Agent-based model of maritime search operations : a validation using test-driven simulation modeling

Onggo, Bhakti Stephan and Karatas, Mumtaz (2016) Agent-based model of maritime search operations : a validation using test-driven simulation modeling. In: Proceedings of the 2015 Winter Simulation Conference :. IEEE, USA, pp. 254-265. ISBN 9781467397438

[thumbnail of WSC15_ABS_Patrol_final_v5]
Preview
PDF (WSC15_ABS_Patrol_final_v5)
WSC15_ABS_Patrol_final_v5.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (357kB)

Abstract

Maritime search operations (and search operations in general) are one of the classic applications of Operational Research (OR). This paper presents a generic agent-based model for maritime search operations which can be used to analyse operations such as search and rescue and patrol. Agent-based simulation (ABS) is a relatively new addition to existing OR techniques. The key elements of an ABS model are agents, their behaviours and their interactions with other agents and the environment. A search operation involves at least two types of agent: a searcher and a target. The unique characteristic of ABS is that we model agents’ behaviours and their interactions at the individual level. Hence, ABS offers an alternative modelling approach to analyse search operations. The second objective of our work is to show how test-driven simulation modelling (TDSM) can be used to validate the agent-based maritime search-operation model.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ID Code:
78291
Deposited By:
Deposited On:
26 Feb 2016 13:32
Refereed?:
Yes
Published?:
Published
Last Modified:
14 Oct 2024 00:38