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  • 标题:Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain
  • 本地全文:下载
  • 作者:Muthusamy Aravendan ; Ramasamy Panneerselvam
  • 期刊名称:Intelligent Information Management
  • 印刷版ISSN:2150-8194
  • 电子版ISSN:2150-8208
  • 出版年度:2015
  • 卷号:07
  • 期号:06
  • 页码:313-338
  • DOI:10.4236/iim.2015.76025
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要: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.
  • 关键词:Closed Loop Supply Chain;Genetic Algorithms;HGA;Meta-Heuristics;MINLP;Model;Network Design;Optimization
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