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arXiv · 1908.08970

Optimal Heterogeneous Asset Location Modeling for Expected Spatiotemporal Search and Rescue Demands using Historic Event Data

Zachary T. Hornberger·Bruce A. Cox·Brian J. Lunday
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Abstract

The United States Coast Guard is charged with the coordination of all search and rescue missions in maritime regions within the United States purview. Given the size of the Pacific Ocean and the limited resources available to respond to search and rescue missions in this region, the service seeks to posture its aligned fleet of maritime and aeronautical assets to reduce the expected response time for such missions. Leveraging historic event records for the region of interest, we propose and demonstrate a two-stage solution approach. In the first stage, we develop and apply a stochastic zonal distribution model to evaluate spatiotemporal trends for emergency event rates and corresponding response strategies to inform the probabilistic modeling of future rescue events respective locations, frequencies, and demands for support. In the second stage, the results from the aforementioned analysis enable the parameterization and solution of a integer linear programming formulation to identify the best locations at which to station limited heterogeneous search and rescue assets. Considering both the 50th and 75th percentile levels of forecast event and asset demand distributions using 7.5 years of historical event data, our models identify asset location strategies that respectively yield a 9.6 percent and 17.6 percent increase in coverage over current asset basing when allowing locations among current homeports and airports, as well as respective 67.3 percent and 57.4 percent increases in coverage when considering a larger set of feasible basing locations. Keywords: search and rescue, spatiotemporal forecasting, location-allocation modeling, p-median location problem, multi-objective optimization

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Zachary T. Hornberger, Bruce A. Cox, Brian J. Lunday. 2019-08-23. Optimal Heterogeneous Asset Location Modeling for Expected Spatiotemporal Search and Rescue Demands using Historic Event Data. https://arxiv.org/abs/1908.08970

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