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  • ISSN 1006-3080
  • CN 31-1691/TQ

一种新的不常用备件需求预测和库存优化方法

孔子庆 刘白杨 刘济

孔子庆, 刘白杨, 刘济. 一种新的不常用备件需求预测和库存优化方法[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210223004
引用本文: 孔子庆, 刘白杨, 刘济. 一种新的不常用备件需求预测和库存优化方法[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210223004
KONG Ziqing, LIU Baiyang, LIU Ji. A New Approach for Demand Forecasting and Inventory Optimization of the Rarely Used Spare Parts[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210223004
Citation: KONG Ziqing, LIU Baiyang, LIU Ji. A New Approach for Demand Forecasting and Inventory Optimization of the Rarely Used Spare Parts[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210223004

一种新的不常用备件需求预测和库存优化方法

doi: 10.14135/j.cnki.1006-3080.20210223004
基金项目: 国家自然科学基金(61673175)
详细信息
    作者简介:

    孔子庆(1995-),男,江苏连云港人,硕士生,研究方向为机器学习、数据挖掘、库存管理、智能控制。E-mail:406805342@qq.com

    通讯作者:

    刘 济,E-mail:jiliu@ecust.edu.cn

  • 中图分类号: TP391

A New Approach for Demand Forecasting and Inventory Optimization of the Rarely Used Spare Parts

  • 摘要: 不常用备件具有需求量变化剧烈、需求发生间隔期长且不确定的特点,从而引发了备件需求预测失准而无法做出合理库存决策的问题。为此,提出了一种新的需求预测和库存优化结合的方法以提高决策的精准度。利用高斯过程回归法预测需求发生间隔,结合Bootstrap增广样本统计方法来预测备件的需求概率分布;基于该概率统计结果,建立库存总成本最小的随机库存优化模型,采用粒子群算法求解得到最优库存决策变量。两种实际工业备件的实验结果表明,本文方法具有更高的预测精度,同时,在满足服务水平约束下的库存决策实现了更低的库存总费用,具有实用性。

     

  • 图  1  不常用备件库存决策流程

    Figure  1.  Rarely used spare parts inventory decision process

    图  2  需求量概率分布预测流程图

    Figure  2.  Flow chart of probabilistic distribution forecast

    图  3  基于GPR的备件需求间隔预测结果

    Figure  3.  Spare parts demand interval prediction results based on GPR

    图  4  提前期内需求量概率直方图

    Figure  4.  Probability histogram of quantity demanded in lead period

    图  5  提前期内需求量累积分布直方图

    Figure  5.  Histogram of cumulative distribution of demand in lead period

    图  6  最小总成本优化过程图

    Figure  6.  Optimization process diagram of minimum total cost

    表  1  本文方法与其他算法的比较

    Table  1.   Comparison of the proposed method and other algorithms

    MethodData 1Data 2
    MAERMSEMAERMSE
    This paper0.3980.2080.49800.4480
    ES0.7570.7870.79800.8204
    SVM0.9001.1570.66300.5890
    下载: 导出CSV

    表  2  两种方法的评估结果

    Table  2.   Evaluation results of two methods

    Lead periodActual demamdThis paperLiterature[10]
    Confidence intervalSafety stockConfidence intervalSafety stock
    52[0,4]3[0,6]4
    84[1,6]4[0,8]6
    115[1,7]6[1,10]8
    下载: 导出CSV

    表  3  历史需求量统计信息

    Table  3.   Historical demand statistics

    Demand quantityOccurrence numberFrequency/%
    02519.53
    12116.41
    23325.78
    32519.53
    41310.16
    532.34
    643.13
    721.56
    810.78
    910.78
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-02-23
  • 网络出版日期:  2021-05-20

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