Islamic Azad University, Qaemshahr Branch Fuzzy Optimization and Modeling Journal 2676-7007 2 3 2021 07 01 On Fixed Points of Soft Set-Valued Maps 1 16 EN Mohammed Shehu Shagari Department of Mathematics, Faculty of Physical Sciences, Ahmadu Bello University, Nigeria shagaris@ymail.com Ibrahim Aliyu Fulatan Department of Mathematics, Faculty of Physical Sciences, Ahmadu Bello University, Nigeria ialiy@abu.edu.ng Yahaya Sirajo School of Arts and Sciences, American University of Nigeria Yola, Adamawa State, Nigeria surajm951@gmail.com 10.30495/fomj.2021.1938962.1034 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 set-valued 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 point-to-point and point-to-set 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. Soft set,Soft set-valued map,e-soft fixed point,fuzzy set,Fuzzy mapping http://fomj.qaemiau.ac.ir/article_684584.html http://fomj.qaemiau.ac.ir/article_684584_3c5c2513d9748af1ce4222c3b07104ee.pdf
Islamic Azad University, Qaemshahr Branch Fuzzy Optimization and Modeling Journal 2676-7007 2 3 2021 07 01 Fuzzy Regression Models Using the Least-Squares Method based on the Concept of Distance: Simplified Approach 17 23 EN Abdullah Al-Qudaimi Department of Information Technology, University of Science and Technology, Sana’a, Yemen aalqudaimi@yahoo.com Walid Yousef Department of Information Technology, University of Science and Technology, Sana’a, Yemen walidshahir@gmail.com 10.30495/fomj.2021.1933334.1031 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.1259-1272) which can be used to find the regression coefficients of a fuzzy regression model without considering the non-negative 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. distance,Fuzzy regression model,fuzzy sets,Least squares method http://fomj.qaemiau.ac.ir/article_684592.html http://fomj.qaemiau.ac.ir/article_684592_e6a321952f011322b68b02cccb07922f.pdf
Islamic Azad University, Qaemshahr Branch Fuzzy Optimization and Modeling Journal 2676-7007 2 3 2021 07 01 Integrating Developed Evolutionary Algorithm and Taguchi Method for Solving Fuzzy Facility’s Layout Problem 24 35 EN Hossein Jafari 0000-0003-0863-5548 Young Researchers and Elite Club, Arak Branch, Islamic Azad Univercity, Arak, Iran hossein_jafari_123@yahoo.com Abbas Sheykhan Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran a-sheykhan@iau-arak.ac.ir 10.30495/fomj.2021.1930688.1027 The quadratic assignment problem (QAP) is one of the combinatorial optimization problems belonging to the NP-hard 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. fuzzy sets,Optimization,Facility's Layout,quadratic assignment problem http://fomj.qaemiau.ac.ir/article_685092.html http://fomj.qaemiau.ac.ir/article_685092_585b5d5e6097c2c165a757595588ba4d.pdf
Islamic Azad University, Qaemshahr Branch Fuzzy Optimization and Modeling Journal 2676-7007 2 3 2021 07 01 Construction of New Implicit Milstein-Type Scheme for Solution of the Systems of SODEs 36 46 EN Hassan Ranjbar 0000-0001-9374-1091 Department of Mathematics, Faculty of Mathematics, Statistics and Computer Sciences, Semnan University, P. O. Box 35195-363, Semnan, Iran hranjbar@semnan.ac.ir Younes Akbari Department of Computer Sciences and Engineering, Qatar University, Doha, Qatar younes.akbari@qu.edu.qa Hadis Derikvandi Department of Mathematics, Faculty of Mathematics, Statistics and Computer Sciences, Semnan University, P. O. Box 35195-363, Semnan, Iran hadis.derikvandi@gmail.com 10.30495/fomj.2021.1939654.1036 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 split-step (α, β)-Milstein method strongly convergence to exact solution with order one. The means-quare stability of the our method for linear stochastic diferential equation with one-dimensional Wiener process is studied. Stability analysis shows that the mean-square stability our proposed method contains the mean-square stability region of linear scalar test equation. Finally, numerical examples illustrates the efectiveness of the theoretical results. Itˆ o stochastic ordinary diferential equations,strong convergence,Mean-square stability http://fomj.qaemiau.ac.ir/article_685527.html http://fomj.qaemiau.ac.ir/article_685527_755e1bec9d064a28e0d848253aeb730c.pdf