A New Approach for Demand Forecasting and Inventory Optimization of the Rarely Used Spare Parts
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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。Abstract: The rarely used spare parts’ demand usually changes sharply, and the demand interval is long and uncertain, which results in inaccurate prediction of spare part demand and the difficulty to make a reasonable inventory decision. A novel method is proposed for demand forecasting and inventory optimization. In the proposed method, the Gaussian process regression is used to forecast the demand interval, and then combined to an augmented sample statistical method called by Bootstrap, to predict the probability distribution of spare parts demand. Based on the demand probability statistics, the stochastic inventory model on the total inventory cost can be established. The particle swarm algorithm is used to solve the problem of minimizing the total cost for inventory decision. The experimental results from two sets of real data show that the proposed method has higher prediction accuracy. Besides, the inventory decision achieves the goal of minimum inventory cost subject to service level of spare parts, which shows the practicality of the proposed method.
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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 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
表 1 本文方法与其他算法的比较
Table 1. Comparison of the proposed method and other algorithms
Method Data 1 Data 2 MAE RMSE MAE RMSE This paper 0.398 0.208 0.4980 0.4480 ES 0.757 0.787 0.7980 0.8204 SVM 0.900 1.157 0.6630 0.5890 
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表 2 两种方法的评估结果
Table 2. Evaluation results of two methods
Lead period Actual demamd This paper Literature[10] Confidence interval Safety stock Confidence interval Safety stock 5 2 [0,4] 3 [0,6] 4 8 4 [1,6] 4 [0,8] 6 11 5 [1,7] 6 [1,10] 8
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表 3 历史需求量统计信息
Table 3. Historical demand statistics
Demand quantity Occurrence number Frequency/% 0 25 19.53 1 21 16.41 2 33 25.78 3 25 19.53 4 13 10.16 5 3 2.34 6 4 3.13 7 2 1.56 8 1 0.78 9 1 0.78
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