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Port terminals consist of two interfaces for transferring cargo among transport modes: (1) the seaside or quayside interface and (2) the landside interface. At the seaside interface, cargo is loaded and unloaded from the vessels and stored temporarily at the yard. Landside operations consist of receiving and dispatching cargo from external trucks and rail. The increasing volumes of international trade are demanding more efficient cargo handling throughout the port logistic chain and coordination with the hinterland, hence attracting more attention from both practitioners and researchers on the landside interface of ports. Due to the high variability of truck arrivals with a significant concentration at peak hours, congestion at the access gates of ports and an unbalanced utilization of the resources occur. Truck appointment systems (TAS) have already been implemented in some ports as a coordination mechanism to reduce congestion at ports, balance demand and capacity, and reduce truck turnaround times. Based on the current situation faced by the Port of Arica, Chile, this paper aims to analyze potential configurations of a TAS and evaluate its impacts on yard operations, specifically in the reduction of container rehandles, as well as truck turnaround times. For this, a discrete-event simulation model and a heuristic procedure are proposed and experimentation is performed using historical data from the port terminal. Results indicate that implementing a TAS may significantly benefit yard operations in terms of reducing container rehandles as well as truck waiting times.

The irregular arrival patterns of container trucks at ports have a substantial impact on logistics operations’ efficiency, resulting in congestion during peak hours and unused port capacity during idle times. Implementing a truck appointment system (TAS) is vital to address this issue effectively. This paper suggests enhancing the TAS by adopting a data‐driven approach using terminal gate data to understand the intricate and uncertain relationship between truck arrival patterns and port operational efficiency. Insights gained from these data are utilized to develop a distributionally robust optimization (DRO) model. This model provides an exact solution for optimizing the appointment quota plan of TASs, thereby improving port efficiency and addressing operational challenges. Compared to existing methods, this approach does not heavily rely on theoretical assumptions concerning the cooperation mechanisms among trucks, yard equipment, quayside equipment, and other facilities and fully considers the complex uncertainties in truck arrivals. Furthermore, to examine the effectiveness of the proposed model, a case study is conducted at Yan Port, China, aiming to achieve practical results. The numerical experiments comparing its performance with the conventional robust optimization (RO) model confirm the superiority of the proposed DRO model in minimizing the total truck turnaround time within the terminal and overall time expenses. This superiority stems from its integration of the respective advantages of stochastic optimization (SO) and traditional RO methods. By optimizing the appointment quota plan in this manner, it achieves a balanced distribution of truck arrivals, showcasing its significant potential to enhance port logistics efficiency.

Truck appointment has proved to be an efficient tool in reducing congestion at container terminals. To make a reasonable appointment quota plan, it is necessary to take terminal operations into consideration. We develop a novel approach (model) for optimizing a truck appointment system with the objective of decreasing external trucks’ waiting times, at the gate and yard, and internal trucks’ waiting times at the yard. The vacation queuing model is used to describe the coordinated service process of yard cranes. Based on non-stationary queuing theory, truck waiting times are estimated more accurately. Numerical experiments are conducted to illustrate the validity of the model and algorithm. Results show that the model reflects the characteristics of the service process of yard cranes and it improves the calculation accuracy of the truck waiting time.

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 closely interconnected, this research investigates synchronization between the approaches used in truck appointment systems and yard crane scheduling strategies. Rubber-tired gantry (RTG) operators for yard crane scheduling operations strive to reduce RTG movement time as part of the container retrieval service. However, there is a conflict between individual agent goals. While seeking to minimize truck turnaround time, RTGs may travel long distances, ultimately slowing down the RTG service. Methods: We address a method that balances individual agent goals while also considering the collective objective, thereby minimizing turnaround time. An agent-based simulation is proposed to simulate scenarios for yard crane scheduling strategies and truck appointment system approaches, which are centralized and decentralized. This study explores the combined effects of different yard scheduling strategies and truck appointment procedures on performance indicators. Various configurations of the truck appointment system and yard scheduling strategies are modeled to investigate how those factors affect the average turnaround time, yard crane utilization, and CO2 emissions. Results: At all levels of truck arrival rates, the nearest-truck-first-served (NTFS) scenario tends to provide lower external truck turnaround times than the first-come-first-served (FCFS) and nearest-truck longest-waiting-time first-served (NLFS) scenario. Conclusions: The decentralized truck appointment system (DTAS) generally shows slightly higher efficiency in emission reduction compared with centralized truck appointment system (CTAS), especially at moderate to high truck arrival rates. The decentralized approach of the truck appointment system should be accompanied by the yard scheduling strategy to obtain better performance indicators.

Due to increased container traffic, the problems of congestion at terminal gates generate serious air pollution and decrease terminal efficiency. To address this issue, many terminals are implementing a truck appointment system (TAS) based on several concepts. Our work addresses gate congestion at a container terminal. A conceptual model was developed to identify system components and interactions, analyzing container flow from both static and dynamic perspectives. A truck appointment system (TAS) was modeled to optimize waiting times using a non-stationary approach. Compared to existing methods, our TAS introduces a more adaptive scheduling mechanism that dynamically adjusts to fluctuating truck arrivals, reducing peak congestion and improving resource utilization. Unlike traditional static appointment systems, our approach helps reduce truckers’ dissatisfaction caused by the deviation between the preferred time and the assigned one, leading to smoother operations. Various genetic algorithms were tested, with a hybrid genetic–tabu search approach yielding better results by improving solution stability and reducing computational time. The model was applied and adapted to the Port of Casablanca using real-world data. The results clearly highlight a significant potential to enhance sustainability, with an annual reduction of 785 tons of CO2 emissions from a total of 1281 tons. Regarding trucker dissatisfaction, measured by the percentage of trucks rescheduled from their preferred times, only 7.8% of arrivals were affected. This improvement, coupled with a 62% decrease in the maximum queue length, further promotes efficient and sustainable operations.

Gate and yard congestion is a typical type of container port congestion, which prevents trucks from traveling freely and has become the bottleneck that constrains the port productivity. In addition, urban traffic increases the uncertainty of the truck arrival time and additional congestion costs. More and more container terminals are adopting a truck appointment system (TAS), which tries to manage the truck arrivals evenly all day long. Extending the existing research, this work considers morning and evening peak congestion and proposes a novel approach for multi-constraint TAS intended to serve both truck companies and container terminals. A Mixed Integer Nonlinear Programming (MINLP) based multi-constraint TAS model is formulated, which explicitly considers the appointment change cost, queuing cost, and morning and evening peak congestion cost. The aim of the proposed multi-constraint TAS model is to minimize the overall operation cost. The Lingo commercial software is used to solve the exact solutions for small and medium scale problems, and a hybrid genetic algorithm and simulated annealing (HGA-SA) is proposed to obtain the solutions for large-scale problems. Experimental results indicate that the proposed TAS can not only better serve truck companies and container terminals but also more effectively reduce their overall operation cost compared with the traditional TASs.

PurposeTruck appointment systems have been applied in critical container ports in the United States due to their potential to improve handling operations. This paper aims to develop a truck appointment system to optimise the total cost experiencing at the entrance of container terminals by managing truck arrivals and the number of service gates satisfying a given level of service.Design/methodology/approachThe approximation of Mt/G/nt queuing model is applied and integrated into a cost optimisation model to identify (1) the number of arrival trucks allowed at each time slot and (2) the number of service gates operating at each time slot that ensure the average waiting time is less than a designated time threshold. The optimisation model is solved by the Genetic Algorithm and tested with a case study. Its effectiveness is identified by comparing the model's outcomes with observed data and other recent studies.FindingsThe results indicate that the developed truck appointment system can provide more than threefold and twofold reductions of the total cost experiencing at the terminal entrance compared to the actual data and results from previous research, respectively.Originality/valueThe proposed approach provides applicably coordinated truck plans and operating service gates efficiently to decrease congestion, emission and expenses.

The implementation of the truck appointment system (TAS) in various ports shows that it can effectively reduce congestion and enhance resource utilization. However, uncertain factors such as traffic and weather conditions usually prevent the external trucks from arriving at the port on time according to the appointed period for container pickup and delivery operations. Comprehensively considering the significant factors associated with truck appointment no-shows, this paper proposes a deep learning model that integrates the long short-term memory (LSTM) network with the transformer architecture based on the cascade structure, namely the LSTM-Transformer model, for actual truck arrival predictions at the container terminal using TAS. The LSTM-Transformer model combines the advantages of LSTM in processing time dependencies and the high efficiency of the transformer in parsing complex data contexts, innovatively addressing the limitations of traditional models when faced with complex data. The experiments executed on two datasets from a container terminal in Tianjin Port, China, demonstrate superior performance for the LSTM-Transformer model over various popular machine learning models such as random forest, XGBoost, LSTM, transformer, and GRU-Transformer. The root mean square error (RMSE) values for the LSTM-Transformer model on two datasets are 0.0352 and 0.0379, and the average improvements are 23.40% and 18.43%, respectively. The results of sensitivity analysis show that possessing advanced knowledge of truck appointments, weather, traffic, and truck no-shows will improve the accuracy of model predictions. Accurate forecasting of actual truck arrivals with the LSTM-Transformer model can significantly enhance the efficiency of container terminal operational planning.

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.

The uncertainty of the arrival time of trucks has increased the complexity of terminal operations. The truck appointment system (TAS) cannot respond to this problem in time, which can easily cause appointment invalidation and reduce the efficiency of truck operations and terminal operations. This paper comprehensively considers the related constraints of truck re-scheduling costs, gate waiting costs, and idle emission costs. With the goal of minimizing the comprehensive operating costs of truck companies and port companies, a dynamic appointment rescheduling model for external trucks based on mixed integer nonlinear programming is established. This paper designs an adaptive quantum revolving door update mechanism and proposes a double-chain real quantum genetic algorithm. The simulation experiment results show that compared with the traditional scheduling, the truck dynamic appointment rescheduling model can effectively reduce the comprehensive operating costs of the truck company and the port company and alleviate the congestion of the port. The probability that the truck cannot arrive at the port on time, the advance time for the truck to confirm the arrival time, and the length of time that the external truck cannot arrive at the port on time have a significant impact on the cost of the reschedule of the TAS. This paper favorably supports the manager’s operational decision-making.