2020-04-02T05:45:55Z http://ijm2c.iauctb.ac.ir/?_action=export&rf=summon&issue=1132710
2017-11-01
International Journal of Mathematical Modelling & Computations 2228-6225 2228-6225 2017 7 4 (FALL) Perishable Inventory Model with Retrial Demands, Negative Customers and Multiple Working Vacations Vijaya Laxmi Pikkala Soujanya M.L. This paper presents the analysis of a continuous review perishable<br /> inventory system wherein the life time of each item follows an<br /> exponential distribution. The operating policy is (s,S) policy<br /> where the ordered items are received after a random time which<br /> follows exponential distribution. Primary arrival follows Poisson<br /> distribution and they may turnout to be positive or negative and<br /> then enter into the orbit. The orbiting demands compete their<br /> service according to exponential distribution. The server takes<br /> multiple working vacations at zero inventory. We assume that<br /> the vacation time, service times both during regular busy period<br /> and vacation period are exponentially distributed. Matrix analytic<br /> method is used for the steady state distribution of the model.<br /> Various performance measures and cost analysis are shown with<br /> numerical results. Perishable inventory (s,S) policy Retrial demands Negative customers Multiple working vacations Matrix analytic method 2017 11 01 239 254 http://ijm2c.iauctb.ac.ir/article_663719_74a04f952743d47057405a453e8ee2f5.pdf
2017-11-01
International Journal of Mathematical Modelling & Computations 2228-6225 2228-6225 2017 7 4 (FALL) A Note on Solving Prandtl's Integro-Differential Equation Atta Dezhbord Taher Lotfi 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. Reproducing Kernel Space Singular integro-differential equation of Prandtl's type Hypersingular integral equation Error estimation 2017 11 01 255 263 http://ijm2c.iauctb.ac.ir/article_663720_6a00cedc563dbd4cbf3740983dbeadfa.pdf
2017-11-01
International Journal of Mathematical Modelling & Computations 2228-6225 2228-6225 2017 7 4 (FALL) The Tau-Collocation Method for Solving Nonlinear Integro-Differential Equations and Application of a Population Model Atefeh Armand Zienab Gouyandeh 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.<br /> 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. Nonlinear integro-differential equation Tau-Collocation method Matrix representation Population model Collocation point 2017 11 01 265 276 http://ijm2c.iauctb.ac.ir/article_663721_353c22ebe2793b8fbeb23498bb5e8212.pdf
2017-11-01
International Journal of Mathematical Modelling & Computations 2228-6225 2228-6225 2017 7 4 (FALL) Numerical Solution of Nonlinear PDEs by Using Two-Level Iterative Techniques and Radial Basis Functions Sara Hosseini ‎Radial basis function method has been used to handle linear and‎<br /> ‎nonlinear equations‎. ‎The purpose of this paper is to introduce the method of RBF to‎<br /> ‎an existing method in solving nonlinear two-level iterative‎<br /> ‎techniques and also the method is implemented to four numerical‎<br /> ‎examples‎. ‎The results reveal that the technique is very effective‎<br /> ‎and simple. The main property of the method lies in its‎<br /> ‎flexibility and ability to solve nonlinear equations accurately‎<br /> ‎and conveniently. ‎First-order evolution equations‎ ‎Two-level‎ ‎iterative techniques‎ ‎Radial basis function‎ ‎Newton's method for‎ ‎nonlinear equations‎ 2017 11 01 277 285 http://ijm2c.iauctb.ac.ir/article_663722_85f8d1cd2ae961e6b1660c564c8fb4fc.pdf
2017-11-01
International Journal of Mathematical Modelling & Computations 2228-6225 2228-6225 2017 7 4 (FALL) Solving Differential Equations by Using a Combination of the First Kind Chebyshev Polynomials and Adomian Decomposition Method hasan barzegar kelishami 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 Polynomials Chebyshev Adomian decomposition method Initial value problems Best polynomial approximation 2017 11 01 287 297 http://ijm2c.iauctb.ac.ir/article_663723_ad2ef8504ccc52c51015209991a03290.pdf
2017-11-01
International Journal of Mathematical Modelling & Computations 2228-6225 2228-6225 2017 7 4 (FALL) Approximation of a Fuzzy Function by Using Radial Basis Functions Interpolation Reza Firouzdor Majid Amirfakhrian In the present paper, Radial Basis Function interpolations are applied to approximate a fuzzy function<br /> \$tilde{f}:Rrightarrow mathcal{F}(R)\$,<br /> 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. Radial Basis Function interpolation fuzzy function fuzzy-valued function an approximation of a fuzzy function 2017 11 01 299 307 http://ijm2c.iauctb.ac.ir/article_663724_ce48161f971bb5d8d6495499424c47cf.pdf