2021
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On Fixed Points of Soft SetValued Maps
http://fomj.qaemiau.ac.ir/article_684584.html
10.30495/fomj.2021.1938962.1034
1
Conventional mathematical tools which require all inferences to be exact, are not always sufficient to handle imprecisions in a wide variety of practical fields. Thus, among various developments in fuzzy mathematics, enormous efforts have been in process to produce new fuzzy analogues of the classical fixed point results and their various applications. Following this line in this paper, a new type of setvalued mapping whose range set lies in a family of soft sets is examined. To this effect, we introduce a few fixed point theorems which are generalizations of several significant fixed point results of pointtopoint and pointtoset valued mappings in the comparable literature. Some of these particular cases are noted and analyzed. Moreover, nontrivial examples are provided to support the assumptions of our main results.
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1
16


Mohammed
Shehu Shagari
Department of Mathematics, Faculty of Physical Sciences, Ahmadu Bello University, Nigeria
Iran
shagaris@ymail.com


Ibrahim
Fulatan
Department of Mathematics, Faculty of Physical Sciences, Ahmadu Bello University, Nigeria
Iran
ialiy@abu.edu.ng


Yahaya
Sirajo
School of Arts and Sciences, American University of Nigeria
Yola, Adamawa State, Nigeria
Iran
surajm951@gmail.com
Soft set
Soft setvalued map
esoft fixed point
fuzzy set
Fuzzy mapping
1

Fuzzy Regression Models Using the LeastSquares Method based on the Concept of Distance: Simplified Approach
http://fomj.qaemiau.ac.ir/article_684592.html
10.30495/fomj.2021.1933334.1031
1
Regression models have been tremendously studying with so many applications in the presence of imprecise data. The regression coefficients are unknown i.e., they cannot be restricted. To the best of our knowledge, there is no approach except Chen and Hsueh approach (IEEE Transactions on Fuzzy Systems, vol. 17, no. 6, December 2009 pp.12591272) which can be used to find the regression coefficients of a fuzzy regression model without considering the nonnegative restrictions on the regression coefficients. Chen and Hsueh have used some mathematical assumptions which lead to limitations in their approach. Furthermore, Chen and Hsueh approach is inefficient regarding to computational complexity. This paper proposed a simplified approach overcoming the limitations and computational complexity of Chen and Hsueh approach which can be considered by the researchers who would like to use Chen and Hsueh approach in real life applications.
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17
23


Abdullah
AlQudaimi
Department of Information Technology, University of Science and Technology, Sana’a, Yemen
Iran
aalqudaimi@yahoo.com


Walid
Yousef
Department of Information Technology, University of Science and Technology, Sana’a, Yemen
Iran
walidshahir@gmail.com
distance
Fuzzy regression model
fuzzy sets
Least squares method
1

Integrating Developed Evolutionary Algorithm and Taguchi Method for Solving Fuzzy Facility’s Layout Problem
http://fomj.qaemiau.ac.ir/article_685092.html
10.30495/fomj.2021.1930688.1027
1
The quadratic assignment problem (QAP) is one of the combinatorial optimization problems belonging to the NPhard problems’ class and has a wide application in the placement of facilities. Thus far, many efforts have been made to solve this problem and countless algorithms have been developed to achieve optimal solutions, one of which is the Genetic Algorithm (GA). This paper aims at finding a suitable layout for the facilities of an industrial workshop by using a developed genetic algorithm and Taguchi Method (TM). The research method in the current study is mathematical modeling and data was analyzed using genetic algorithm in Minitab and MATLAB. The results show that the Developed Genetic Algorithm (DGA) is highly efficient, as it has the power to discover many optimal solutions. Therefore, according to the obtained results, it is recommended that when the genetic algorithm (GA) is used to solve problems, it is better to run this algorithm several times; because the proposed method increases the variety of answers in the genetic algorithm and power for discovering the optimal solution becomes more.
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24
35


Hossein
Jafari
Young Researchers and Elite Club, Arak Branch, Islamic Azad Univercity, Arak, Iran
Iran
hossein_jafari_123@yahoo.com


Abbas
Sheykhan
Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran
Iran
asheykhan@iauarak.ac.ir
fuzzy sets
Optimization
Facility's Layout
quadratic assignment problem
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Construction of New Implicit MilsteinType Scheme for Solution of the Systems of SODEs
http://fomj.qaemiau.ac.ir/article_685527.html
10.30495/fomj.2021.1939654.1036
1
The main aim of this study is to construct a new approximation method for solving stif Itˆo stochastic ordinary diferential equations based on explicit Milstein scheme. Under the suicient conditions such as Lipschitz condition and linear growth bound, we prove that splitstep (α, β)Milstein method strongly convergence to exact solution with order one. The meansquare stability of the our method for linear stochastic diferential equation with onedimensional Wiener process is studied. Stability analysis shows that the meansquare stability our proposed method contains the meansquare stability region of linear scalar test equation. Finally, numerical examples illustrates the efectiveness of the theoretical results.
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36
46


Hassan
Ranjbar
Department of Mathematics, Faculty of Mathematics, Statistics and Computer Sciences, Semnan University, P. O. Box 35195363, Semnan, Iran
Iran
hranjbar@semnan.ac.ir


Younes
Akbari
Department of Computer Sciences and Engineering, Qatar University, Doha, Qatar
Iran
younes.akbari@qu.edu.qa


Hadis
Derikvandi
Department of Mathematics, Faculty of Mathematics, Statistics and Computer Sciences, Semnan University, P. O. Box 35195363, Semnan, Iran
Iran
hadis.derikvandi@gmail.com
Itˆ o stochastic ordinary diferential equations
strong convergence
Meansquare stability