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An Optimized Unbiased GM (1, 1) Power Model for Forecasting MRO Spare Parts Inventory

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  • Chen Bing
  • Sun Shouqun
  • Liu Gang

Abstract

With respect to the problem of complexity and uncertainty in the MRO (Maintenance, Repair and Overhaul) spare parts inventory, an optimized grey forecasting model is employed to forecast the demand of spare parts. The parameter g is optimized based on genetic algorithm (GA) method in the unbiased GM (1, 1) power model to minimize the ARPE (Average Relative Proportional Error) of accuracy. And the optimal model is used to forecast the prediction demand in a practical example. The experiment results indicate the forecasting accuracy can be accepted by using the optimized unbiased GM (1, 1) power model.

Suggested Citation

  • Chen Bing & Sun Shouqun & Liu Gang, 2012. "An Optimized Unbiased GM (1, 1) Power Model for Forecasting MRO Spare Parts Inventory," Modern Applied Science, Canadian Center of Science and Education, vol. 6(6), pages 1-12, June.
    • Handle: RePEc:ibn:masjnl:v:6:y:2012:i:6:p:12
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    References listed on IDEAS

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    1. Wang, Wenbin & Syntetos, Aris A., 2011. "Spare parts demand: Linking forecasting to equipment maintenance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1194-1209.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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