CRII: CPS: A Decentralized and Differentially Private Framework for Sensing, Operations and Respond Logistics in Large-Scale Vehicle Fleets

现代车队配备了越来越多的传感器仪表，可以生成大量关于资产状况和操作感知的数据。近年来，关于利用这些数据为操作提供预测性见解的方法的文献越来越多。单独来看，这些方法对舰队级决策的改进有限。大规模车队中存在着重要的动态相互依赖关系，包括(i)在退化和故障风险方面持续的资产对资产的相互作用，（ii）与运营协调和共享资源使用（例如任务需求和备件资源）相关的车辆之间的相互作用，以及（iii）备件物流、维护和运营之间的相互作用。通过对数据驻留、隐私和计算可伸缩性的严格要求引入了额外的挑战层。该NSF项目为车队管理提供了一个统一的预测-规范框架，该框架集成了(i)传感器驱动的对动态发展的资产故障概率和操作风险的预测，以及（ii）车队级操作、维护和响应物流的自适应鲁棒优化模型。该项目的智力优势包括在分散和差分私有混合整数优化模型中制定传感器驱动的风险；并并行开发了量身定制的解决方法。该项目的更广泛影响包括通过出版物、课程、会议和讲习班传播研究成果。该项目将支持暑期实习和本科生研究机会，特别是来自代表性不足社区的学生，以培养下一代车队管理工程师。在车队管理应用程序中利用传感器数据的真正价值，需要对车队级别的交互进行集成和详细的建模，以及传感器驱动的传感和决策能力的无缝集成。为了应对这一挑战，本提案旨在为传感器驱动的车队管理开发一个分散的、差异化的私有框架。特别是，拟议的项目(i)在联合决策优化模型中集成传感器驱动的资产剩余寿命分布，以确定最佳的运营、维护和备件物流计划；（ii）在自适应稳健的车队管理模型重构中动态建模感知资产剩余寿命、故障风险和其他运营不确定性。（三）在分权和差别化私人协调机制下重新构建决策模型。重要的计算挑战将通过分散的解决算法来解决，这些算法利用了所提出的决策优化模型的结构。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Modern vehicle fleets are equipped with increasing levels of sensor instrumentation that generate large quantities of data on asset conditions and operational awareness. In recent years, there has been a growing literature on methods that harness this data to provide predictive insights for operations. Taken individually, these methods provide limited improvements to fleet-level decision making. There are significant and dynamic interdependencies in large-scale vehicle fleets that include (i) continuous asset-to-asset interactions in degradation and failure risks, (ii) interactions across vehicles related to operational coordination and use of shared resources (e.g. mission requirements and spare part resources), and (iii) interactions between spare part logistics, maintenance and operations. Additional layers of challenges are introduced through stringent requirements for data residency, privacy, and computational scalability. This NSF project provides a unified predictive-prescriptive framework for vehicle fleet management that integrates (i) sensor-driven predictions on dynamically evolving asset failure probabilities and operational risks, with (ii) adaptive robust optimization models for fleet-level operations, maintenance and respond logistics. Intellectual merits of the project include formulation of sensor-driven risks within decentralized and differentially private mixed integer optimization models; and a parallel development of tailored solution methods. Broader impacts of the project include dissemination of research findings through publications, coursework, conferences and workshops. The project will support summer internships and undergraduate research opportunities, specifically for students from underrepresented communities, to educate the next-generation of engineers for vehicle fleet management.Harnessing the true value of sensor data in a fleet management application, requires an integrated and detailed modeling of fleet level interactions, along with a seamless integration of sensor-driven sensing, and decision-making capabilities. To address this challenge, this proposal aims to develop a decentralized and differentially private framework for sensor-driven fleet management. In particular, the proposed project (i) integrates sensor-driven asset remaining life distributions within a joint decision optimization model to identify optimal operations, maintenance and spare part logistics schedule, (ii) dynamically models the perceived asset remaining life, failure risks and other operational uncertainties within an adaptive robust reformulation of the fleet management model, and (iii) reformulates the decision model within a decentralized and differentially private coordination mechanism. Significant computational challenges will be addressed through decentralized solution algorithms that leverage on the structure of the proposed decision optimization models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.