Optimization of daily operations in the marine industry using ant colony optimization (ACO)-An artificial intelligence (AI) approach

• Autorzy: Sardar A. , Anantharaman M. , Garaniya V. , Khan F.

Identyfikatory

DOI

10.12716/1001.17.02.04

• Języki publikacji: EN

Abstrakty

EN

The maritime industry plays a crucial role in the global economy, with roughly 90% of world trade being conducted through the use of merchant ships and more than a million seafarers. Despite recent efforts to improve reliability and ship structure, the heavy dependence on human performance has led to a high number of casualties in the industry. Decision errors are the primary cause of maritime accidents, with factors such as lack of situational awareness and attention deficit contributing to these errors. To address this issue, the study proposes an Ant Colony Optimization (ACO) based algorithm to design and validate a verified set of instructions for performing each daily operational task in a standardised manner. This AI-based approach can optimise the path for complex tasks, provide clear and sequential instructions, improve efficiency, and reduce the likelihood of human error by minimising personal preference and false assumptions. The proposed solution can be transformed into a globally accessible, standardised instructions manual, which can significantly contribute to minimising human error during daily operational tasks on ships.

Słowa kluczowe

EN

ant colony optimization; artificial intelligence; maritime transport; International Maritime Organization; international safety management; formal safety assessment; algorithms

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2023
• Tom: Vol. 17 No. 2
• Strony: 290--295
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Sardar A.

• University of Tasmania, Launceston, Australia

autor

Anantharaman M.

• University of Tasmania, Launceston, Australia

autor

Garaniya V.

• University of Tasmania, Launceston, Australia

autor

Khan F.

• Memorial University of Newfoundland, NL, St. John’s, Canada

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).