Birandom variables and birandom programming

Authors:
Jin Peng;Baoding Liu
Affiliations:
Institute of Uncertain Systems, College of Mathematical and Information Sciences, Huanggang Normal University, Hubei 438000, China;Uncertainty Theory Laboratory, Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China
Venue:
Computers and Industrial Engineering
Year:
2007

Citing 10
Cited 3

Challenges in stochastic programming

Mathematical Programming: Series A and B
Comparative evaluation of genetic algorithm and backpropagation for training neural networks

Information Sciences—Informatics and Computer Science: An International Journal
Programming Microsoft Office 2000 Web Components with Cdrom

Programming Microsoft Office 2000 Web Components with Cdrom
Uncertain Programming

Uncertain Programming
Network design techniques using adapted genetic algorithms

Advances in Engineering Software
Random fuzzy dependent-chance programming and its hybrid intelligent algorithm

Information Sciences—Informatics and Computer Science: An International Journal
Theory and Practice of Uncertain Programming

Theory and Practice of Uncertain Programming
Expected value operator of random fuzzy variable and random fuzzy expected value models

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Fuzzy random chance-constrained programming

IEEE Transactions on Fuzzy Systems
Fuzzy random dependent-chance programming

IEEE Transactions on Fuzzy Systems

A class of multi-objective expected value decision-making model with birandom coefficients and its application to flow shop scheduling problem

Information Sciences: an International Journal
A class of multiobjective linear programming models with random rough coefficients

Mathematical and Computer Modelling: An International Journal
Original Articles: A class of multi-objective equilibrium chance maximization model with twofold random phenomenon and its application to hydropower station operation

Mathematics and Computers in Simulation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The emphasis of this paper is to introduce a novel concept of birandom variable and to exhibit the framework of birandom programming. The so-called birandom variable is a measurable mapping from a probability space to a collection of random variables. Based on this definition, the expected value operator of birandom variable and chance measures of birandom event are further introduced. As a generalized scenario of stochastic programming, a spectrum of birandom programming models are developed to deal with birandom systems. To solve the proposed models, birandom simulations are presented and then a hybrid intelligent algorithm is designed by embedding neural networks into genetic algorithm. Finally, some numerical experiments are provided to illustrate the effectiveness of the algorithm.