Prescriptive analytics models for vessel inspection planning in maritime transportation

Port state control (PSC) inspections are crucial for maritime safety and pollution reduction. The inspection process involves identifying high-risk vessels, allocating surveyors, and conducting onboard checks. This study aims to optimize the selection and assignment process through a two-stage frame...

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Main Authors: Yang, Ying, Yan, Ran., Wang, Shuaian
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/175834
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1758342024-05-08T00:42:30Z Prescriptive analytics models for vessel inspection planning in maritime transportation Yang, Ying Yan, Ran. Wang, Shuaian School of Civil and Environmental Engineering Engineering Maritime transportation Vessel inspection Port state control (PSC) inspections are crucial for maritime safety and pollution reduction. The inspection process involves identifying high-risk vessels, allocating surveyors, and conducting onboard checks. This study aims to optimize the selection and assignment process through a two-stage framework, balancing the benefits of identifying deficiencies against the costs of inspection delays. Initially, we employ a predict-then-optimize approach, predicting the number of vessel deficiencies using a k-nearest neighbor (kNN) model, which informs the inspection decisions. However, due to the nonlinear nature of the optimization in relation to predicted values, we also explore an estimate-then-optimize framework that estimates distributions of potential deficiencies. We enhance two prescriptive analytics models and introduce an advanced global model with a pre-processing algorithm for better distribution estimation. A case study using data from the Hong Kong port demonstrates that the estimate-then-optimize models surpass the predict-then-optimize approach, offering solutions closer to the optimal policy. Furthermore, our improved model outperforms existing methods, proving more effective in practical applications. The Research Grants Council of the Hong Kong Special Administrative Region, China [Project numbers 15201121, HKSAR RGC TRS T32-707/22-N]. 2024-05-08T00:42:30Z 2024-05-08T00:42:30Z 2024 Journal Article Yang, Y., Yan, R. & Wang, S. (2024). Prescriptive analytics models for vessel inspection planning in maritime transportation. Computers and Industrial Engineering, 190, 110012-. https://dx.doi.org/10.1016/j.cie.2024.110012 0360-8352 https://hdl.handle.net/10356/175834 10.1016/j.cie.2024.110012 2-s2.0-85187198768 190 110012 en Computers and Industrial Engineering © 2024 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Maritime transportation
Vessel inspection
spellingShingle Engineering
Maritime transportation
Vessel inspection
Yang, Ying
Yan, Ran.
Wang, Shuaian
Prescriptive analytics models for vessel inspection planning in maritime transportation
description Port state control (PSC) inspections are crucial for maritime safety and pollution reduction. The inspection process involves identifying high-risk vessels, allocating surveyors, and conducting onboard checks. This study aims to optimize the selection and assignment process through a two-stage framework, balancing the benefits of identifying deficiencies against the costs of inspection delays. Initially, we employ a predict-then-optimize approach, predicting the number of vessel deficiencies using a k-nearest neighbor (kNN) model, which informs the inspection decisions. However, due to the nonlinear nature of the optimization in relation to predicted values, we also explore an estimate-then-optimize framework that estimates distributions of potential deficiencies. We enhance two prescriptive analytics models and introduce an advanced global model with a pre-processing algorithm for better distribution estimation. A case study using data from the Hong Kong port demonstrates that the estimate-then-optimize models surpass the predict-then-optimize approach, offering solutions closer to the optimal policy. Furthermore, our improved model outperforms existing methods, proving more effective in practical applications.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Yang, Ying
Yan, Ran.
Wang, Shuaian
format Article
author Yang, Ying
Yan, Ran.
Wang, Shuaian
author_sort Yang, Ying
title Prescriptive analytics models for vessel inspection planning in maritime transportation
title_short Prescriptive analytics models for vessel inspection planning in maritime transportation
title_full Prescriptive analytics models for vessel inspection planning in maritime transportation
title_fullStr Prescriptive analytics models for vessel inspection planning in maritime transportation
title_full_unstemmed Prescriptive analytics models for vessel inspection planning in maritime transportation
title_sort prescriptive analytics models for vessel inspection planning in maritime transportation
publishDate 2024
url https://hdl.handle.net/10356/175834
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