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Department of Computational and Data Sciences, George Mason University, 4400 University Dr Fairfax VA, U.S.A.
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2 Papers

Modeling Missing Maritime Objects Using an Agent Based Model

Topics: Decision Support Systems; Simulation; OR in Emergency Management

Authors: Jarrod Grewe 1 and Igor Griva 2

Affiliations: 1 Department of Computational and Data Sciences, George Mason University, 4400 University Dr Fairfax VA, U.S.A. , 2 Department of Mathematical Sciences, George Mason University, 4400 University Dr Fairfax VA, U.S.A.

Digital Object Identifier: 10.5220/0012317300003639

Proceedings of ICORES 2024, ISBN: 978-989-758-681-1

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Accurate modeling the movement and behaviors of missing persons and vessels is critical in their finding and rescuing in maritime environments. Current methods focus on using particle techniques that model several factors including leeway and drift but lack the ability to model human factors and behaviors. This research explores the idea of using an agent-based approach to model missing objects with the goal of developing a methodology that accounts for missing person behavior in a maritime domain. This new approach leads to a more accurate missing persons movement trajectories and results in finding better search plans. The results show that an agent-based model can consider environmental elements, behavioral factors, and hazards when modeling target movement in a maritime domain which is critical in missing object modeling. The developed approach also shows how an agent-based model can help find optimal search plans.

Optimizing Heterogeneous Maritime Search Teams using an Agent-based Model and Nonlinear Optimization Methods

Topics: Simulation; Optimization

Authors: Jarrod Grewe 1 and Igor Griva 2

Affiliations: 1 Department of Computational and Data Sciences, George Mason University, 4400 University Dr Fairfax VA, U.S.A. , 2 Department of Mathematical Sciences, George Mason University, 4400 University Dr Fairfax VA, U.S.A.

Digital Object Identifier: 10.5220/0010869100003117

Proceedings of ICORES 2022, ISBN: 978-989-758-548-7

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This paper introduces a new search planning methodology, nicknamed Pathfinder, that can optimize heterogeneous teams of mobile and stationary searchers. Unlike previously developed search methods, the new methodology applies an Agent-Based Model (ABM) to simulate target movement and behavior then uses nonlinear optimization methods to find optimal search plans for complex teams of searchers. We describe initial target location with a probability distribution influenced by evidence and environmental data. The ABM models target movement based on environmental and behavioral factors. Then, Pathfinder suggests a search plan that maximizes the probability of target detection and satisfies searcher requirements.

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