A class of expected value multi-objective programming problems with random rough coefficients

Authors:
Jiuping Xu;Liming Yao
Affiliations:
Uncertainty Decision-Making Laboratory, School of Business and Administration, Sichuan University, Chengdu, 610064, PR China;Uncertainty Decision-Making Laboratory, School of Business and Administration, Sichuan University, Chengdu, 610064, PR China
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2009

Citing 10
Cited 0

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms

Genetic Algorithms
Theory and Practice of Uncertain Programming

Theory and Practice of Uncertain Programming
Constrained Multiobjective Optimization Immune Algorithm: Convergence and Application

Computers & Mathematics with Applications
An overview of evolutionary algorithms in multiobjective optimization

Evolutionary Computation
A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor

Information Sciences: an International Journal
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A class of multiobjective linear programming models with random rough coefficients

Mathematical and Computer Modelling: An International Journal
A class of multiobjective linear programming model with fuzzy random coefficients

Mathematical and Computer Modelling: An International Journal
A fuzzy random multiob jective 0-1 programming based on the expectation optimization model using possibility and necessity measures

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.98

Visualization

Abstract

The emphasis of this paper is to introduce two kinds of random rough variables. One is the discrete random rough variable, the other is the continuous random rough variable. The expected value model with random rough coefficients is proposed and some special models are discussed. In order to compute the expected value of some complicated functions, the technique of the random rough simulation is presented. Combined with genetic algorithm, it is applied to check the objective function and constraint function, and to obtain the optimal solution. Finally, some numerical examples are provided to show the effectiveness of the proposed technique.