Modeling and Solution of Planar Facility Location Problems with Uncertainty

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This project will contribute to the national economy, security and prosperity by advancing the state-of-the-art in facility location decision-support tools. Analytical tools for facility location problems are used for policy and decision making in a wide range of applications, including locating public facilities such as emergency medical centers and fire stations. The increasing dependability on these tools makes it critical to develop more accurate approaches that eliminate potential errors due to aggregation of data and data uncertainty, which in turn could lead to millions of dollars in wasteful expenditure. The goal of this project is to create pathways toward the design of next-generation facility location analytical tools which are error-free and have the capability to account for uncertainties. The collaboration with Roanoke River Cleaning project will inform and validate the developed methodology, as well as demonstrate a meaningful broader impact in the local community. This project will also contribute toward training of undergraduate and graduate students and engaging underrepresented K-12 students in research via established programs at Virginia Tech.The research objective of this project is to bring a paradigm shift in modeling the classical facility location problems by eliminating critical errors arising from traditional practice of demand aggregation into demand points and binary coverage assumptions. As part of the work, computationally efficient data-driven algorithms will be developed to solve the planar Maximum/Set Covering Location Problems. The developed modeling frameworks allows spatial (non-aggregated) representations of demand and service zones, partial coverage in its true sense, and an adjustable level of risk-aversion to address uncertainty. Exact and approximation algorithms will build on the theoretical properties of the models and result in efficient solution methods. Extensions to explicitly address uncertainty will also be considered. The PI will evaluate the effectiveness of the models and algorithms by executing a pilot project with the Roanoke River Cleaning Project.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.