Taguchi-factorial type-2 fuzzy random optimization model for planning conjunctive water management with compound uncertainties

2017 ◽  
Vol 97 ◽  
pp. 184-200 ◽  
Author(s):  
C.X. Wang ◽  
Y.P. Li ◽  
G.H. Huang
Keyword(s):  
Water Management ◽  
Optimization Model ◽  
Random Optimization ◽  
Conjunctive Water Management ◽  
Fuzzy Random

scholarly journals A TYPE-2 FUZZY OPTIMIZATION MODEL FOR PROJECT PORTFOLIO SELECTION AND SCHEDULING INCORPORATING PROJECT INTERDEPENDENCY AND SPLITTING

2021 ◽  
Vol 27 (2) ◽  
pp. 493-510
Author(s):  
Samaneh Zolfaghari ◽  
Seyed Meysam Mousavi ◽  
Jurgita Antuchevičienė
Keyword(s):  
Portfolio Selection ◽  
Optimization Model ◽  
Fuzzy Optimization ◽  
Project Portfolio ◽  
Time Value Of Money ◽  
Solution Approach ◽  
Project Portfolio Selection ◽  
The Interest Rate ◽  
Interval Type

This paper presents a new optimization model and a new interval type-2 fuzzy solution approach for project portfolio selection and scheduling (PPSS) problem, in which split of projects and re-execution are allowable. Afterward, the approach is realized as a multi-objective optimization that maximizes total benefits of projects concerning economic concepts by considering the interest rate and time value of money and minimizes the tardiness value and total number of interruptions of chosen projects. Besides, budget and resources limitation, newfound relations are proposed to consider dependency relationships via a synergy among projects to solve PPSS problem hiring interval type-2 fuzzy sets. For validation of the model, numerical instances are provided and solved by a new extended procedure based on fuzzy optimistic and pessimistic viewpoints regarding several situations. In the end, their results are studied. The results show that it is more beneficial when projects are allowed to be split.

A game theoretical low impact development optimization model for urban storm water management

2019 ◽  
Vol 241 ◽  
pp. 118323 ◽  
Author(s):  
Morvarid Latifi ◽  
Gholamreza Rakhshandehroo ◽  
Mohammad Reza Nikoo ◽  
Mojtaba Sadegh
Keyword(s):  
Water Management ◽  
Optimization Model ◽  
Low Impact Development ◽  
Storm Water ◽  
Storm Water Management ◽  
Urban Storm

THE INDEPENDENCE OF FUZZY VARIABLES WITH APPLICATIONS TO FUZZY RANDOM OPTIMIZATION

2007 ◽  
Vol 15 (supp02) ◽  
pp. 1-20 ◽  
Author(s):  
YIAN-KUI LIU ◽  
JINWU GAO
Keyword(s):  
Distribution Function ◽  
Sufficient Conditions ◽  
Possibility Distribution ◽  
Fuzzy Variables ◽  
Fundamental Properties ◽  
Random Optimization ◽  
Fuzzy Random Programming ◽  
Necessary And Sufficient ◽  
The Relationship ◽  
Fuzzy Random

This paper presents the independence of fuzzy variables as well as its applications in fuzzy random optimization. First, the independence of fuzzy variables is defined based on the concept of marginal possibility distribution function, and a discussion about the relationship between the independent fuzzy variables and the noninteractive (unrelated) fuzzy variables is included. Second, we discuss some properties of the independent fuzzy variables, and establish the necessary and sufficient conditions for the independent fuzzy variables. Third, we propose the independence of fuzzy events, and deal with its fundamental properties. Finally, we apply the properties of the independent fuzzy variables to a class of fuzzy random programming problems to study their convexity.

Optimization Model of Loan Portfolio Based on Mean Chance

2010 ◽  
Vol 108-111 ◽  
pp. 488-493
Author(s):  
Dong Jing Pan
Keyword(s):  
Neural Network ◽  
Optimization Model ◽  
Real Life ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Loan Portfolio ◽  
Hybrid Intelligent Algorithm ◽  
New Model ◽  
Fuzzy Random ◽  
Mean Chance

This paper discusses the problem of loan portfolio in fuzzy random environment, in real life, because of the influence of random and fuzzy factors, the return rates of loan in bank often have fuzzy random characteristic. Mean chance is a measure of fuzzy random variable, based on mean chance, a new optimization model of loan portfolio is provided. To give a general solution to the new model, a hybrid intelligent algorithm is designed. The algorithm integrates fuzzy random simulation, neural network and genetic algorithm. Neural network is employed to calculate the expected value and the mean chance value, it greatly reduce the computational work. At last, a numerical example is presented to illustrate the new model and the proposed new algorithm.

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