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<p>In the complex network of international trade, ports act as critical nodes facilitating the smooth movement of goods. The efficiency of port operations, particularly the estimation of vessel waiting times, has significant implications for various stakeholders. This study aims to improve vessel waiting time predictions, addressing critical gaps in port planning and investment decisions. This study scrutinizes the efficacy of advanced queuing models, specifically the Pointwise Stationary Approximation (PSA) and <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(M/G/c\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>M</mi> <mo stretchy="false">/</mo> <mi>G</mi> <mo stretchy="false">/</mo> <mi>c</mi> </mrow> </math></EquationSource> </InlineEquation> models, in accurately estimating vessel waiting times in port. The analysis, utilizing empirical data from the Port of Busan, reveals that the advanced models perform better than the conventional models. Our findings show that conventional models often misrepresent waiting times, with significant implications for capacity expansion programs, both in long-haul and intraregional terminals. Our improved approach can prevent overinvestment while ensuring adequate capacity, ultimately strengthening logistics competitiveness.</p>

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Estimating vessel waiting time using queuing models

  • 期刊论文

  • 发表日期：

    2025年4月24日

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  • Ah-Hyun Jo,
  • Young-Tae Chang,
  • Hwaseop Lee,
  • Seong-Hyun Cho

摘要

In the complex network of international trade, ports act as critical nodes facilitating the smooth movement of goods. The efficiency of port operations, particularly the estimation of vessel waiting times, has significant implications for various stakeholders. This study aims to improve vessel waiting time predictions, addressing critical gaps in port planning and investment decisions. This study scrutinizes the efficacy of advanced queuing models, specifically the Pointwise Stationary Approximation (PSA) and \(M/G/c\) M / G / c models, in accurately estimating vessel waiting times in port. The analysis, utilizing empirical data from the Port of Busan, reveals that the advanced models perform better than the conventional models. Our findings show that conventional models often misrepresent waiting times, with significant implications for capacity expansion programs, both in long-haul and intraregional terminals. Our improved approach can prevent overinvestment while ensuring adequate capacity, ultimately strengthening logistics competitiveness.