Islamic Azad University, Qaemshahr BranchFuzzy Optimization and Modeling Journal2676-70072320210701On Fixed Points of Soft Set-Valued Maps11668458410.30495/fomj.2021.1938962.1034ENMohammedShehu ShagariDepartment of Mathematics, Faculty of Physical Sciences, Ahmadu Bello University, NigeriaIbrahim AliyuFulatanDepartment of Mathematics, Faculty of Physical Sciences, Ahmadu Bello University, NigeriaYahayaSirajoSchool of Arts and Sciences, American University of Nigeria
Yola, Adamawa State, NigeriaJournal Article20210820Conventional 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.Islamic Azad University, Qaemshahr BranchFuzzy Optimization and Modeling Journal2676-70072320210701Fuzzy Regression Models Using the Least-Squares Method based on the Concept of Distance: Simplified Approach172368459210.30495/fomj.2021.1933334.1031ENAbdullahAl-QudaimiDepartment of Information Technology, University of Science and Technology, Sana’a, YemenWalidYousefDepartment of Information Technology, University of Science and Technology, Sana’a, YemenJournal Article20210615Regression 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.Islamic Azad University, Qaemshahr BranchFuzzy Optimization and Modeling Journal2676-70072320210701Integrating Developed Evolutionary Algorithm and Taguchi Method for Solving Fuzzy Facility’s Layout Problem243568509210.30495/fomj.2021.1930688.1027ENHosseinJafariYoung Researchers and Elite Club, Arak Branch, Islamic Azad Univercity, Arak, Iran0000-0003-0863-5548AbbasSheykhanDepartment of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, IranJournal Article20210515The 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.Islamic Azad University, Qaemshahr BranchFuzzy Optimization and Modeling Journal2676-70072320210701Construction of New Implicit Milstein-Type Scheme for Solution of the Systems of SODEs364668552710.30495/fomj.2021.1939654.1036ENHassanRanjbarDepartment of Mathematics, Faculty of Mathematics, Statistics and Computer Sciences, Semnan University, P. O. Box 35195-363, Semnan, Iran0000-0001-9374-1091YounesAkbariDepartment of Computer Sciences and Engineering, Qatar University, Doha, QatarHadisDerikvandiDepartment of Mathematics, Faculty of Mathematics, Statistics and Computer Sciences, Semnan University, P. O. Box 35195-363, Semnan, IranJournal Article20210905The 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.