A Bayesian network-based TOPSIS framework to dynamically control the risk of maritime piracy

Fan, Hanwen; Lu, Jing; Chang, Zheng; Ji, Yuan

doi:10.1080/03088839.2023.2193585

Cited by 6 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fan et al 9 introduced a novel approach, combining the BN and the two-stage technique for order of preference by similarity to an ideal solution (TOPSIS) model. The BN, constructed in the first stage, uncovers causal relationships influencing pirate behaviours, while the second stage, employing TOPSIS, calculates a decision matrix for strategy selection.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian network-based tool for crisis classification in piracy or armed robbery incidents on passenger ships

Ventikos,

Koimtzoglou,

Michelis

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

Piracy and armed robbery continue to pose significant security threats to the shipping industry. This paper presents a real-time threat assessment and crisis classification tool for piracy or armed robbery incidents. The tool is part of a crisis classification module that addresses various categories of security threats. This module is currently being developed as part of the EU-funded research project ISOLA, which aims to introduce an intelligent security superintendence ecosystem. The ecosystem is designed to complement the existing ship security processes and measures applied onboard passenger ships. The tool operates by providing real-time threat classification and subsequent warnings by analysing data collected from the ship’s legacy systems and installed sensors with the utilisation of Bayesian probabilistic techniques, particularly Bayesian Networks. The BN model developed for this purpose is thoroughly examined, and its validation is presented through indicative case studies involving piracy and armed robbery. The main objective is to improve situational awareness, enhance vigilance and early threat detection, and support the decision-making process for the Master and crew, especially under time-sensitive circumstances and stressful conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

A Bayesian network-based tool for crisis classification in piracy or armed robbery incidents on passenger ships

Ventikos,

Koimtzoglou,

Michelis

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

show abstract

Multicriteria decision support under uncertainty: combining outranking methods with Bayesian networks

Cebesoy,

Tuncer Şakar,

Yet

2024

Ann Oper Res

View full text Add to dashboard Cite

Assessing alternative solutions that have uncertain evaluations in conflicting multiple criteria is not straightforward. Probabilistic models such as Bayesian networks (BNs) can effectively model and represent the uncertainty in such problems, but they do not include built-in mechanisms to guide different decision makers (DMs) with varying preferences toward the final decision. We propose a systematic approach to combine outranking methods with BNs to provide decision support for solutions with multiple and conflicting criteria under uncertainty. The proposed approach is based on Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and it offers different levels of precision and flexibility to the DMs in assessing the solutions. Our approach includes graphical tools and summary metrics to enhance the presentation of its results to the DMs. We test our approach with a case study on supplier selection where the uncertainty in supplier performances is modeled with a BN. We demonstrate that the proposed approach can enable the joint use of multiple criteria techniques with probabilistic modeling techniques like BNs to provide decision support in complex environments including uncertainty.

show abstract

Contemplating Maritime Autonomous Surface Ships (MASS) under the international law on ship-source pollution

Xing

2024

Marine Pollution Bulletin

View full text Add to dashboard Cite

A Bayesian network-based TOPSIS framework to dynamically control the risk of maritime piracy

Cited by 6 publications

References 28 publications

A Bayesian network-based tool for crisis classification in piracy or armed robbery incidents on passenger ships

A Bayesian network-based tool for crisis classification in piracy or armed robbery incidents on passenger ships

Multicriteria decision support under uncertainty: combining outranking methods with Bayesian networks

Contemplating Maritime Autonomous Surface Ships (MASS) under the international law on ship-source pollution

Contact Info

Product

Resources

About