Islamic Azad University, Qaemshahr BranchFuzzy Optimization and Modeling Journal1120180921Fuzzy bi-level linear programming problem using TOPSIS approach-110543187ENShyamali GhoshDept. of Applied Mathematics with Ocenology and Computer Programming,
Vidyasaghar University, IndiaSankar Kumar RoyDepartment of Applied Mathematics with Oceanology and Computer ProgrammingJournal Article20180501This paper deals with a class of bi-level linear programming problem (BLPP) with fuzzy data. Fuzzy data are mainly considered to design the real-life BLPP. So we assume that the coefficients and the variables of BLPP are trapezoidal fuzzy numbers and the corresponding BLPP is treated as fuzzy BLPP (FBLPP). Traditional approaches such as vertex enumeration algorithm, Kth-best algorithm, Krush-Kuhn-Tucker (KKT) condition and Penalty function approach for solving BLPP are not only technically inefficient but also lead to a contradiction when the follower’s decision power dominates to the leader’s decision power. Also these methods are needed to solve only crisp BLPP. To overcome the difficulty, we extend Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in fuzzy environment with the help of ranking function. Fuzzy TOPSIS provides the most appropriate alternative solution based on fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS). An example is included how to apply the discussed concepts of the paper for solving the FBLPP.http://fomj.qaemiau.ac.ir/article_543187_b1641db7a30f059bed25eb1663ca9022.pdfIslamic Azad University, Qaemshahr BranchFuzzy Optimization and Modeling Journal1120180901A Hybrid Algorithm for Fault Diagnosis using Fuzzy Clustering Tools----1130545576ENAdrián Rodríguez RamosDepartamento de Automática y Computación, Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE, La Habana, CubaPedro Juan Rivera-TorresDepartamento de Ciencias de Computos, Universidad de Puerto Rico, Recinto de Río Piedras, San Juan, Puerto RicoAntônio José da Silva NetoInstituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ), Nova Friburgo, RJ, BrazilOrestes Llanes-SantiagoPolitécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ), Nova Friburgo, RJ, Brazil0000-0002-6864-9629Journal Article20180501In this paper, a hybrid algorithm using fuzzy clustering techniques is proposed for developing a robust fault diagnosis platform in industrial systems. The proposed algorithm is applied in a fault diagnosis scheme with online detection of novel faults and automatic learning. The hybrid algorithm identifies the outliers based on data density. Later, the outliers are removed, and the clustering process is performed. To extract the important features and improve the clustering, the maximum-entropy-regularized weighted fuzzy c-means is used. The use of a kernel function allows achieving a greater separability among the classes by reducing the classification errors. Finally, a step is used to optimize the parameters m (regulation factor of the fuzziness of the resulting partition) and (bandwidth, and indicator of the degree of smoothness of the Gaussian kernel function). The proposed hybrid algorithm was validated using the Tennessee Eastman (TE) process benchmark. The results obtained indicate the feasibility of the proposal.------http://fomj.qaemiau.ac.ir/article_545576_d466ed54c1eea0760c939f1c66747bab.pdfIslamic Azad University, Qaemshahr BranchFuzzy Optimization and Modeling Journal1120180910The Reference Ideal Method and the Pythagorean Fuzzy Numbers---3140545712ENElio CablesDepto de Ingeniería Informática, Universidad de Holguín "Oscar Lucero Moya", Holguín, CubaMaría Teresa LamataUniversity of GranadaJose Luis VerdegayComputer Science and Artificial Intelligence, University of Granada, SpainJournal Article20180505As it is well known, in spite of having small dimensions, there are daily many<br />situations that require the solution of a decision-making problem: eating, streets<br />crossing, assessments, shopping and so on. Generally, the way of working on<br />these types of problems depends on how the information used to evaluate each<br />alternative is provided and represented, as for instance is the case with: crisp<br />values, fuzzy values, Pythagorean values, etc. In this way, different very wellknown<br />methods have been developed and modified to help to solve this kind of<br />problems. Among them, the following may be remarked: AHP, PROMETHEE,<br />ELECTRE, VIKOR, TOPSIS. But there are many other. This paper shows how<br />to apply the so-called Reference Ideal Method (RIM), previously developed by<br />the authors, when Pythagorean Fuzzy numbers are used to evaluate each<br />alternative. The paper shows how to solve a decision-making problem through<br />the proposed method using such kind of fuzzy numbers and, in order to show<br />how to practically apply the RIM method, an illustrative example is provided.-------http://fomj.qaemiau.ac.ir/article_545712_75330f3c005b26b7ca5ff1c6b9c05e83.pdf