2021-04-12T05:40:46Z
http://jiei.azad.ac.ir/?_action=export&rf=summon&issue=110213
Journal of Industrial Engineering, International
1735-5702
1735-5702
2007
3
5
Portfolio selection through imprecise Goal Programming model: Integration of the manager`s preferences
N
Mansour
A
Rebai
B
Aouni
In the portfolio selection problem, the manager considers several objectives simultaneously such as the rate of return, the liquidity and the risk of portfolios. These objectives are conflicting and incommensurable. Moreover, the objectives can be imprecise. Generally, the portfolio manager seeks the best combination of the stocks that meets his investment objectives. The imprecise Goal Programming model will be utilized to build the most satisfactory portfolio. The concept of satisfaction functions will be utilized to integrate explicitly the preferences of the portfolio’s manager. The developed model has been applied to portfolio selection within the Tunisian stock exchange market.
Portfolio Selection
Imprecise goal programming
Satisfaction function
Manager’s preferences
2007
07
01
1
8
http://jiei.azad.ac.ir/article_511077_f3afecb7aeb4a992f5d5128601966798.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2007
3
5
Numerical modeling of economic uncertainty
H
Schjær-Jacobsen
Representation and modeling of economic uncertainty is addressed by different modeling methods, namely stochastic variables and probabilities, interval analysis, and fuzzy numbers, in particular triple estimates. Fo-cusing on discounted cash flow analysis numerical results are presented, comparisons are made between alter-native modeling methods, and characteristics of the methods are discussed.
Economic uncertainty
Modeling
stochastic
Probability
Interval
fuzzy number
Triple esti-mate
Discounted Cash Flow
2007
07
01
9
18
http://jiei.azad.ac.ir/article_511078_4ec205ffc7734f99cf0c7876f655299a.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2007
3
5
Fuzzy model for risk analysis
F
Luban
The goal of this paper is to show how the concept of fuzzy logic can be used to establish a degree to which an investment project belongs to a class of risk. Also, the probability of the fuzzy event is presented and is ap-plied to calculate the probability of the fuzzy event “the project X is a good investment”. This process has to enable the decision maker to compare several alternative investments from the fuzzy logic perspective and, in this way, allows him to include the uncertainty that comes with the problem in reality. Moreover, by experi-ments with the proposed fuzzy model the user can obtain new knowledge for investment risk analysis.
fuzzy numbers
fuzzy logic
Fuzzy probability
Risk analysis
2007
07
01
19
26
http://jiei.azad.ac.ir/article_511079_ab49ee3cabe830a9ab5421cdae0dba54.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2007
3
5
A new Simulated Annealing algorithm for the robust coloring problem
M.A
Gutiérrez-Andrade
P
Lara-Velázquez
S.G
de-los-Cobos-Silva
The Robust Coloring Problem (RCP) is a generalization of the well-known Graph Coloring Problem where we seek for a solution that remains valid when extra edges are added. The RCP is used in scheduling of events with possible last-minute changes and study frequency assignments of the electromagnetic spectrum. This problem has been proved as NP-hard and in instances larger than 30 vertices, meta-heuristics are required. In this paper a Simulated Annealing Algorithm is proposed, and his performance is compared against other tech-niques such as GRASP, Tabu Search and Scatter Search. In the classic instances of the problem our proposal method which gives the best solutions at this moment.
Robust coloring problem
Graph coloring
Heuristics
Simulated Annealing
2007
07
01
27
32
http://jiei.azad.ac.ir/article_511080_89715ace7269f08dbfa8e32cffc3acdd.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2007
3
5
Correlation coefficient of intuitionistic fuzzy sets
W
Zeng
H
Li
Based on the point of view of geometrical representation of an intuitionistic fuzzy set, we take into account all three parameters describing intuitionistic fuzzy set, propose a kind of new method to calculate correlation and correlation coefficient of intuitionistic fuzzy sets which is similar to the cosine of the intersectional angle in finite sets and probability space, respectively. Further, we discuss some of their properties and give three numerical examples to illustrate our proposed method reasonable.
Intuitionistic fuzzy set
fuzzy set
Correlation
Correlation Coefficient
2007
07
01
33
40
http://jiei.azad.ac.ir/article_511081_2a005d2c8358b9696bced128682e9985.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2007
3
5
A fuzzy approach to the evaluation of human factors in ultrasonic nondestructive examinations
J
Domech Moré
A.S
Guimarães
G
Bonorino Xexéo
R
Tanscheit
Human factors are among the main elements affecting the reliability of nondestructive examinations (NDE). In a man-machine system, human reliability is affected by many factors (performance shaping factors) whose influence on reliability cannot be easily expressed quantitatively. This paper identifies and ranks 59 perform-ance shaping factors by using a fuzzy reasoning method and proposes a procedure to measure them. This will determine the Quality Standard (QS) for a NDE system so that human reliability in ultrasonic nondestructive examinations can be qualitatively evaluated.
Human reliability
Ultrasonic nondestructive examinations
fuzzy sets
Human Factors
Perform-ance shaping factors
2007
07
01
41
52
http://jiei.azad.ac.ir/article_511082_ec11a03a2374b716b8b2f6db3e4cff98.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2007
3
5
Comparison of the performances of neural networks specification, the Translog and the Fourier flexible forms when different production technologies are used
R
Feki
This paper investigates the performances of artificial neural networks approximation, the Translog and the Fourier flexible functional forms for the cost function, when different production technologies are used. Using simulated data bases, the author provides a comparison in terms of capability to reproduce input demands and in terms of the corresponding input elasticities of substitution estimates. The results suggest that ANN provide a better approximation than other traditional functional forms only when a single technology is used. However, when elasticities of substitution are calculated, the Translog approximate batters the true technology in both single and mixed technology.
Artificial Neural Networks
Cost function
Flexible functional forms
2007
07
01
53
60
http://jiei.azad.ac.ir/article_511083_4b955eb975fb2058e01970f6c4313c29.pdf