2015
7
2
2
0
Performance evaluation of efficiency change and productivity growth in Supply Chain Management
2
2
The performance of a supply chain can be evaluated in either a crosssectional or a time series manner, and data envelopment analysis is a useful method for both types of evaluation. In this paper we develop an index and indicator of productivity change that can be used with radial and nonradial models for supply chain malmquist index. The supply chain malmquist productivity index (SCMPI) can be decomposed into two components: one is measuring the technical change (TC) and the other measuring technical efficiency change (TEC). So that we propose a supply chain DEA models that have suppliermanufacturer structures.
1

121
127


M.
Fallah Jelodar
Department of Mathematics, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran.
Department of Mathematics, Ayatollah Amoli
Iran
m.fallahjelodar@iauamol.ac.ir


M.
Sanei
Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran,Iran.
Department of Mathematics, Central Tehran
Iran


S.
MamizadehChatghayeh
Young Researchers and Elite club, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Young Researchers and Elite club, Central
Iran
Supply chain management
Data Envelopment Analysis
Productivity Malmquist index
Linear optimization of fuzzy relation inequalities with maxLukasiewicz composition
2
2
In this paper, we study the finitely many constraints of fuzzy relation inequalities problem and optimize the linear objective function on this region which is defined with fuzzy maxLukasiewicz operator. In fact Lukasiewicz tnorm is one of the four basic tnorms. A new simplification technique is given to accelerate the resolution of the problem by removing the components having no effect on the solution process. Also, an algorithm and one numerical example are offered to abbreviate and illustrate the steps of the problem resolution process.
1

129
138


E.
Shivanian
Department of Mathematics, Imam Khomeini International University, Qazvin, 3414916818, Iran.
Department of Mathematics, Imam Khomeini
Iran
shivanian@sci.ikiu.ac.ir
Linear objective function optimization
Fuzzy relation equations
Fuzzy relation inequalities
maxLukasiewicz composition
A new multimode and multiproduct hub covering problem: A priority M/M/c queue approach
2
2
One main group of a transportation network is a discrete hub covering problem that seeks to minimize the total transportation cost. This paper presents a multiproduct and multimode hub covering model, in which the transportation time depends on travelling mode between each pair of hubs. Indeed, the nature of products is considered different and hub capacity constraint is also applied. Due to the transport volume and related traffic, a new priority M/M/c queuing system is considered, in which products with high priority are selected for service ahead of those with low priority. The objectives of this model minimize the total transportation cost and total time. Besides, because of the computational complexity, a multiobjective parallel simulated annealing (MOPSA) algorithm is proposed and some computational experiments are provided to illustrate the efficiency of the presented model and proposed MOPSA algorithm. The performance of this algorithm is compared with two wellknown multiobjective evolutionary algorithms, namely nondominated sorting genetic algorithm (NSGAII) and Pareto archive evolution strategy (PAES).
1

139
148


S.
Sedehzadeh
School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
School of Industrial Engineering, South Tehran
Iran


R.
TavakkoliMoghaddam
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Industrial Engineering, College
Iran
tavakoli@ut.ac.ir


F.
Jolai
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Industrial Engineering, College
Iran
Multiobjective hub covering problem
Priority queuing model
Multi modes
Parallel simulated annealing
Numerical solution of the one dimensional nonlinear Burgers equation using the Adomian decomposition method and the comparison between the modified Local CrankNicolson method and the VIM exact solution
2
2
The Burgersâ€™ equation is a simplified form of the NavierStokes equations that very well represents their nonlinear features. In this paper, numerical methods of the Adomian decomposition and the Modified Crank â€“ Nicholson, used for solving the onedimensional Burgersâ€™ equation, have been compared. These numerical methods have also been compared with the analytical method. In contrast to the conventional CrankNicolson method, the MLCN method is an explicit and unconditionally stable method. The Adomian decomposition method includes the unknown function U (x), in which each equation is defined and solved by an infinite series of unbounded functions. Velocity parameters u in the direction of the X axis, are examined at different times with different Reynolds numbers over a fixed time step. Also the accuracy of the Adomian and the CrankNicolson methods at different Reynolds numbers have been studied using two examples with different initial conditions, and the Adomian decomposition method is closer to the analytical method.
1

149
159


AR.
Haghighi
Department of Mathematics, Urmia University of Technology, Urmia, Iran.
Department of Mathematics, Urmia University
Iran
ah.haghighi@gmail.com


M.
Shojaeifard
Department of Mathematics, Urmia University of Technology, Urmia, Iran.
Department of Mathematics, Urmia University
Iran
Nonlinear Burgers equation
Adomian method
the modified Local CrankNicolson method.
A twostage model for ranking DMUs using DEA/AHP
2
2
In this paper, we present a twostage model for ranking of decision making units (DMUs) using interval analytic hierarchy process (AHP). Since the efficiency score of unity is assigned to the efficient units, we evaluate the efficiency of each DMU by basic DEA models and calculate the weights of the criteria using proposed model. In the first stage, the proposed model evaluates decision making units, and in the second stage it establishes pairwise comparison matrix then ranks all DMUs by AHP. Finally, a numerical example and an application of the proposed model in 23 universities are provided.
1

161
169


T.
Rezaeitaziani
Department of Mathematics, Islamic Azad University, Bandar Abbas Branch, Bandar Abbas, Iran.
Department of Mathematics, Islamic Azad
Iran


M.
Barkhordariahmadi
Department of Mathematics, Islamic Azad University, Bandar Abbas Branch, Bandar Abbas, Iran.
Department of Mathematics, Islamic Azad
Iran
barkhordarim@yahoo.com
Data envelopment analysis
Analytic hierarchy process
Ranking
Barrier options pricing of fractional version of the BlackScholes model
2
2
In this paper two different methods are presented to approximate the solution of the fractional BlackScholes equation for valuation of barrier option. Also, the two schemes need less computational work in comparison with the traditional methods. In this work, we propose a new generalization of the twodimensional differential transform method and decomposition method that will extend the application of this methods for pricing barrier options of fractional version of the BlackScholes model. Undoubtedly this model is the most well known model for pricing financial derivatives. This methods finds the analytical solution without any discretization or additive assumption. the approximate analytic solution is calculated in the form of convergent series with easily computable components, to solve the fractional BlackScholes equation.
1

171
178


M. A.
Mohebbi Ghandehari
Department of Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran.
Department of Mathematics, Azarbaijan Shahid
Iran
mohammadalimohebbi@yahoo.com


M.
Ranjbar
Department of Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran.
Department of Mathematics, Azarbaijan Shahid
Iran
Fractional BlackScholes equations
Barrier option pricing problem
Analytical solution
Positiveadditive functional equations in nonArchimedean $C^*$algebras
2
2
Hensel [K. Hensel, Deutsch. Math. Verein, {6} (1897), 8388.] discovered the $p$adic number as a number theoretical analogue of power series in complex analysis. Fix a prime number $p$. for any nonzero rational number $x$, there exists a unique integer $n_x inmathbb{Z}$ such that $x = frac{a}{b}p^{n_x}$, where $a$ and $b$ are integers not divisible by $p$. Then $x_p := p^{n_x}$ defines a nonArchimedean norm on $mathbb{Q}$. The completion of $mathbb{Q}$ with respect to metric $d(x, y)=x y_p$, which is denoted by $mathbb{Q}_p$, is called {it $p$adic number field}. In fact, $mathbb{Q}_p$ is the set of all formal series $x = sum_{kgeq n_x}^{infty}a_{k}p^{k}$, where $a_{k} le p1$ are integers. The addition and multiplication between any two elements of $mathbb{Q}_p$ are defined naturally. The norm $Bigsum_{kgeq n_x}^{infty}a_{k}p^{k}Big_p = p^{n_x}$ is a nonArchimedean norm on $mathbb{Q}_p$ and it makes $mathbb{Q}_p$ a locally compact field. In this paper, we consider nonArchimedean $C^*$algebras and, using the fixed point method, we provide an approximation of the positiveadditive functional equations in nonArchimedean $C^*$algebras.
1

179
185


R.
Saadati
Department of Mathematics, Iran University of Science and Technology, Tehran, Iran.
Department of Mathematics, Iran
Iran
rsaadati@iust.ac.ir
Functional equation
fixed point
Positiveadditive functional equation
Linear mapping
NonArchimedean $C^*$algebra
New model for ranking DMUs in DDEA as a special case
2
2
The purpose of this paper is to offer the equitable method for ranking Decision Making Units(DMUs) based on the Dynamic Data Envelopment Analysis (DDEA) concept, where quasifixed inputs or intermediate products are the source of intertemporal dependence between consecutive periods. In fact, this paper originally makes the use of an approach extending the ranking of DMUs in DEA by Khodabakhshi and Aryavash into the Dynamic DEA framework. Hence, firstly, we compute minimum and maximum efficiency values of each DMUs in dynamic state, under the assumption that the sum of efficiency values of all DMUs in dynamic state is equal to unity. Thus, with the combination of its minimum and maximum efficiency values, the rank of each DMUs is determined.
1

187
192


J.
Pourmahmoud
Department of Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran.
Department of Mathematics, Azarbaijan Shahid
Iran
pourmahmoud@azaruniv.ac.ir
Data envelopment analysis (DEA)
Decision Making Units(DMU)
efficiency
Ranking
Dynamic DEA (DDEA).