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.