Journals / Journal of Eta Maritime Science / 2021 / Cilt: 9 - Sayı: 1

Analysis of Draught Survey Errors by Extended Fuzzy Analytic Hierarchy Process

Pages
51–63
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study will contribute to the literature by shedding light on draught survey errors in which studies in the academic literature are very limited.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Bulk carriers and general cargo vessels have the largest number and tonnage among different types of ship fleets.
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Key Findings & Data:According to the United Nations Conference on Trade and Development data in 2019, the tonnage of the global bulk carrier and general cargo vessel is 842,438,000 and 74,000,000 metric tons, respectively.
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Conclusion & Impact:This study will contribute to the literature by shedding light on draught survey errors in which studies in the academic literature are very limited.
🔬Design:Survey & Empirical Study
📍Scope:2019 Verileri
⚙️Variables:Domain Parameters

Özet

Bulk carriers and general cargo vessels have the largest number and tonnage among different types of ship fleets. According to the United Nations Conference on Trade and Development data in 2019, the tonnage of the global bulk carrier and general cargo vessel is 842,438,000 and 74,000,000 metric tons, respectively. The main cargo weight measurement method for bulk carriers and general cargo vessels is the draught survey calculation. In this study, a fuzzy analytic hierarchy process questionnaire was prepared based on previous studies and experts’ opinions. Responses from the experts were consolidated to determine the priority vector of the criteria of draught survey error sources. Expert evaluations showed that the major reasons of draught survey inconsistencies are problems occurring at draught reading and ballast measurement stages. Accordingly, the most effective alternatives to minimize the errors were found to be training and documentation, which are closely related to education. This study aims to determine the draught survey error causes and their priorities along with different means to reduce the errors from the experts’ opinions. This study will contribute to the literature by shedding light on draught survey errors in which studies in the academic literature are very limited.

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Cite This Article

CANIMOĞLU, R., YILDIRIM, U., İNEGÖL, G. M. (2021). Analysis of Draught Survey Errors by Extended Fuzzy Analytic Hierarchy Process. Journal of Eta Maritime Science, Cilt: 9 - Sayı: 1, 51–63. https://ajindex.com/articles/212670

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References (4)

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