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Information Technology Journal

Year: 2013 | Volume: 12 | Issue: 22 | Page No.: 6901-6908
DOI: 10.3923/itj.2013.6901.6908
An Adaptive Genetic Algorithm to Optimize Two-echelon Automotive Spare Parts Inventory
Hu Xiao-Jian, Kan Yan-Jiao, Zhang Yan-Li and Zhao Ju

Abstract: This study considers multiple product suppliers, multiple regional distribution centers and multiple automotive 4S stores and implement the reorder point, order quantity (R, Q) inventory policy with the background of automobile companies based on a two-echelon non-repairable automotive service parts inventory system that consists of single center warehouse. A mathematical model with the objective of minimizing the total annual inventory investment subject to constraints on the average annual order frequency, expected number of backorders and budget is formulated. An adaptive genetic algorithm is used to solve the problem and a numerical example was given to show that the algorithm is effective.

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How to cite this article
Hu Xiao-Jian, Kan Yan-Jiao, Zhang Yan-Li and Zhao Ju, 2013. An Adaptive Genetic Algorithm to Optimize Two-echelon Automotive Spare Parts Inventory. Information Technology Journal, 12: 6901-6908.

Keywords: Two-echelon inventory, the adaptive genetic algorithm and spare parts

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