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Ergun Awarded $161K NSF Grant

August 17, 2015

MIE Associate Professor Ozlem Ergun was awarded a $161K NSF grant to work with Georgia Tech to determine the optimum balance of resource allocation between uncertainty and learning.


Abstract Source: NSF

This collaborative project will research restoration of connectivity of interdependent road and service networks under uncertainty and learning. Real-life applications include post-disaster debris clearance to enable disaster response activities and interdependent infrastructure recovery and repair after a disruption. For example, when the road network is (partially) disrupted, edges (roads) need to be restored (e.g., through repair and debris clearance). The goal is to establish connectivity between supply and demand nodes in a timely manner to satisfy demand. Under uncertainty (about network conditions, and supply-demand levels), decision-making can be improved by collecting situational spatial data. However, data collection consumes time and resources. Furthermore, a common set of resources may perform both restoration and learning activities. Hence, under resource and time constraints, decisions on dynamically prioritizing edge recovery and resource allocation between recovery and learning are crucial for operational efficiency and effectiveness. This project considers the trade-off between learning and restoration activities, and decisions on (equitable and timely) resource allocation and frequency of information updates. The project will leverage the outreach network established by the Center for Health and Humanitarian Systems at Georgia Tech to disseminate results and interact with practitioners during project execution.

Previous research on network connectivity in stochastic networks with learning is limited. If successful, this project will introduce network repair models that are characterized by limited resources, uncertainty, ability to reduce uncertainty by deploying resources, and the need to maintain fairness in service access. It will contribute to a deeper understanding of how to solve dynamic multi-stage decision problems in stochastic networks that offer an opportunity to update information and in which learning and recovery actions share the same resources. This research will lead to efficient solution approaches for finding optimal or near-optimal solutions. The research team will perform structural analysis of underlying problems on simple networks, evaluate different information update mechanisms, and use those to derive insights and generate customized solutions.