文献类型：期刊文献

中文题名：一种新的不常用备件需求预测和库存优化方法

英文题名：A New Approach for Demand Forecasting and Inventory Optimization of the Rarely Used Spare Parts

作者：孔子庆[1];刘白杨[2];刘济[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]国家管网集团北方管道有限责任公司压缩机组维检修中心,河北廊坊065000

年份：2022

卷号：48

期号：3

起止页码：366

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家自然科学基金(61673175)。

语种：中文

中文关键词：间断需求;需求间隔;高斯过程回归;Bootstrap;概率分布;库存控制

外文关键词：intermittent demand;demand interval;Gaussian process regression;Bootstrap;probability distribution;inventory control

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相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。两个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库存决策实现了更低的库存总费用,具有实用性。
The main characteristic of rarely used spare parts include the sharp change of demand, and long and uncertain demand interval, which will lead to inaccurate prediction of the spare part demand and difficult to make a reasonable inventory decision. Aiming at the above issues, this work proposes a novel demand forecasting and inventory optimization method to improve the accuracy of decision-making. In the proposed method, the Gaussian process regression is used to forecast the demand interval, and then, the Bootstrap augmented sample statistical method is combined to predict the probability distribution of spare parts demand. Based on the obtained demand probability statistics, a stochastic inventory model of total inventory cost is established, and the particle swarm algorithm is further utilized to search the optimal inventory decision variable. The experimental results of two groups of practical industrial spare parts show that the proposed method has high prediction accuracy. Meanwhile, the obtained inventory decision can achieve lower total inventory cost on the premise of satisfying service level, which illustrates the practicality of the proposed prediction and optimization method of infrequent spare parts method.