TITLE:
Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain
AUTHORS:
Muthusamy Aravendan, Ramasamy Panneerselvam
KEYWORDS:
Closed Loop Supply Chain, Genetic Algorithms, HGA, Meta-Heuristics, MINLP, Model, Network Design, Optimization
JOURNAL NAME:
Intelligent Information Management,
Vol.7 No.6,
November
24,
2015
ABSTRACT: This paper presents four different hybrid
genetic algorithms for network design problem in closed loop supply chain. They
are compared using a complete factorial experiment with two factors, viz.
problem size and algorithm. Based on the significance of the factor
“algorithm”, the best algorithm is identified using Duncan’s multiple range
test. Then it is compared with a mathematical model in terms of total cost. It
is found that the best hybrid genetic algorithm identified gives results on par
with the mathematical model in statistical terms. So, the best algorithm out of
four algorithm proposed in this paper is proved to be superior to all other
algorithms for all sizes of problems and its performance is equal to that of
the mathematical model for small size and medium size problems.