CAREER: An Adaptive Stochastic Look-ahead Framework for Disaster Relief Logistics under Forecast Uncertainty

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national health, prosperity and welfare by contributing new knowledge on effective disaster relief logistics operations for advance-notice natural disasters such as hurricanes and slow-moving storms. Improved disaster relief efforts can both alleviate human suffering and reduce economic loss.

Current disaster relief logistics planning and operations do not effectively incorporate evolving weather forecasts and natural hazard analysis tools. This project will address this shortcoming by creating adaptive decision-support methods for effectively staging and utilizing scarce resources, leveraging both real-time forecast information and historical data.

This project will foster a long-term collaboration between the operations research community and emergency management agencies by designing novel logistics decision support tools. The accompanying educational program aims to enrich engineering curriculum with data-driven analytic tools, create interdisciplinary research opportunities, and develop outreach activities for K-12 students and the general public to help them understand the role of operations research in addressing critical societal challenges such as disaster relief logistics.

This research will contribute a holistic modeling and algorithmic framework for sequential decision making in disaster relief logistics planning and operations under dynamically evolving disaster situations and their rolling forecasts. This project will: (i) establish new theory to understand the impact of evolving forecast uncertainty on the quality of the decision policy induced by past forecast information; (ii) produce novel algorithms that integrate offline and online stochastic programming models using adaptive sampling, state space approximation, and stage approximation within a rolling-horizon procedure; and (iii) create and analyze novel structured decision policies to address the need to coordinate the timing of various logistics operations with heterogeneous modalities.

The modeling and solution methodology on disaster relief logistics operations planning will be validated using both historical data on past hurricanes and simulation data. Research results will help engage and inform emergency managers in making logistics planning and operational policies that balance between adaptability, optimality and executability in practice.

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.