Department of Industrial Engineering,Sharif University of Technology
Abstract
In this paper, two methods to solve multi-response statistical problems are presented. In these methods, desirability function, genetic algorithm and simulation methodology are applied. The desirability function is responsible for modeling the multi-response statistical problem, the genetic algorithm tries to optimize the model and, finally, the simulation approach generates the required input data from a simulated system. The methods differ from each other in controlling the randomness of the problem. In the first method, replications control this randomness, while, in the second method, the randomness is controlled by a statistical test. Furthermore, these methods are compared by designed experiments and the results are reported.
Pasandideh,S and Akhavan Niaki,S . (2006). Optimizing Multi-Response Statistical Problems Using a Genetic Algorithm. (e2562). Scientia Iranica, 13(1), e2562
MLA
Pasandideh,S , and Akhavan Niaki,S . "Optimizing Multi-Response Statistical Problems Using a Genetic Algorithm" .e2562 , Scientia Iranica, 13, 1, 2006, e2562.
HARVARD
Pasandideh S, Akhavan Niaki S. (2006). 'Optimizing Multi-Response Statistical Problems Using a Genetic Algorithm', Scientia Iranica, 13(1), e2562.
CHICAGO
S Pasandideh and S Akhavan Niaki, "Optimizing Multi-Response Statistical Problems Using a Genetic Algorithm," Scientia Iranica, 13 1 (2006): e2562,
VANCOUVER
Pasandideh S, Akhavan Niaki S. Optimizing Multi-Response Statistical Problems Using a Genetic Algorithm. Scientia Iranica. 2006;13(1):e2562.
