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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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... NetLogo, verification is performed by selecting the "Check" option. Based on Figure 6, it is confirmed that the syntax created could be executed, thereby verifying the model developed using NetLogo. The simulation model is validated by first testing the simulation model with the parameters used in the reference model and comparing the results obtained. ...
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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.
