ORIGINAL_ARTICLE On Fixed Points of Soft Set-Valued Maps 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. http://fomj.qaemiau.ac.ir/article_684584_3c5c2513d9748af1ce4222c3b07104ee.pdf 2021-07-01 1 16 10.30495/fomj.2021.1938962.1034 Soft set Soft set-valued map e-soft fixed point fuzzy set Fuzzy mapping Mohammed Shehu Shagari shagaris@ymail.com 1 Department of Mathematics, Faculty of Physical Sciences, Ahmadu Bello University, Nigeria LEAD_AUTHOR Ibrahim Fulatan ialiy@abu.edu.ng 2 Department of Mathematics, Faculty of Physical Sciences, Ahmadu Bello University, Nigeria AUTHOR Yahaya Sirajo surajm951@gmail.com 3 School of Arts and Sciences, American University of Nigeria Yola, Adamawa State, Nigeria AUTHOR
ORIGINAL_ARTICLE Fuzzy Regression Models Using the Least-Squares Method based on the Concept of Distance: Simplified Approach 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. http://fomj.qaemiau.ac.ir/article_684592_e6a321952f011322b68b02cccb07922f.pdf 2021-07-01 17 23 10.30495/fomj.2021.1933334.1031 distance Fuzzy regression model fuzzy sets Least squares method Abdullah Al-Qudaimi aalqudaimi@yahoo.com 1 Department of Information Technology, University of Science and Technology, Sana’a, Yemen LEAD_AUTHOR Walid Yousef walidshahir@gmail.com 2 Department of Information Technology, University of Science and Technology, Sana’a, Yemen AUTHOR
ORIGINAL_ARTICLE Integrating Developed Evolutionary Algorithm and Taguchi Method for Solving Fuzzy Facility’s Layout Problem 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. http://fomj.qaemiau.ac.ir/article_685092_585b5d5e6097c2c165a757595588ba4d.pdf 2021-07-01 24 35 10.30495/fomj.2021.1930688.1027 fuzzy sets Optimization Facility's Layout quadratic assignment problem Hossein Jafari hossein_jafari_123@yahoo.com 1 Young Researchers and Elite Club, Arak Branch, Islamic Azad Univercity, Arak, Iran LEAD_AUTHOR Abbas Sheykhan a-sheykhan@iau-arak.ac.ir 2 Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran AUTHOR
ORIGINAL_ARTICLE Construction of New Implicit Milstein-Type Scheme for Solution of the Systems of SODEs 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. http://fomj.qaemiau.ac.ir/article_685527_755e1bec9d064a28e0d848253aeb730c.pdf 2021-07-01 36 46 10.30495/fomj.2021.1939654.1036 Itˆ o stochastic ordinary diferential equations strong convergence Mean-square stability Hassan Ranjbar hranjbar@semnan.ac.ir 1 Department of Mathematics, Faculty of Mathematics, Statistics and Computer Sciences, Semnan University, P. O. Box 35195-363, Semnan, Iran LEAD_AUTHOR Younes Akbari younes.akbari@qu.edu.qa 2 Department of Computer Sciences and Engineering, Qatar University, Doha, Qatar AUTHOR Hadis Derikvandi hadis.derikvandi@gmail.com 3 Department of Mathematics, Faculty of Mathematics, Statistics and Computer Sciences, Semnan University, P. O. Box 35195-363, Semnan, Iran AUTHOR