A maritime search and rescue location analysis considering multiple criteria, with simulated demand

ABSTRACT

In this paper, a multi-criteria analysis is performed on the location of Maritime Search and Rescue resources. Two well-known standard location models (the maximal covering location problem and p-median problem) are modified and applied in accordance with our problem characteristics. The study considers several distinct response vessel types with different capabilities. Future incidents are simulated based on the underlying distribution of historical incidents. The models are formulated and solved using data from the Atlantic region of Canada. The optimal solutions of these two models, along with the current resource arrangement, are compared in terms of five decision criteria: (1) mean access time, (2) primary coverage, (3) backup coverage, (4) Gini index and (5) maximum access time. The results indicate a significant increase in efficiency of resource utilization and availability of service based on access time and coverage criteria for the solutions provided by the optimization models compared to the current situation.

KEYWORDS:

• Maritime Search and Rescue
• location analysis
• multiple criteria
• coverage
• access time
• kernel estimation

Acknowledgments

We would like to express our thanks to the Natural Sciences and Engineering Research Council of Canada for their support of this study. Also, we extend our appreciation to ACENET, the regional advanced research computing consortium for universities in Atlantic Canada for their financial support and computational resources. A sincere thanks is extended to the Canadian Coast Guard for sharing their data, but most importantly for providing guidance on their operations and priorities. We are also grateful to the anonymous reviewers for their insightful comments and suggestions that helped to improve the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Natural Sciences and Engineering Research Council of Canada [grant number RGPIN 184119-2011]. ACENET, the regional advanced research computing consortium for universities in Atlantic Canada [grant number 13-17-035].