English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The United States Coast Guard is charged with coordinating all search and rescue missions in maritime regions within the United States’ purview. Given the size of the Pacific Ocean and limited available resources, the service seeks to posture its fleet of organic assets to reduce the expected response time for such missions. Leveraging 7.5 years of historic event records for the region of interest, we demonstrate a two-stage solution approach. In the first stage, we develop a stochastic zonal distribution model to evaluate spatiotemporal trends for emergency event rates and response strategies. In the second stage, results from the aforementioned analysis enable parameterization of a bi-objective MILP to identify the best locations to station limited heterogeneous search and rescue assets. This research models both 50th and 75th percentile forecast demands across both the set of current homeports, and a larger set of feasible basing locations. Results provide a minimum 9.6% decrease in expected response time over current asset basing. Our analysis also reveals that positioning assets to respond to 75th percentile demands sacrifices, at most, 2.5% in response time during median demand months, whereas positioning for median demand results in operationally inadequate response capability when 75th percentile demands are encountered.

Optimal heterogeneous search and rescue asset location modeling for expected spatiotemporal demands using historic event data