An improved genetic algorithm for the multi-echelon inventory problem of repairable spare parts

Repairable spare parts are crucial material basis for equipment support, and the multi-echelon inventory control of it is an important practical problem. In this paper, an improved genetic algorithm for the multi-inventory problem of repairable spare parts was proposed. In our algorithm, three cross...

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Bibliographic Details
Published in2010 IEEE International Conference on Intelligent Computing and Intelligent Systems Vol. 1; pp. 440 - 444
Main Authors Sun Jiangsheng, Zhao Fanggeng, Zhang Lianwu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2010
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genetic algorithm
genetic operator
multi-echelon inventory problem
repairable spare parts
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Summary:Repairable spare parts are crucial material basis for equipment support, and the multi-echelon inventory control of it is an important practical problem. In this paper, an improved genetic algorithm for the multi-inventory problem of repairable spare parts was proposed. In our algorithm, three crossover operators and a mutation operator were implemented, and a local search procedure that includes two heuristics was integrated into the algorithm. The comparison experiments of different genetic operator combinations were performed, and computational results clearly show that the improved genetic algorithm for the multi-inventory problem of repairable spare parts is more efficient than previous genetic algorithm.
ISBN:9781424465828
1424465826
DOI:10.1109/ICICISYS.2010.5658605