Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 51, Issue 17, Pages 5039-5054Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2013.784411
Keywords
Genetic algorithm (GA); Response surface methodology (RSM); Assembly line balancing; Parameter optimisation; Design of experiment
Ask authors/readers for more resources
Genetic algorithms (GAs) are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP-hard problems. Genetic algorithm includes a number of parameters whose different levels strictly affect the performance of the algorithm. The general approach to determine the appropriate parameter combination of GA depends on too many trials of different combinations, and the best one of them that produces good results is selected for the programme, which would be used for problem solving. A few researchers studied on the parameter optimisation of GA. In this paper, response surface-dependent parameter optimisation is proposed to determine the optimal parameters of GA. Results are tested for benchmark problems that are most common in mixed-model assembly line balancing problems of type-I.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available