| 引用本文: | 林琳,陈湘芝,钟诗胜.一种新的间断型备件需求预测方法[J].哈尔滨工业大学学报,2016,48(1):40.DOI:10.11918/j.issn.0367-6234.2016.01.006 |
| LIN Lin,CHEN Xiangzhi,ZHONG Shisheng.A new approach of forecasting intermittent demand for spare parts[J].Journal of Harbin Institute of Technology,2016,48(1):40.DOI:10.11918/j.issn.0367-6234.2016.01.006 |
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| 摘要: |
| 针对间断型需求因需求发生随机、需求量值波动大而导致预测困难这一问题，提出一种新的备件需求预测方法.该方法能分别预测需求发生时间和非零需求发生时的需求量值.对于0-1需求发生时间序列，采用调制方法对其进行平滑处理，运用神经网络对调制后的0-1时间序列进行预测，获得需求发生时间的预测值.采用时间聚合方法对实际备件需求时间序列进行预测，将滚动预测应用到解聚合过程中，得到备件的需求量预测值.使用三一重工砼活塞和核电设备的备件需求数据对方法进行验证，结果表明，该方法的预测精度要优于Croston方法、指数平滑法以及BP神经网络，证明了所提方法的有效性和准确性.
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| 关键词: 备件需求 需求预测 间断需求 神经网络 时间聚合 调制 |
| DOI：10.11918/j.issn.0367-6234.2016.01.006 |
| 分类号:TP301；F272.1 |
| 文献标识码:A |
| 基金项目:国家高技术研究发展计划(2015BAF32B01-4). |
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| A new approach of forecasting intermittent demand for spare parts |
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LIN Lin,CHEN Xiangzhi,ZHONG Shisheng
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(School of Mechatronics Engineering, Harbin Institute of Technology, 150001 Harbin, China)
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| Abstract: |
| Intermittent demand is characterized by infrequent demand arrivals and variable demand sizes, which results in the difficult of demand forecasting. To solve this problem, a new approach was developed to forecast spare parts demand. The methodology provided mechanism to forecast the demand arrivals couple with the demand values when demand occurs. It firstly used the method of modulation to transform the 0-1 demand arrival time series into the continuous time series, and then adopted the neural network model to forecast the processed time series. Next, it applied the method of time aggregation to forecast the real demand time series, and took the rolling forecasting method into disaggregating, then got the predictive demand values. Applying this approach in forecasting the spare parts of the nuclear power equipment, the experimental results showed that the prediction accuracy was superior to Croston's method, exponential smoothing and BP neural network, which proved the methodology to be effective and accurate.
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| Key words: demand forecasting intermittent demand neural network time aggregation modulation |
