Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic
Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic
With the maturing of autonomous technology and better accessibility, there has been a growing interest in the use of autonomous underwater vehicles (AUVs). The deployment of AUVs for under-ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extreme operating environment. To control the risk of loss, existing risk analyses approaches tend to focus more on the AUV's technical aspects and neglect the role of soft factors, such as organizational and human influences. In addition, the dynamic and complex interrelationships of risk variables are also often overlooked due to uncertainties and challenges in quantification. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. In the FuSDRA framework, system dynamics models the interrelationships between risk variables from different dimensions and considers the time-dependent nature of risk while fuzzy logic accounts for uncertainties. To demonstrate its application, an example based on an actual Antarctic AUV program is presented. Focusing on funding and experience of the AUV team, simulation of the FuSDRA risk model shows a declining risk of loss from 0.293 in the early years of the Antarctic AUV program, reaching a minimum of 0.206 before increasing again in later years. Risk control policy recommendations were then derived from the analysis. The example demonstrated how FuSDRA can be applied to inform funding and risk management strategies, or broader application both within the AUV domain and on other complex technological systems.
Fuzzy Set Theory, Risk Analysis, System Dynamics, autonomous underwater vehicle
818-841
Loh, Tzu Yang
f21489db-1aa9-4cdf-bcb0-ad784a98b2fb
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Bose, Neil
37b8d6e4-fd93-4bbe-827c-f060e0ce0851
Xu, Jingjing
879c88fa-1ef9-4288-8bb8-27479b6274ec
Tenekedjiev, Kiril
82dd0d80-2ee5-4db2-b04a-654cc703bd47
1 April 2020
Loh, Tzu Yang
f21489db-1aa9-4cdf-bcb0-ad784a98b2fb
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Bose, Neil
37b8d6e4-fd93-4bbe-827c-f060e0ce0851
Xu, Jingjing
879c88fa-1ef9-4288-8bb8-27479b6274ec
Tenekedjiev, Kiril
82dd0d80-2ee5-4db2-b04a-654cc703bd47
Loh, Tzu Yang, Brito, Mario, Bose, Neil, Xu, Jingjing and Tenekedjiev, Kiril
(2020)
Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic.
Risk Analysis, 40 (4), .
(doi:10.1111/risa.13429).
Abstract
With the maturing of autonomous technology and better accessibility, there has been a growing interest in the use of autonomous underwater vehicles (AUVs). The deployment of AUVs for under-ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extreme operating environment. To control the risk of loss, existing risk analyses approaches tend to focus more on the AUV's technical aspects and neglect the role of soft factors, such as organizational and human influences. In addition, the dynamic and complex interrelationships of risk variables are also often overlooked due to uncertainties and challenges in quantification. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. In the FuSDRA framework, system dynamics models the interrelationships between risk variables from different dimensions and considers the time-dependent nature of risk while fuzzy logic accounts for uncertainties. To demonstrate its application, an example based on an actual Antarctic AUV program is presented. Focusing on funding and experience of the AUV team, simulation of the FuSDRA risk model shows a declining risk of loss from 0.293 in the early years of the Antarctic AUV program, reaching a minimum of 0.206 before increasing again in later years. Risk control policy recommendations were then derived from the analysis. The example demonstrated how FuSDRA can be applied to inform funding and risk management strategies, or broader application both within the AUV domain and on other complex technological systems.
Text
JRA-Revised 28 Jul accepted
- Accepted Manuscript
More information
Accepted/In Press date: 12 November 2019
e-pub ahead of print date: 4 December 2019
Published date: 1 April 2020
Additional Information:
Funding Information:
This research was supported by the Australian Research Council's Special Research Initiative under the Antarctic Gateway Partnership (Project ID SR140300001). The first author also acknowledges the Australian Government Research Training Program Scholarship in support of this higher degree research.
Publisher Copyright:
© 2019 Society for Risk Analysis
Keywords:
Fuzzy Set Theory, Risk Analysis, System Dynamics, autonomous underwater vehicle
Identifiers
Local EPrints ID: 435660
URI: http://eprints.soton.ac.uk/id/eprint/435660
ISSN: 0272-4332
PURE UUID: 853427e9-ba3f-413b-8541-4bf32da56360
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Date deposited: 18 Nov 2019 17:30
Last modified: 02 Aug 2022 04:01
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Contributors
Author:
Tzu Yang Loh
Author:
Neil Bose
Author:
Jingjing Xu
Author:
Kiril Tenekedjiev
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