Bayesian Inventory and Replacement Models Using Real-Time Condition Monitoring Information

使用实时状态监测信息的贝叶斯库存和更换模型

• 批准号: 0856379
• 负责人: Jennifer Ryan
• 金额: $ 18.82万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-03-15 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0856379&HistoricalAwards=false
• 关键词: Bayesian; Inventory; Replacement; Models; Using

Bayesian Inventory and Replacement Models Using Real-Time Condition Monitoring InformationAbstractThis grant provides funding to develop methods for incorporating real-time sensor information obtained through condition monitoring (CM) into inventory management and replacement decisions for service parts, with the goal of improving the management of service parts inventories. This research considers the problem faced by a manufacturer who manages inventory for a machine part that is used on a large number of geographically-dispersed machines and which is subject to deterioration. The degradation signal generated by the deterioration of an individual part, and captured via CM, is modeled using a Wiener process. This degradation model, which can be used to predict part failure, as well as demand for service parts, is periodically updated as new sensor information is obtained. A stochastic inventory model that incorporates the updated demand distribution will be developed and the form of the optimal inventory control policy will be studied. While adaptive inventory policies such as those developed in this research can help manufacturers to increase machine availability and reduce inventory costs, their implementation can be computationally intensive and may require the retention of a significant quantity of sensor data. Thus, this research will focus on the development of computationally tractable methods that take advantage of the available sensor information.If successful, this research will develop implementable and broadly applicable tools that will be of practical value to manufacturing firms, assisting them in taking advantage of CM technology to improve their after-sales service and to compete more effectively. This research also seeks to contribute to the development of essential linkages between condition monitoring technology, part maintenance and replacement, and inventory management. This research identifies a critical area for interdisciplinary collaboration, seeking to build connections between research on CM technology and research on the development of decision models that make use of information obtained through CM.

贝叶斯库存和更换模型使用实时状态监测信息摘要该基金提供资金，以开发方法，将通过状态监测（CM）获得的实时传感器信息纳入库存管理和更换决策的服务部件，以改善服务部件库存管理的目标。本研究考虑了制造商所面临的问题，该制造商管理用于大量地理上分散的机器并且容易变质的机器部件的库存。 使用Wiener过程对由单个部件的劣化产生并通过CM捕获的劣化信号进行建模。这种退化模型，它可以用来预测零件故障，以及对服务零件的需求，定期更新新的传感器信息的获得。将建立一个包含最新需求分布的随机库存模型，并研究最优库存控制策略的形式。 虽然本研究中开发的自适应库存策略可以帮助制造商提高机器可用性并降低库存成本，但它们的实施可能是计算密集型的，并且可能需要保留大量的传感器数据。因此，本研究的重点将是开发利用现有传感器信息的计算方法，如果成功的话，本研究将开发可实施和广泛适用的工具，将具有实用价值的制造企业，帮助他们利用CM技术，以改善他们的售后服务，更有效地竞争。 这项研究还旨在促进状态监测技术，零件维护和更换，库存管理之间的重要联系的发展。本研究确定了跨学科合作的一个关键领域，寻求建立CM技术的研究和决策模型的开发，利用通过CM获得的信息的研究之间的联系。