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

Jarrod Grewe, Igor Griva

2022

Abstract

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.

Paper Citation

in Harvard Style

Grewe J. and Griva I. (2022). Optimizing Heterogeneous Maritime Search Teams using an Agent-based Model and Nonlinear Optimization Methods. In Proceedings of the 11th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-548-7, pages 200-207. DOI: 10.5220/0010869100003117

in Bibtex Style

@conference{icores22,
author={Jarrod Grewe and Igor Griva},
title={Optimizing Heterogeneous Maritime Search Teams using an Agent-based Model and Nonlinear Optimization Methods},
booktitle={Proceedings of the 11th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2022},
pages={200-207},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010869100003117},
isbn={978-989-758-548-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Optimizing Heterogeneous Maritime Search Teams using an Agent-based Model and Nonlinear Optimization Methods
SN - 978-989-758-548-7
AU - Grewe J.
AU - Griva I.
PY - 2022
SP - 200
EP - 207
DO - 10.5220/0010869100003117