Volume 28, Number 2 (IJIEPR 2017)                   IJIEPR 2017, 28(2): 151-161 | Back to browse issues page



DOI: 10.22068/ijiepr.28.2.151

XML Print


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

Babazadeh R, Tavakkoli-Moghaddam R. A hybrid GA-TLBO algorithm for optimizing a capacitated three-stage supply chain network . IJIEPR. 2017; 28 (2) :151-161
URL: http://ijiepr.iust.ac.ir/article-1-725-en.html

Prof. University of Tehran , tavakoli@ut.ac.ir
Abstract:   (151 Views)

A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND problem as a strategic level decision-making problem in supply chain management is an NP-hard class of computational complexity. To escape infeasible solutions emerged in the problem of interest due to realistic constraints, combination of a random key and priority-base encoding scheme is also used. To assess the quality of the proposed hybrid GA-TLBO algorithm, some numerical examples are conducted. Then, the results are compared with the GA, TLBO, differential evolution (DE) and branch-and -bound algorithms. Finally, the conclusion is provided.

Full-Text [PDF 784 kb]   (45 Downloads)    
Type of Study: Research | Subject: Logistic & Apply Chain
Received: 2017/03/11 | Accepted: 2017/07/15 | Published: 2017/07/15

Add your comments about this article : Your username or email:
Write the security code in the box

Send email to the article author


© 2015 All Rights Reserved | International Journal of Industrial Engineering & Production Research

Designed & Developed by : Yektaweb