ORIGINAL_ARTICLE
New insight on solving fuzzy linear fractional programming in material aspects
Recently, Srinivasan [On solving fuzzy linear fractional programming inmaterial aspects, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.04.209] proposed a method to solve fractional linear programmingproblem under fuzzy environment based on ranking and decompositionmethods. Srinivasan also claimed that the proposed method solved fractionallinear programming problem with inequality and equality constraints. In thisnote, we point out that the paper entitled above suffers from certainmathematical mistakes for solving these problems. Hence, the mentionedmethod and example are not valid. Further the exact method is stated and solvedthe problem.
http://fomj.qaemiau.ac.ir/article_674713_7e97402cd6cdddd83d68fc47fe895974.pdf
2020-04-01
1
7
Fractional programming
Fuzzy linear programming
Triangular Fuzzy Numbers
Sapan
Das
cool.sapankumar@gmail.com
1
Department of Mathematics, National Institute of Technology Jamshedpur, India
LEAD_AUTHOR
Seyyed Ahamad
Edalatpanah
saedalatpanah@gmail.com
2
Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran
AUTHOR
ORIGINAL_ARTICLE
The Application of Data Envelopment Analysis in Fuzzy Queuing Models
In this paper, an approach is presented for the evaluation of efficiency in fuzzy queuing models with publicity and renouncement. In the existing method proposed for the functions of fuzzy profit of queuing models, in the last stage the function of standardized profit and the level of expense can be evaluated among different α-level set. According to the new approach we determine which α-level set can be chosen for the system as efficient and ideal. In this step an interval data envelopment analysis model is suggested to get the overall efficiency of the proposed method for the functions of fuzzy profit of queuing models. Numerical illustration is provided to show the application of interval DEA models to the fuzzy queuing systems.
http://fomj.qaemiau.ac.ir/article_674896_b04b1260c2a3ea3a9d9f8d004df544fa.pdf
2020-04-20
8
15
Data envelopment analysis
Fuzzy queuing theory
fuzzy optimization
Najmeh
Malekmohammadi
n.malekmohammadi@gmail.com
1
Islamic Azad University, South Tehran Branch
LEAD_AUTHOR
ORIGINAL_ARTICLE
Developing a Fuzzy Knowledge Based Optimisation System for Storage and Retrieval Operations of Long Stay Containers
Owing to the many uncertainties involved, the management of container yard operations is very challenging. The storage of containers is one of those operations that require proper management to achieve efficient utilisation of the yard, short handling time and a minimum number of re-handlings. The aim of this study is to develop a fuzzy knowledge based optimisation system based on genetic algorithm named ‘FKBGA’ for the management of container yard operations that take into consideration factors and constraints of long stay containers that exist in real-life situations. One of these factors is the duration of stay of a container in each stack. Because the duration of stay of containers stored with pre-existing containers varies dynamically over time, an ‘ON/OFF’ strategy is proposed to activate or deactivate the duration of stay factor in the estimation of departure time if the topmost containers for each stack have been stored for a similar time period. A genetic algorithm module based Multi-Layer concept is developed which identifies the optimal fuzzy rules required for each set of fired rules to achieve a minimum number of container re-handlings when selecting a stack. An industrial case study is used to demonstrate the applicability and practicability of the developed system. The proposed system has the potential to produce more effective storage and retrieval strategies, by reducing the number of re-handlings of containers. The performance of the proposed system is assessed by comparing with other Constrained-Probabilistic Stack Allocation “CPSA” and Constrained-Neighbourhood Stack Allocation “CNSA” storage and retrieval techniques.
http://fomj.qaemiau.ac.ir/article_674913_126c94df93fa8fac03970a9ca41ba8e6.pdf
2020-04-25
16
42
Fuzzy Knowledge Based System
Multi-Layer Genetic Algorithm
Fuzzy Rules
‘ON/OFF’ Strategy
Ali
Abbas
bbuk9999@gmail.com
1
Coventry University
LEAD_AUTHOR
ORIGINAL_ARTICLE
Rough Set in Fuzzy Approximation Space with Fuzzy Equivalence Class
The rough set model was constructed in fuzzy approximation space. In this study, we first introduce the fuzzy relation, relative sets, and fuzzy equivalence class. Then, we prove some properties of the fuzzy equivalence class. Thereafter, the concept of fuzzy rough set is proposed over fuzzy relation and inverse fuzzy relation in fuzzy approximation space by means of relative sets and fuzzy equivalence class sets, and some propositions are proved. Also, some examples and dentitions are presented in this study
http://fomj.qaemiau.ac.ir/article_674915_5f3f13eddb1f732313771a74c6de62a5.pdf
2020-07-10
43
49
fuzzy set
fuzzy relation
fuzzy rough set
Mohadeseh
Taginejad
mohadesehtaqinejad@gmail.com
1
Department of Mathematics, Payame Noor University (PNU) Tehran, Iran
LEAD_AUTHOR
ORIGINAL_ARTICLE
A Comparative Study on Evaluation of Modular Courses in Conservatories by the Fuzzy Delphi Method
The development of modular courses at the conservatory and theoretical courses throughout the country is being developed by the organization of education to design effective and appropriate systems for the development and use of information on the learner's progress. So, in this paper, we try to identify factors that contribute to increasing the efficiency of evaluating the learning - studying processes and teaching modular courses. In the first step, 16 factors influencing the assessment and teaching of conservatory courses were identified using the experts’ opinions and identifying the important criteria of modular courses. Finally, we measured the strengths and weaknesses of the modular courses in the conservatories of Chamestan using the fuzzy Delphi technique. Also, the results of the viewpoints of the lecturers in these courses indicate that one of the strengths of these courses is the revaluation factor in each module that makes it impossible to create an atmosphere of anxiety during the study and evaluation for the student. On the other hand, one of the weaknesses of these courses is the lack of space and workshops with courses content, which prevents from the fulfilment of the appropriate effect that the student expects for these courses.
http://fomj.qaemiau.ac.ir/article_674920_01e4b666753df3d015d3fecbf46c5f5d.pdf
2020-04-01
50
59
education
Modular courses
Fuzzy Delphi technique
Reza
Shahverdi
shahverdi_592003@yahoo.com
1
Department of Mathematics, Islamic Azad University Qaemshahr branch, Iran.
LEAD_AUTHOR
Safieh
Mohsenifar
safiehmohsenifar@yahoo.com
2
Department of Computer, Tabari Non-Profit University, Babol, Iran
AUTHOR
ORIGINAL_ARTICLE
A Parameterized Approach for Linear Regression of Interval Data: Suggested Modifications
Souza et al. (Knowledge-Based Systems, 131 (2017), pp. 149-159) pointed out that although several approaches have been proposed in the literature for fitting interval linear regression models (linear regression models its parameters are represented as intervals). However, as there are flaws in all the existing approaches, it is scientifically incorrect to use these approaches in real life problems. To resolve the flaws of the existing approaches, Souza et al. proposed a new approach for fitting interval linear regression models. After a deep study, it is observed that in the approach, proposed by Souza et al., some mathematical incorrect assumptions have been considered and hence, it is scientifically incorrect to use the Souza et al.’s approach, in real life problems. In this paper the mathematical incorrect assumption, considered by Souza et al, is pointed out and suggested modifications are provided as well as a new approach is proposed as for fitting the interval linear regression models. The proposed model guarantee mathematical coherent such that the predicted values of the model are intervals with lower bound less than or equal upper bound. Furthermore, the proposed has been illustrated with the help of a numerical example.
http://fomj.qaemiau.ac.ir/article_678788_08f9acea2061d6c10330c4c80f345eb8.pdf
2020-06-10
60
68
Interval linear regression fuzzy
Symbolic data analysis
Interval parameterization
Abdullah
Al-Qudaimi
aalqudaimi@yahoo.com
1
University of Engineering and Technology
LEAD_AUTHOR