2020
1
2
0
90
1

New insight on solving fuzzy linear fractional programming in material aspects
http://fomj.qaemiau.ac.ir/article_674713.html
1
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.
0

1
7
SAPAN
DAS
Sapan
Das
Department of Mathematics, National Institute of Technology Jamshedpur, India
Iran
cool.sapankumar@gmail.com


Seyyed Ahamad
Edalatpanah
Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran
Iran
saedalatpanah@gmail.com
Fractional programming
Fuzzy linear programming
Triangular Fuzzy Numbers
1

The Application of Data Envelopment Analysis in Fuzzy Queuing Models
http://fomj.qaemiau.ac.ir/article_674896.html
1
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 αlevel set. According to the new approach we determine which αlevel 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.
0

8
15


Najmeh
Malekmohammadi
Islamic Azad University, South Tehran Branch
Iran
n.malekmohammadi@gmail.com
Data envelopment analysis
Fuzzy queuing theory
fuzzy optimization
1

Developing a Fuzzy Knowledge Based Optimisation System for Storage and Retrieval Operations of Long Stay Containers
http://fomj.qaemiau.ac.ir/article_674913.html
1
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 rehandlings. 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 reallife 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 preexisting 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 MultiLayer concept is developed which identifies the optimal fuzzy rules required for each set of fired rules to achieve a minimum number of container rehandlings 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 rehandlings of containers. The performance of the proposed system is assessed by comparing with other ConstrainedProbabilistic Stack Allocation “CPSA” and ConstrainedNeighbourhood Stack Allocation “CNSA” storage and retrieval techniques.
0

16
42


Ali
Abbas
Coventry University
Iran
bbuk9999@gmail.com
Fuzzy Knowledge Based System
MultiLayer Genetic Algorithm
Fuzzy Rules
‘ON/OFF’ Strategy
1

Rough Set in Fuzzy Approximation Space with Fuzzy Equivalence Class
http://fomj.qaemiau.ac.ir/article_674915.html
1
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
0

43
49
محدثه
تقی نژاد
Mohadeseh
Taginejad
Department of Mathematics, Payame Noor University (PNU) Tehran, Iran
Iran
mohadesehtaqinejad@gmail.com
fuzzy set
fuzzy relation
fuzzy rough set
1

A Comparative Study on Evaluation of Modular Courses in Conservatories by the Fuzzy Delphi Method
http://fomj.qaemiau.ac.ir/article_674920.html
1
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.
0

50
59


Reza
Shahverdi
Department of Mathematics, Islamic Azad University Qaemshahr branch, Iran.
Iran
shahverdi_592003@yahoo.com


Safieh
Mohsenifar
Department of Computer, Tabari NonProfit University, Babol, Iran
Iran
safiehmohsenifar@yahoo.com
education
Modular courses
Fuzzy Delphi technique
1

A Parameterized Approach for Linear Regression of Interval Data: Suggested Modifications
http://fomj.qaemiau.ac.ir/article_678788.html
1
Souza et al. (KnowledgeBased Systems, 131 (2017), pp. 149159) 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.
0

60
68


Abdullah
AlQudaimi
University of Engineering and Technology
Iran
aalqudaimi@yahoo.com
Interval linear regression fuzzy
Symbolic data analysis
Interval parameterization