Volume 18, Issue 2 (5-2021)                   jor 2021, 18(2): 107-124 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Fasihi M, Najafi S E, Tavakkoli-Moghaddam R, Hahiaghaei-Keshteli M. Combined Method of the Taguchi Approach and DEA for Setting Parameters and Operators of Metaheuristic Algorithms - Genetic Algorithm to Solve the Reentrant Permutation Flow Shop Problem. jor 2021; 18 (2) :107-124
URL: http://jamlu.liau.ac.ir/article-1-1625-en.html
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Abstract:   (1620 Views)
The efficiency of metaheuristic algorithms has a direct relationship with their parameters setting, so that the incorrect selection of completely effective algorithmic parameters could make them inefficient. In this research, the combination of Taguchi approach and the Data Envelopment Analysis (DEA) method are applied to enhance the efficiency of the genetic algorithm to solve the Reentrant Permutation Flow Shop (RPFS) problem. Various scenarios are formed to select genetic algorithm’s operators for units under evaluation. First, using the Taguchi method for each unit, the optimal parameters are specified with the goal of minimizing the objective function (maximum tardiness). Then the effective units are determined and ranked in order to specify the best operators of the algorithm according to the optimal objective function in the shortest possible time. This research can be used as a method for setting the parameters of other evolutionary and metaheuristic algorithms in order to avoid the disadvantages of the trial and error methods.
Full-Text [PDF 996 kb]   (872 Downloads)    
Type of Study: Research | Subject: Special
Received: 2019/12/9 | Accepted: 2021/01/13

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.