | P. K. Maji, Neutrosophic soft set, Annals Fuzzy Math. Inform., 5 (2013), 157–168. |
[41] |
F. Karaaslan, Possibility neutrosophic soft sets and PNSdecision making method, Appl. Soft Comput. J., 54 (2016), 403–414. https://doi.org/10.1016/j.asoc.2016.07.013 doi: 10.1016/j.asoc.2016.07.013
![]() |
[42] |
S. Broumi, Generalized neutrosophic soft set, Int. J. Comput. Sci. Eng. Inform. Tec., 3 (2013). https://doi.org/10.5121/ijcseit.2013.3202 doi: 10.5121/ijcseit.2013.3202
![]() |
[43] |
I. Deli, Y. Subas, A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems, Int. J. Mach. Learn. Cyb., 8 (2017), 1309–1322. https://doi.org/10.1007/s13042-016-0505-3 doi: 10.1007/s13042-016-0505-3
![]() |
[44] | H. Wang, F. Smarandache, Y. Zhang, Single valued neutrosophic sets, Shanghai: Infinite Study, 2010. |
[45] |
J. Ye, A multi-criteria decision-making method using aggregation operators for simplified neutrosophic sets, J. Intell. Fuzzy Syst., 26 (2014), 2459–2466. https://doi.org/10.3233/IFS-130916 doi: 10.3233/IFS-130916
![]() |
[46] | F. Smarandache, Extension of soft set to Hypersoft set, and then to plithogenic Hypersoft set, Neutrosophic Set. Sy., 22 (2018), 168–170. |
[47] |
A. U. Rahman, M. Saeed, H. A. E. W. Khalifa, W. A. Afifi, Decision making algorithmic techniques based on aggregation operations and similarity measures of possibility intuitionistic fuzzy hypersoft sets, AIMS Math., 7 (2022), 3866–3895. https://doi.org/10.3934/math.2022214 doi: 10.3934/math.2022214
![]() |
[48] |
R. M. Zulqarnain, X. L. Xin, M. Saeed, Extension of TOPSIS method under intuitionistic fuzzy hypersoft environment based on correlation coefficient and aggregation operators to solve decision making problem, AIMS Math., 6 (2020), 2732–2755. https://doi.org/10.3934/math.2021153 doi: 10.3934/math.2021153
![]() |
[49] | R. M. Zulqarnain, X. L. Xin, M. Saeed, A development of Pythagorean fuzzy hypersoft set with basic operations and decision-making approach based on the correlation coefficient, Theor. Appl. Hypersoft Set, 40 (2021), 149–168. |
[50] |
I. Siddique, R. M. Zulqarnain, R. Ali, F. Jarad, A. Iampan, Multicriteria decision-making approach for aggregation operators of pythagorean fuzzy hypersoft sets, Comput. Intell. Neurosci., 2021 (2021), 19. https://doi.org/10.1155/2021/2036506 doi: 10.1155/2021/2036506
![]() |
[51] |
P. Sunthrayuth, F. Jarad, J. Majdoubi, R. M. Zulqarnain, A. Iampan, I. Siddique, A novel multicriteria decision-making approach for einstein weighted average operator under Pythagorean fuzzy hypersoft environment, J. Math., 2022 (2022), 24. https://doi.org/10.1155/2022/1951389 doi: 10.1155/2022/1951389
![]() |
[52] |
R. M. Zulqarnain, I. Siddique, R. Ali, F. Jarad, A. Iampan, Einstein weighted geometric operator for Pythagorean fuzzy hypersoft with its application in material selection, CMES-Comput. Model. Engin. Sci., 135 (2022), 2557–2583.https://doi.org/10.32604/cmes.2023.023040 doi: 10.32604/cmes.2023.023040
![]() |
[53] |
R. M. Zulqarnain, I. Siddique, R. Ali, J. Awrejcewicz, H. Karamti, D. Grzelczyk, et al., Einstein ordered weighted aggregation operators for Pythagorean fuzzy hypersoft set with its application to solve MCDM problem, IEEE Access, 10 (2022), 95294–95320. https://doi.org/10.1109/ACCESS.2022.3203717 doi: 10.1109/ACCESS.2022.3203717
![]() |
[54] | S. Khan, M. Gulistan, H. A. Wahab, Development of the structure of q-Rung orthopair fuzzy hypersoft set with basic operations, Punjab Uni. J. Math., 53 (2021), 881–892. |
[55] |
S. H. Gurmani, H. Chen, Y. Bai, Extension of TOPSIS method under q-Rung orthopair fuzzy hypersoft environment based on correlation coefficients and its applications to multi-attribute group decision making, Int. J. Fuzzy Syst., 2022. https://doi.org/10.1007/s40815-022-01386-w doi: 10.1007/s40815-022-01386-w
![]() |
[56] |
S. Khan, M. Gulistan, N. Kausar, S. Kousar, D. Pamucar, G. M. Addis, Analysis of cryptocurrency market by using q-Rung orthopair fuzzy hypersoft set algorithm based on aggregation operator, Complexity, 2022 (2022), 7257449. https://doi.org/10.1155/2022/7257449 doi: 10.1155/2022/7257449
![]() |
[57] |
K. Alkaradaghi, S. S. Ali, N. Al-Ansari, J. Laue, A. J. S. Chabuk, Landfill site selection using MCDM methods and GIS in the sulaimaniyah governorate, Sustainability, 11 (2019), 4530. https://doi.org/10.3390/su11174530 doi: 10.3390/su11174530
![]() |
[58] |
Z. Hameed, Z. Aman, S. R. Naqvi, R. Tariq, I. Ali, A. A. J. E. Makki, Kinetic and thermodynamic analyses of sugar cane bagasse and sewage sludge co-pyrolysis process, Energ Fuels., 32 (2018), 9551–9558. https://doi.org/10.1021/acs.energyfuels.8b01972 doi: 10.1021/acs.energyfuels.8b01972
![]() |
[59] |
S. R. Naqvi, M. Naqvi, S. A. A. Taqvi, F. Iqbal, A. Inayat, A. H. Khoja, et al., Agro-industrial residue gasification feasibility in captive power plants: a South-Asian case study, Energy, 214 (2020), 118952. https://doi.org/10.1016/j.energy.2020.118952 doi: 10.1016/j.energy.2020.118952
![]() |
[60] |
U. N. Ngoc, H. Schnitzer, Sustainable solutions for solid waste management in southeast Asian countries, Waste Manag., 29 (2009), 1982–1995. https://doi.org/10.1016/j.wasman.2008.08.031 doi: 10.1016/j.wasman.2008.08.031
![]() |
[61] |
M. Naqvi, J. Yan, E. Dahlquist, S. R. Naqvi, Waste biomass gasification based off-grid electricity generation: a case study in Pakistan, Energy Procedia, 103 (2016), 406–412. https://doi.org/10.1016/j.egypro.2016.11.307 doi: 10.1016/j.egypro.2016.11.307
![]() |
[62] |
S. R. Naqvi, Recent developments on biomass utilization for bioenergy production in Pakistan, Sci. P. Ser., 2 (2020), 156–160. https://doi.org/10.31580/sps.v2i2.1461 doi: 10.31580/sps.v2i2.1461
![]() |
[63] |
Z. Hameed, S. R. Naqvi, M. Naqvi, I. Ali, S. A. A. Taqvi, N. Gao, et al., A comprehensive review on thermal coconversion of biomass, sludge, coal, and their blends using thermogravimetric analysis, J. Chem., 2020 (2020), 23, https://doi.org/10.1155/2020/5024369 doi: 10.1155/2020/5024369
![]() |
[64] |
G. Mondelli, H. L. Giacheti, M. E. G. Boscov, V. R. Elis, J. Hamada, Geoenvironmental site investigation using different techniques in a municipal solid waste disposal site in Brazil, Environ. Geol., 52 (2007), 871–887. https://doi.org/10.1007/s00254-006-0529-1 doi: 10.1007/s00254-006-0529-1
![]() |
[65] |
M. Naqvi, E. Dahlquist, A. S. Nizami, M. Danish, S. Naqvi, U. Farooq, et al., Gasification integrated with small chemical pulp mills for fuel and energy production, Energy Procedia, 142 (2017), 977–983. https://doi.org/10.1016/j.egypro.2017.12.156 doi: 10.1016/j.egypro.2017.12.156
![]() |
[66] |
M. Barzehkar, N. M. Dinan, S. Mazaheri, R. M. Tayebi, G. I. Brodie, Landfill site selection using GIS-based multi-criteria evaluation (case study: SaharKhiz region located in Gilan Province in Iran), SN Appl. Sci., 1 (2019), 1082. https://doi.org/10.1007/s42452-019-1109-9 doi: 10.1007/s42452-019-1109-9
![]() |
[67] |
I. Kamdar, S. Ali, A. Bennui, K. Techato, W. Jutidamrongphan, Municipal solid waste landfill siting using an integrated GIS-AHP approach: a case study from Songkhla, Thailand, Resour. Conserv. Recycl., 149 (2019), 220–235. https://doi.org/10.1016/j.resconrec.2019.05.027 doi: 10.1016/j.resconrec.2019.05.027
![]() |
[68] |
O. Basar, O. S. Cevik, K. Cengiz, Waste disposal location selection by using pythagorean fuzzy REGIME method, J. Intell. Fuzzy Syst., 42 (2022), 401–410. https://doi.org/10.3233/JIFS-219199 doi: 10.3233/JIFS-219199
![]() |
[69] |
P. Li, J. Liu, C. Wei, A dynamic decision making method based on GM(1, 1) model with Pythagorean fuzzy numbers for selecting waste disposal enterprises, Sustainability, 11 (2019), 5557. https://doi.org/10.3390/su11205557 doi: 10.3390/su11205557
![]() |
[70] |
Y. Ren, X. Yuan, R. Lin, A novel MADM algorithm for landfill site selection based on q-rung orthopair probabilistic hesitant fuzzy power Muirhead mean operator, PLoS One, 16 (2021), e0258448. https://doi.org/10.1371/journal.pone.0258448 doi: 10.1371/journal.pone.0258448
![]() |
[71] |
A. Karasan, E. Bolturk, Solid waste disposal site selection by using neutrosophic combined compromise solution method, Atlantis Studies in Uncertainty Modelling, 11th Conference of the European Society for Fuzzy Logic and Technology, 1 (2019), 416–422. https://doi.org/10.2991/eusflat-19.2019.58 doi: 10.2991/eusflat-19.2019.58
![]() |
[72] |
N. B. Chang, G. Parvathinathan, J. B. Breeden, Combining GIS with fuzzy multi-criteria decision-making for landfill siting in a fast-growing urban region, J. Environ. Manage., 87 (2008), 139–153. https://doi.org/10.1016/j.jenvman.2007.01.011 doi: 10.1016/j.jenvman.2007.01.011
![]() |
[73] | V. Akbari, M. A. Rajabi, S. H. Chavoshi, R. Shams, Landfill site selection by combining GIS and fuzzy multi criteria decision analysis, case study: Bandar Abbas, Iran, World Appl. Sci. J., 3 (2008), 39–47. |
[74] | R. M. Hasan, K. Tetsuo, A. S. Islam, Landfill demand and allocation for municipal solid waste disposal in Dhaka city-an assessment in a GIS environment, J. Civil Eng., 37 (2009), 133–149. |
[75] |
H. Ersoy, F. Bulut, Spatial and multi-criteria decision analysis-based methodology for landfill site selection in growing urban regions, Waste Manag. Res., 27 (2009), 489–500. https://doi.org/10.1177/0734242X08098430 doi: 10.1177/0734242X08098430
![]() |
[76] | G. Khanlari, Y. Abdilor, R. Babazadeh, Y. Mohebi, Land fill site selection for municipal solid waste management using GSI method, Malayer, Iran, Adv. Environ. Biol., 6 (2012), 886–894. |
[77] | Y. Wind, T. L. Saaty, Marketing applications of the analytic hierarchy process, Manage. Sci., 26 (1980), 641–658. |
[78] | A. Mussa, K. V. Suryabhagavan, Solid waste dumping site selection using GIS-based multi-criteria spatial modeling: a case study in Logia town, Afar region, Ethiopia, Geo. Ecol. Landsc., 5 (2021), 186–198. |
[79] |
P. V. Gorsevski, K. R. Donevska, C. D. Mitrovski, J. P. Frizado, Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average, Waste Manage., 32 (2012), 287–296. https://doi.org/10.1016/j.wasman.2011.09.023 doi: 10.1016/j.wasman.2011.09.023
![]() |
[80] |
C. Kahraman, S. Cebi, S. C. Onar, B. Oztaysi, A novel trapezoidal intuitionistic fuzzy information axiom approach: an application to multi-criteria landfill site selection, Eng. Appl. Artif. Intell., 67 (2018), 157–172. https://doi.org/10.1016/j.engappai.2017.09.009 doi: 10.1016/j.engappai.2017.09.009
![]() |
1. | Muhammad Imran Liaqat, Adnan Khan, Hafiz Muhammad Anjum, Gregory Abe-I-Kpeng, Emad E. Mahmoud, S. A. Edalatpanah, A Novel Efficient Approach for Solving Nonlinear Caputo Fractional Differential Equations, 2024, 2024, 1687-9120, 10.1155/2024/1971059 | |
2. | Yufeng Zhang, Lizhen Wang, Application of Laplace Adomian decomposition method for fractional Fokker-Planck equation and time fractional coupled Boussinesq-Burger equations, 2024, 41, 0264-4401, 793, 10.1108/EC-06-2023-0275 | |
3. | Qasim Khan, Anthony Suen, Hassan Khan, Application of an efficient analytical technique based on Aboodh transformation to solve linear and non-linear dynamical systems of integro-differential equations, 2024, 11, 26668181, 100848, 10.1016/j.padiff.2024.100848 | |
4. | Zareen A. Khan, Muhammad Bilal Riaz, Muhammad Imran Liaqat, Ali Akgül, Goutam Saha, A novel technique using integral transforms and residual functions for nonlinear partial fractional differential equations involving Caputo derivatives, 2024, 19, 1932-6203, e0313860, 10.1371/journal.pone.0313860 | |
5. | Saad. Z. Rida, Anas. A. M. Arafa, Hussein. S. Hussein, Ismail Gad Ameen, Marwa. M. M. Mostafa, Application of Second Kind Shifted Chebyshev Residual Power Series Method for Solving Time-Fractional Fokker Planck Models, 2025, 11, 2349-5103, 10.1007/s40819-025-01908-8 |
τ | ϑ | NAAM | HPM [33] | ADM [34] | Exact |
0.2 | 0.25 | 0.305333 | 0.305333 | 0.305333 | 0.305351 |
0.2 | 0.50 | 0.610667 | 0.610667 | 0.610667 | 0.610701 |
0.2 | 0.75 | 0.916000 | 0.916000 | 0.916000 | 0.916052 |
0.2 | 1.00 | 1.221333 | 1.221333 | 1.221333 | 1.221403 |
0.4 | 0.25 | 0.372667 | 0.372667 | 0.372667 | 0.372956 |
0.4 | 0.50 | 0.745333 | 0.745333 | 0.745333 | 0.745912 |
0.4 | 0.75 | 1.118000 | 1.118000 | 1.118000 | 1.118869 |
0.4 | 1.00 | 1.490667 | 1.490667 | 1.490667 | 1.491825 |
0.6 | 0.25 | 0.454000 | 0.454000 | 0.454000 | 0.455530 |
0.6 | 0.50 | 0.908000 | 0.908000 | 0.908000 | 0.911059 |
0.6 | 0.75 | 1.362000 | 1.362000 | 1.362000 | 1.366589 |
0.6 | 1.00 | 1.816000 | 1.816000 | 1.816000 | 1.822119 |
τ | ϑ | NAAM | HPM [33] | ADM [34] | Exact |
0.2 | 0.25 | 0.076333 | 0.076333 | 0.076333 | 0.076338 |
0.50 | 0.305333 | 0.305333 | 0.305333 | 0.305351 | |
0.75 | 0.687000 | 0.687000 | 0.687000 | 0.687039 | |
1.00 | 1.221333 | 1.221333 | 1.221333 | 1.221403 | |
0.4 | 0.25 | 0.093167 | 0.093167 | 0.093167 | 0.093239 |
0.50 | 0.372667 | 0.372667 | 0.372667 | 0.372956 | |
0.75 | 0.838500 | 0.838500 | 0.838500 | 0.839151 | |
1.00 | 1.490667 | 1.490667 | 1.490667 | 1.491825 | |
0.6 | 0.25 | 0.113500 | 0.113500 | 0.11350 | 0 0.113882 |
0.50 | 0.454000 | 0.454000 | 0.454000 | 0.455530 | |
0.75 | 1.021500 | 1.021500 | 1.021500 | 1.024942 | |
1.00 | 1.816000 | 1.816000 | 1.816000 | 1.822119 |
τ | ϑ | NAAM | HPM [33] | ADM [34] | Exact |
0.2 | 0.25 | 0.305333 | 0.305333 | 0.305333 | 0.305351 |
0.2 | 0.50 | 0.610667 | 0.610667 | 0.610667 | 0.610701 |
0.2 | 0.75 | 0.916000 | 0.916000 | 0.916000 | 0.916052 |
0.2 | 1.00 | 1.221333 | 1.221333 | 1.221333 | 1.221403 |
0.4 | 0.25 | 0.372667 | 0.372667 | 0.372667 | 0.372956 |
0.4 | 0.50 | 0.745333 | 0.745333 | 0.745333 | 0.745912 |
0.4 | 0.75 | 1.118000 | 1.118000 | 1.118000 | 1.118869 |
0.4 | 1.00 | 1.490667 | 1.490667 | 1.490667 | 1.491825 |
0.6 | 0.25 | 0.454000 | 0.454000 | 0.454000 | 0.455530 |
0.6 | 0.50 | 0.908000 | 0.908000 | 0.908000 | 0.911059 |
0.6 | 0.75 | 1.362000 | 1.362000 | 1.362000 | 1.366589 |
0.6 | 1.00 | 1.816000 | 1.816000 | 1.816000 | 1.822119 |
τ | ϑ | NAAM | HPM [33] | ADM [34] | Exact |
0.2 | 0.25 | 0.076333 | 0.076333 | 0.076333 | 0.076338 |
0.50 | 0.305333 | 0.305333 | 0.305333 | 0.305351 | |
0.75 | 0.687000 | 0.687000 | 0.687000 | 0.687039 | |
1.00 | 1.221333 | 1.221333 | 1.221333 | 1.221403 | |
0.4 | 0.25 | 0.093167 | 0.093167 | 0.093167 | 0.093239 |
0.50 | 0.372667 | 0.372667 | 0.372667 | 0.372956 | |
0.75 | 0.838500 | 0.838500 | 0.838500 | 0.839151 | |
1.00 | 1.490667 | 1.490667 | 1.490667 | 1.491825 | |
0.6 | 0.25 | 0.113500 | 0.113500 | 0.11350 | 0 0.113882 |
0.50 | 0.454000 | 0.454000 | 0.454000 | 0.455530 | |
0.75 | 1.021500 | 1.021500 | 1.021500 | 1.024942 | |
1.00 | 1.816000 | 1.816000 | 1.816000 | 1.822119 |