Optimizing Boat Resources at the U.S. Coast Guard: Deterministic and Stochastic Models

Abstract

The United States Coast Guard (USCG), a part of the U.S. Department of Homeland Security, is the nation's leading agency in maritime security, safety, and stewardship. One of the primary USCG resources is a fleet of boats (maritime vessels less than 65 feet in length) of various types that must be allocated to USCG stations nationwide. This paper describes the academic-industry collaboration between the authors and the USCG, which resulted in the development of an integer linear programming model that optimally matches supplies of various types of boats to station demands. The paper also introduces a model for the optimal sharing of scarce boat resources. In addition, we generalize our model, using value-atrisk and robust optimization ideas, to manage the risk of boat shortages. The paper reports on the USCG implementation process and discusses internal resistance issues and eventual adoption. We describe USCG modifications to the model recommendations due to practicalities not captured by our model. Finally, we present the significant improvements to USCG quantitative performance metrics that resulted from our model's recommendations. These include a considerable reduction of excess capacity and boat shortages at the stations, a decrease in the overall fleet size with a simultaneous increase in boat utilization, and overall reduction of the fleet operating cost. We also discuss in depth how our model effected these improvements.

Preventing environmental disasters from grounding accidents: A case study of tugboat positioning along the Norwegian coast

“…Their objective function minimizes the shortage or excess capacity at the stations. An improved formulation called boat allocation tool (BAT) is developed by Wagner and Radovilsky (2012), but do not consider actual locations of incidents and the corresponding response time. Chircop et al (2013) address the fleet sizing problem faced by the Royal Australian Navy (RAN) with a column generation algorithm incorporated into a branch-and-price framework.…”

An ILP and simulation model to optimize search and rescue helicopter operations

“…Also, their model does not permit assigning a boat for multiple demand points. Wagner and Radovilsky (2012) improve Radovilsky and Koermer (2007)'s work and develop a decision support tool with multi-objective Mixed Integer Programming (MIP) model, namely the Boat Allocation Tool (BAT) which results in an improvement on USCG fleet performance. They try to minimize the following three objectives: the deviation of supply from demand, the number of boat types at a station and the total cost.…”

“…However, we believe that ILP is an applicable and effective technique for solving maritime SAR resource allocation problems since the expected problem sizes are small enough for that technique. The studies Afshartous et al (2009) and Wagner and Radovilsky (2012) conducted for the USCG, the world's largest SAR operating fleet, also used ILP for optimal allocation planning.…”

Determining the Distribution of Coast Guard Vessels

“…The solution to this problem is based the portsand-waterways safety assessment (PAWSA) model on multiattribute decision-analysis techniques, and on local-expert and stakeholder assessments of safety levels and the effects safety alternatives would have on these levels. Wagner and Radovilsky (2012) propose an integer linear programming model that optimally matches supplies of various types of boats to station demands. The proposed model concentrates on USCG boats and their allocation among the USCG stations by using value-at-risk and robust optimization concepts to manage the risk of boat shortages.…”