Chae-Uk Song

Vessel arrival priority determination is a critical factor affecting port safety and efficiency in maritime traffic management, yet existing approaches relying on First Come, First Served (FCFS) principles or empirical judgment have limitations in systematic decision-making. This study aims to develop a systematic decision-making framework that overcomes these limitations by creating an automated, expert knowledge-based priority determination system for vessel traffic services. A dynamic score-based vessel arrival priority determination model was developed integrating the Delphi technique and Fuzzy Analytic Hierarchy Process (Fuzzy AHP). Basic score evaluation factors were derived through Delphi surveys conducted with 50 field experts, and weights were calculated by differentially applying Fuzzy AHP and conventional AHP according to hierarchical complexity. The proposed model consists of a dynamic scoring system integrating basic scores reflecting vessel characteristics and operational conditions, special situation scores considering emergency situations, and risk scores quantifying safety intervals between vessels. To validate the model performance, simulation-based evaluation with eight scenarios was conducted targeting experienced VTS (Vessel Traffic Services) officers, demonstrating strong agreement with expert judgment across diverse operational conditions. The developed algorithm processes real-time maritime traffic data to dynamically calculate priorities, providing port managers and maritime authorities with an automated decision support tool that enhances VTS management and coastal traffic operations.

This study addresses the critical challenge of lacking quantitative measures for objective evaluation of vessel traffic service (VTS) operator workload, where current uniform staffing approaches fail to reflect center-specific operational characteristics. The VTS Operator Workload Index (VOWI) model was developed using the Delphi–AHP methodology to determine the relative importance of key factors including traffic, sea area characteristics, port facilities, and weather conditions, which formed the basis for calculating both center-wide and per-operator workload indices. Factor analysis revealed that traffic factors showed the highest importance at 0.4627, followed by sea area (0.1960), port facilities (0.1916), and weather (0.1497) factors. Application of the VOWI model to 19 VTS centers in South Korea demonstrated that per-operator workload at Busan, Incheon, and Ulsan VTS was up to three times higher than at other centers. This finding indicates that the current uniform staffing approach based on sector count inadequately reflects each center’s actual operational characteristics. The VOWI model provides objective criteria for efficient personnel management in VTS centers and is expected to contribute to improving VTS service quality.

This study aims to identify the key success factors for the adoption of a cloud computing system in vessel traffic service (VTS) systems and evaluate the relative importance of each factor. Through a literature review and expert Delphi surveys, 12 success factors were derived across the dimensions of technology, organization, environment, and institution. The results of the analytic hierarchy process (AHP) analysis revealed that stability in the technological dimension was the most important factor. This study provides useful implications for future decision-making in VTS cloud adoption by systematically identifying the key success factors and presenting their priorities through the application of the TOE-I framework to VTS cloud computing.