Coast Guard Faces Big Challenge in Allocating and Sharing More than 1,500 Boats across 400 Stations
The homeland security research group CCICADA has delivered to the US Coast Guard a sophisticated computer program to help the agency further refine its efforts to make the best use of its limited supply of boats and cutters.
CCICADA delivered the programming code and related documentation for Boat Allocation Module (BAM) II to the USCG at its Washington headquarters on June 11, 2015.
CCICADA Director Roberts, as well as the primary BAM II developers, Postdoctoral Associates Brian Nakamura and Christie Nelson, attended the presentation.
The efficient deployment of resources and mission assets is a critical challenge facing all sectors and operational units of the homeland security enterprise.
BAM II addresses the Coast Guard’s challenge of how to optimize the allocation and sharing of more than 1,500 boats and cutters across 400 stations, each responsible for up to 12 different mission goals. The USCG has twelve different types of vessels, each with a large set of capabilities.
Through many years of practitioner expertise from first-hand knowledge and experience, the USCG had developed a very good and serviceable boat allocation scheme.
Several years ago, the USCG, a US Department of Homeland Security (DHS) agency, approached the Command, Control and Interoperability for Advanced Data Analysis (CCICADA), to further refine how it assigns boats to stations.
CCICADA is a DHS University Center of Excellence. It uses complex data analysis, mathematical modeling and computer systems to help homeland security agencies like the Coast Guard do a better job of protecting the American homeland from natural, terrorist and other threats.
CCICADA’s Boat Allocation Module project is helping the Coast Guard achieve two primary goals:
a) Minimize Unmet Mission Hours: Given a fixed budget, minimize the number of its boats’ unused mission hours (if budget is small). Otherwise, efficiently meet the required mission hours and station requirements while staying within a given budget, and
b) Minimize Budget Expenses: Satisfy all of the station and mission requirements, while minimizing the total budget amount.
On January 16, 2013, CCICADA formally presented the project outputs and products (programming code, final report, user’s guide and all related documentation) to the USCG command for the initial implementation of its Boat Allocation Module (BAM). That implementation is expected to save the agency up to $120 million in boat operation expenses over a 20-year period.
The next natural question became: “Can we do better?” The initial Boat Allocation Module seeks to optimize the assignment of whole boats to Coast Guard stations. However, a boat might sit idle for a period of time at its assigned station, leaving valuable mission hours unused.
The second phase of BAM—or BAM II— addresses fractional boat allocation, allowing more than one station to share a particular boat. If two USCG stations can share one boat’s hours (i.e., the number of available hours that a boat may operate over a given period), then that boat could very well cover required missions at both stations. Under the original BAM, two boats would have to be assigned, one to each station.
Although there are practical, implementation challenges to sharing boat assets, BAM II is designed to show what sharing scenarios are possible, opening the real possibility of additional savings for the Coast Guard, especially as old boats are retired and new ones put into service.
In the development of BAM and BAM II, a true CCICADA-USCG partnership made it possible to find data-driven solutions to aid the USCG in its decision making. The USCG dedicated technical personnel to work closely with CCICADA researchers in the development of the modules. CCICADA researchers spent time at USCG sites to learn firsthand about USCG operations and procedures. Several joint project meetings were held as the modules progressed. This coordination and cooperation continues on other projects as well, including Arctic resource planning and an emerging data accuracy and cleanup project.