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Background: Container terminal congestion is often measured by the average turnaround time for external trucks. Reducing the average turnaround time can be resolved by controlling the yard crane operation and the arrival times of external trucks (truck appointment system). Because the truck appointment system and yard crane scheduling problem are c...
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... shown in Table 1, previous research has commonly utilized mathematical modeling, operational research, or discrete-event simulation approaches. However, these methods often neglect the communication and operational sequence aspects of the appointment reservation process in their formulation and evaluation of improvement proposals or models. ...Context 2
... yields outcomes that are not significantly different from those of the centralized system in terms of the performance indicator, i.e., external truck turnaround time. The results of various configurations of truck appointment approaches and yard scheduling strategies in terms of truck turnaround time are described in Figure 7. Based on yard crane utilization, NTFS consistently shows lower utilization rates compared with FCFS in both the centralized and decentralized truck appointment system approaches, as presented in Tables 9 and 10. This indicates that yard crane operation time under NTFS is lower than under FCFS, aligning with the truck turnaround time results. ...Context 3
... the significant difference in inconvenience costs, NTFS is more effective than FCFS in minimizing inconvenience for truck drivers, leading to higher overall satisfaction and smoother operations, especially under higher traffic conditions, as shown in Table 11 and Figure 9. Therefore, if the goal is to reduce inconvenience for truck drivers and ensure more consistent operations, NTFS would be the superior strategy. Figure 9. Results of inconvenience cost for decentralized truck appointment system (DTAS). ...Context 4
... Carbon emission factor: 2.68 kg CO 2 per liter of diesel . The results from Tables 12 and 13 demonstrate a clear comparison of CO 2 emissions across the different yard crane scheduling strategies in both centralized (CTAS) and decentralized (DTAS) truck appointment systems. In Table 12, the NTFS strategy shows the lowest average CO 2 emissions at 2193.68 kg compared with FCFS (8264.61 kg) and NLFS (8376.10 kg). This suggests ...Context 5
... results from Tables 12 and 13 demonstrate a clear comparison of CO 2 emissions across the different yard crane scheduling strategies in both centralized (CTAS) and decentralized (DTAS) truck appointment systems. In Table 12, the NTFS strategy shows the lowest average CO 2 emissions at 2193.68 kg compared with FCFS (8264.61 kg) and NLFS (8376.10 ...Context 6
... suggests that NTFS is more effective in reducing carbon emissions in centralized systems. Similarly, in Table 13, the NTFS strategy once again results in the lowest average emissions at 2178.28 kg, while FCFS shows 7910.83 kg and NLFS produces 7959.01 kg. ...Context 7
... the NTFS strategy demonstrates superior consistency, with a smaller range between minimum and maximum turnaround times, suggesting greater reliability over FCFS. In terms of CO 2 emissions, NTFS consistently achieves the lowest average emissions across both CTAS and DTAS, as seen in Tables 12 and 13. DTAS generally shows slightly higher efficiency in emission reduction compared with CTAS, especially at moderate to high truck arrival rates. ...Similar publications
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Citations
... Optimization models are used to minimize waiting times, emissions, or operational costs, and to balance yard workloads and optimize crane scheduling [9,18,19]. Simulation-based models are used to evaluate system performance under real-world variability, and to test the robustness of TAS designs [3,[20][21][22][23][24]. Hybrid models combining queuing, simulation, and optimization techniques between them and with data analytics techniques, have been proposed to handle complex constraints, uncertainty, and multi-objective scenarios [25][26][27][28][29]. Collaborative systems are modeled as decentralized and agent-based frameworks accommodating multiple actors [30]. Emerging trends include AI-driven and IoT-enabled solutions for real-time and predictive scheduling. ...
... Environmental sustainability targets reducing truck emissions, energy consumption, and empty trips, emphasizing eco-friendly practices, reducing the environmental impact of port operations, and supporting compliance with emission regulations and sustainability targets [42,43]. Lastly, cost minimization seeks to lower transportation costs and eliminate unnecessary travel, aligning financial efficiency with environmental benefits, achieving cost efficiency while maintaining service quality, and addressing economic impacts on both terminals and external stakeholders [7,30,40]. Multi-objective functions often integrate environmental, economic, and operational goals [36,44]. These classifications align with terminal goals to improve efficiency, sustainability, and cost effectiveness. ...
... The third generation of TAS models addresses uncertainty in operational conditions, such as dynamic truck arrivals, stochastic travel times, and real-time disruptions. These models are characterized by the introduction of dynamic and stochastic approaches [68], the use of agent-based simulations to explore decentralized and centralized scheduling [30], and the incorporation of real-time decision-making capabilities [51]. This generation accounted for uncertainties in truck arrivals, travel times, and service times, making the models more realistic. ...
This paper provides a comprehensive review of truck appointment scheduling models and algorithms that support truck appointment systems (TASs) at container terminals. TASs have become essential tools for minimizing congestion, reducing wait times, and improving operational efficiency at the port and maritime industry. This review systematically categorizes and evaluates existing models and optimization algorithms, highlighting their strengths, limitations, and applicability in various operational contexts. We explore deterministic, stochastic, and hybrid models, as well as exact, heuristic, and metaheuristic algorithms. By synthesizing the latest advancements and identifying research gaps, this paper offers valuable insights for academics and practitioners aiming to enhance TAS efficiency and effectiveness. Future research directions and potential improvements in model formulation are also discussed.
