基于张量优化,对零件的间歇性需求进行稳健的间隔预测
Kairong Hong1, Yingying Ren1, Fengyuan Li1
1China Railway Tunnel Group, Zhengzhou 450001, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一种新的可靠间隔预测方法,用于间歇性备件需求. 张量优化方法有效地捕捉趋势并提高准确性,为售后市场服务提供可靠的预测.
科学领域:
- 运营研究 运营研究
- 数据科学数据科学数据科学
- 制造业 工程 制造工程
背景情况:
- 大型制造企业的售后服务依赖于准确的备件需求预测,用于库存和质量管理.
- 间歇性的备件需求表现出随机波动和异常值,挑战了传统的时间序列预测方法.
- 现有的方法很难捕捉进化模式,并为杂的间歇性数据提供可靠的预测.
研究的目的:
- 为售后零件需求的间歇时间序列提出一个强大的间隔预测方法.
- 为了应对需求数据中随机波动,异常值和间歇性特征的挑战.
- 为了提高备件需求预测对售后市场服务的可靠性和准确性.
主要方法:
- 一个序列平滑网络使用张量分解 (塔克分解) 和堆叠的自动编码器来识别需求数据.
- 一种交替优化算法,可以从间歇序列中提取进化趋势并优化特征表示.
- 一个具有动态更新的适应间隔预测算法,用于点和间隔预测.
主要成果:
- 拟议的张量优化方法有效地捕捉了间歇序列的进化趋势,优于传统方法.
- 使用现实世界的售后数据证明了预测准确度的提高,特别是对于小样本间歇序列,使用现实世界的售后数据.
- 该方法提供可靠,弹性预测间隔,减轻因数据扭曲引起的问题.
结论:
- 基于张量优化的稳健间隔预测方法为间歇性备件需求的准确和可靠预测提供了一个新的解决方案.
- 这种方法提高了智能规划和决策在实际的维护和售后服务.
- 该方法处理噪音和间歇性数据的能力在需求预测方面取得了重大进展.
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