Multi-Objective Optimization Problems in Taguchi Parameter Design – A Literature Review

2015 ◽  
Vol 813-814 ◽  
pp. 1188-1192
Author(s):  
S. Rajarasalnath ◽  
K. Balasubramanian ◽  
N. Rajeswari

With the advent of latest technology, manufacturing process becomes so sophisticated and complicated that a single response variable (quality characteristic) can not reflect the true product quality and there is intense competition between market participants for cost and delivery (productivity) as well. Any manufacturing process in the present state requires multi objective optimization model to optimize quality, cost and productivity simultaneously. Optimum prediction is critical as it requires lot of experiments for data capturing which involves time and cost. In the present manufacturing set up, it is essential to identify optimum parametric combination for multi objective function real time problems with lesser experiments and lesser effort with better accuracy. Taguchi method is a well known fraction factorial design, which requires minimum number of trials for Identifying optimum parametric combination in real time problems.In this paper an attempt is made to review the literatures of various methods used by researchers for multi - objective optimization problems using Taguchi methods.

2021 ◽  
pp. 103546
Author(s):  
Cristóbal Barba-González ◽  
Antonio J. Nebro ◽  
José García-Nieto ◽  
María del Mar Roldán-García ◽  
Ismael Navas-Delgado ◽  
...  

2013 ◽  
Vol 307 ◽  
pp. 161-165
Author(s):  
Hai Jin ◽  
Jin Fa Xie

A multi-objective genetic algorithm is applied into the layout optimization of tracked self-moving power. The layout optimization mathematical model was set up. Then introduced the basic principles of NSGA-Ⅱ, which is a Pareto multi-objective optimization algorithm. Finally, NSGA-Ⅱwas presented to solve the layout problem. The algorithm was proved to be effective by some practical examples. The results showed that the algorithm can spread toward the whole Pareto front, and provide many reasonable solutions once for all.


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