Islamic Azad University, Qaemshahr Branch
Fuzzy Optimization and Modeling Journal
2676-7007
1
1
2018
09
01
Fuzzy bi-level linear programming problem using TOPSIS approach
1
10
EN
Shyamali
Ghosh
Dept. of Applied Mathematics with Ocenology and Computer Programming,
Vidyasaghar University, India
shyamalighosh1989@gmail.com
Sankar Kumar
Roy
Department of Applied Mathematics with Oceanology and Computer Programming
sankroy2006@gmail.com
This 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.
Bi-level linear programming,Fuzzy programming,TOPSIS,Compromise solution
http://fomj.qaemiau.ac.ir/article_543187.html
http://fomj.qaemiau.ac.ir/article_543187_b1641db7a30f059bed25eb1663ca9022.pdf
Islamic Azad University, Qaemshahr Branch
Fuzzy Optimization and Modeling Journal
2676-7007
1
1
2018
09
01
A Hybrid Algorithm for Fault Diagnosis using Fuzzy Clustering Tools
11
30
EN
Adrián
Rodríguez Ramos
Departamento de Automática y Computación, Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE, La Habana, Cuba
Pedro Juan
Rivera-Torres
Departamento de Ciencias de Computos, Universidad de Puerto Rico, Recinto de Río Piedras, San Juan, Puerto Rico
Antônio José
da Silva Neto
Instituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ), Nova Friburgo, RJ, Brazil
Orestes
Llanes-Santiago
0000-0002-6864-9629
Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ), Nova Friburgo, RJ, Brazil
orestes@tesla.cujae.edu.cu
In 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.
Automatic learning,Online detection,Fuzzy clustering tools,Optimal parameters
http://fomj.qaemiau.ac.ir/article_545576.html
http://fomj.qaemiau.ac.ir/article_545576_d466ed54c1eea0760c939f1c66747bab.pdf
Islamic Azad University, Qaemshahr Branch
Fuzzy Optimization and Modeling Journal
2676-7007
1
1
2018
09
01
The Reference Ideal Method and the Pythagorean Fuzzy Numbers
31
40
EN
Elio
Cables
Depto de Ingeniería Informática, Universidad de Holguín "Oscar Lucero Moya", Holguín, Cuba
payizan_ae58@yahoo.com
María Teresa
Lamata
University of Granada
mtl@decsai.ugr.es
Jose Luis
Verdegay
Computer Science and Artificial Intelligence, University of Granada, Spain
verdegay@ugr.es
As 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.
Reference Ideal Method (RIM),TOPSIS,Pythagorean Fuzzy Set,MCDM
http://fomj.qaemiau.ac.ir/article_545712.html
http://fomj.qaemiau.ac.ir/article_545712_75330f3c005b26b7ca5ff1c6b9c05e83.pdf
Islamic Azad University, Qaemshahr Branch
Fuzzy Optimization and Modeling Journal
2676-7007
1
1
2018
09
01
Optimum Selection of Drill Bits for Drilling Operations in Sarvak and Asmari Formations Using a Fuzzy Multiple Criteria Decision-Making Approach
41
50
EN
Arash
Ebrahimabadi
0000-0002-1996-2731
Department of Mining, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
arash.xer@gmail.com
Siavash
Moradi
Department of Petroleum Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
siavash.moradi@srbiau.ac.ir
Proper decision making in drilling bit selection issue may contribute to drilling efficiency and considerable cost reduction. Since the bit selection is a Multiple Criteria Decision-Making (MCDM) problem, MCDM techniques are the most powerful approaches to be applied in such cases. In this study, among MCDM approaches and with respect to great accuracy and validity of results, fuzzy TOPSIS method is utilized for optimum bit selection for drilling operations in Sarvak and Asmari formations in an Iranian oil field. With this regard, three types of bits (i.e. 517, 527 and 537) candidate in Asmari & Sarvak formations are analysed using fuzzy TOPSIS method to rank and prioritize the alternatives, leading to choose the best option. Considering bits operating in Asmari formation, similarity factors for bit types of 517, 527 and 537 bits found to be 0.479, 0.438 and 0.382, respectively indicating bit type 517 can be considered a proper option compared to other ones. Similarly, achieved results from application of fuzzy TOPSIS approach in Sarvak formation shows 0.5405, 0.5019 and 0.5622 values for 517, 527 and 537 bit types respectively, demonstrating the bit type 537 is the most appropriate alternative in Sarvak formation.
Bit Selection,Fuzzy TOPSIS,Asmari Formation,Sarvak Formation
http://fomj.qaemiau.ac.ir/article_669003.html
http://fomj.qaemiau.ac.ir/article_669003_7d26b9c3a3d20be840ff0e38172bf549.pdf
Islamic Azad University, Qaemshahr Branch
Fuzzy Optimization and Modeling Journal
2676-7007
1
1
2018
09
01
Ranking method for efficient units by RPA and TOPSIS in DEA
51
59
EN
Farzad
Rezai Balf
Department of Mathematics, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran.
fr.balf@qaemiau.ac.ir
This paper considers the rank of set efficient units in Data envelopment analysis (DEA). DEA measures the efficiency of decision making units (DMUs) within the range of less than or equal to one. The corresponding efficiencies are referred to as relative efficiencies, which describe the best performances of DMUs, and these efficient units determine efficiency frontier. This research proposes an extended on a current research by a technique for order preference by similarity to an ideal solution (TOPSIS) method. Therefore, in this paper, we first introduce two methods namely regular polygon area (RPA) and TOPSIS. Then using common set of weights in order to all efficient units obtained from DEA models, they are projected into two-dimensional plane. Finally, the units are ranked by RPA and TOPSIS methods. Also, with the numerical example, our method is compared with other methods. The obtained results of numerical example show that they are almost close to each of several methods.
Data Envelopment Analysis (DEA),RPA, TOPSIS,Ranking
http://fomj.qaemiau.ac.ir/article_669004.html
http://fomj.qaemiau.ac.ir/article_669004_080e111fc059918dfb2409c8015b27b4.pdf