ORIGINAL_ARTICLE
Perishable Inventory Model with Retrial Demands, Negative Customers and Multiple Working Vacations
This paper presents the analysis of a continuous review perishable inventory system wherein the life time of each item follows an exponential distribution. The operating policy is (s,S) policy where the ordered items are received after a random time which follows exponential distribution. Primary arrival follows Poisson distribution and they may turnout to be positive or negative and then enter into the orbit. The orbiting demands compete their service according to exponential distribution. The server takes multiple working vacations at zero inventory. We assume that the vacation time, service times both during regular busy period and vacation period are exponentially distributed. Matrix analytic method is used for the steady state distribution of the model. Various performance measures and cost analysis are shown with numerical results.
http://ijm2c.iauctb.ac.ir/article_663719_74a04f952743d47057405a453e8ee2f5.pdf
2017-11-01T11:23:20
2020-04-05T11:23:20
239
254
Perishable inventory
(s,S) policy
Retrial demands
Negative customers
Multiple working vacations
Matrix analytic method
Vijaya Laxmi
Pikkala
vijaya_iit2003@yahoo.co.in
true
1
Department of Applied Mathematics, Andhra University, Visakhapatnam, India. Pin- 530003
Department of Applied Mathematics, Andhra University, Visakhapatnam, India. Pin- 530003
Department of Applied Mathematics, Andhra University, Visakhapatnam, India. Pin- 530003
LEAD_AUTHOR
Soujanya
M.L.
logintosouji@gmail.com
true
2
Department of Applied mathematics, Andhra University, Visakhapatnam, India. 530003
Department of Applied mathematics, Andhra University, Visakhapatnam, India. 530003
Department of Applied mathematics, Andhra University, Visakhapatnam, India. 530003
AUTHOR
ORIGINAL_ARTICLE
A Note on Solving Prandtl's Integro-Differential Equation
A simple method for solving Prandtl's integro-differential equation is proposed based on a new reproducing kernel space. Using a transformation and modifying the traditional reproducing kernel method, the singular term is removed and the analytical representation of the exact solution is obtained in the form of series in the new reproducing kernel space. Compared with known investigations, its advantages are that the representation of the exact solution is obtained in a reproducing kernel Hilbert space and accuracy in numerical computation is higher. On the other hand, the approximate solution and its derivatives converge uniformly to the exact solution and its derivatives. The final numerical experiments illustrate the method is efficient.
http://ijm2c.iauctb.ac.ir/article_663720_6a00cedc563dbd4cbf3740983dbeadfa.pdf
2017-11-01T11:23:20
2020-04-05T11:23:20
255
263
Reproducing Kernel Space
Singular integro-differential equation of Prandtl's type
Hypersingular integral equation
Error estimation
Atta
Dezhbord
dezhbord22.ata@gmail.com
true
1
Department of Mathematics, Hamedan Branch, Islamic Azad University, Hamedan 65138, Iran
Department of Mathematics, Hamedan Branch, Islamic Azad University, Hamedan 65138, Iran
Department of Mathematics, Hamedan Branch, Islamic Azad University, Hamedan 65138, Iran
LEAD_AUTHOR
Taher
Lotfi
lotfi@iauh.ac.ir
true
2
Department of Mathematics, Hamedan Branch, Islamic Azad University, Hamedan 65138, Iran
Department of Mathematics, Hamedan Branch, Islamic Azad University, Hamedan 65138, Iran
Department of Mathematics, Hamedan Branch, Islamic Azad University, Hamedan 65138, Iran
AUTHOR
ORIGINAL_ARTICLE
The Tau-Collocation Method for Solving Nonlinear Integro-Differential Equations and Application of a Population Model
This paper presents a computational technique that called Tau-collocation method for the developed solution of non-linear integro-differential equations which involves a population model. To do this, the nonlinear integro-differential equations are transformed into a system of linear algebraic equations in matrix form without interpolation of non-poly-nomial terms of equations. Then, using collocation points, we solve this system and obtain the unknown coefficients. To illustrate the ability and reliability of the method some nonlinear integro-differential equations and population models are presented. The results reveal that the method is very effective and simple.
http://ijm2c.iauctb.ac.ir/article_663721_353c22ebe2793b8fbeb23498bb5e8212.pdf
2017-11-01T11:23:20
2020-04-05T11:23:20
265
276
Nonlinear integro-differential equation
Tau-Collocation method
Matrix representation
Population model
Collocation point
Atefeh
Armand
atefeh.armand@ymail.com
true
1
Dep. Math, Yadegar imam khomeini (rah) shahre Rey, IAU
Dep. Math, Yadegar imam khomeini (rah) shahre Rey, IAU
Dep. Math, Yadegar imam khomeini (rah) shahre Rey, IAU
LEAD_AUTHOR
Zienab
Gouyandeh
zgouyandeh@yahoo.com
true
2
Dep. Math, Najaf Abad, IAU
Dep. Math, Najaf Abad, IAU
Dep. Math, Najaf Abad, IAU
AUTHOR
ORIGINAL_ARTICLE
Numerical Solution of Nonlinear PDEs by Using Two-Level Iterative Techniques and Radial Basis Functions
Radial basis function method has been used to handle linear and nonlinear equations. The purpose of this paper is to introduce the method of RBF to an existing method in solving nonlinear two-level iterative techniques and also the method is implemented to four numerical examples. The results reveal that the technique is very effective and simple. The main property of the method lies in its flexibility and ability to solve nonlinear equations accurately and conveniently.
http://ijm2c.iauctb.ac.ir/article_663722_85f8d1cd2ae961e6b1660c564c8fb4fc.pdf
2017-11-01T11:23:20
2020-04-05T11:23:20
277
285
First-order evolution equations
Two-level iterative techniques
Radial basis function
Newton's method for nonlinear equations
Sara
Hosseini
s_hosseini66@yahoo.com
true
1
Qazvin Branch, Islamic Azad University
Qazvin Branch, Islamic Azad University
Qazvin Branch, Islamic Azad University
LEAD_AUTHOR
ORIGINAL_ARTICLE
Solving Differential Equations by Using a Combination of the First Kind Chebyshev Polynomials and Adomian Decomposition Method
In this paper, we are going to solve a class of ordinary diﬀerential equations that its source term are rational functions. We obtain the best approximation of source term by Chebyshev polynomials of the ﬁrst kind, then we solve the ordinary diﬀerential equations by using the Adomian decomposition method
http://ijm2c.iauctb.ac.ir/article_663723_ad2ef8504ccc52c51015209991a03290.pdf
2017-11-01T11:23:20
2020-04-05T11:23:20
287
297
Polynomials Chebyshev
Adomian decomposition method
Initial value problems
Best polynomial approximation
hasan
barzegar kelishami
hbk.math@gmail.com
true
1
Department of Mathematics&lrm;, &lrm;Islamic Azad University&lrm;, &lrm;Central Tehran Branch&lrm;, &lrm;Tehran&lrm;, &lrm;Iran
Department of Mathematics&lrm;, &lrm;Islamic Azad University&lrm;, &lrm;Central Tehran Branch&lrm;, &lrm;Tehran&lrm;, &lrm;Iran
Department of Mathematics&lrm;, &lrm;Islamic Azad University&lrm;, &lrm;Central Tehran Branch&lrm;, &lrm;Tehran&lrm;, &lrm;Iran
LEAD_AUTHOR
ORIGINAL_ARTICLE
Approximation of a Fuzzy Function by Using Radial Basis Functions Interpolation
In the present paper, Radial Basis Function interpolations are applied to approximate a fuzzy function $\tilde{f}:\R\rightarrow \mathcal{F}(\R)$, on a discrete point set $X=\{x_1,x_2,\ldots,x_n\}$, by a fuzzy-valued function $\tilde{S}$. RBFs are based on linear combinations of terms which include a single univariate function. Applying RBF to approximate a fuzzy function, a linear system will be obtained which by defining coefficient vector, target function will be approximated. Finally for showing the efficiency of the method we give some numerical examples.
http://ijm2c.iauctb.ac.ir/article_663724_ce48161f971bb5d8d6495499424c47cf.pdf
2017-11-01T11:23:20
2020-04-05T11:23:20
299
307
Radial Basis Function interpolation
fuzzy function
fuzzy-valued function
an approximation of a fuzzy function
Reza
Firouzdor
r.firouzdor2016@gmail.com
true
1
university
university
university
AUTHOR
Majid
Amirfakhrian
majiamir@yahoo.com
true
2
IAUCTB
IAUCTB
IAUCTB
LEAD_AUTHOR