Automated Container Terminals Research Articles

Simulation and Optimization of Automated Guided Vehicle Charging Strategy for U-Shaped Automated Container Terminal Based on Improved Proximal Policy Optimization

As the decarbonization strategies of automated container terminals (ACTs) continue to advance, electrically powered Battery-Automated Guided Vehicles (B-AGVs) are being widely adopted in ACTs. The U-shaped ACT, as a novel layout, faces higher AGV energy consumption due to its deep yard characteristics. A key issue is how to adopt charging strategies suited to varying conditions to reduce the operational capacity loss caused by charging. This paper proposes a simulation-based optimization method for AGV charging strategies in U-shaped ACTs based on an improved Proximal Policy Optimization (PPO) algorithm. Firstly, Gated Recurrent Unit (GRU) structures are incorporated into the PPO to capture temporal correlations in state information. To effectively limit policy update magnitudes in the PPO, we improve the clipping function. Secondly, a simulation model is established by mimicking the operational process of the U-shaped ACTs. Lastly, iterative training of the proposed method is conducted based on the simulation model. The experimental results indicate that the proposed method converges faster than standard PPO and Deep Q-network (DQN). When comparing the proposed method-based charging threshold with a fixed charging threshold strategy across six different scenarios with varying charging rates, the proposed charging strategy demonstrates better adaptability to terminal condition variations in two-thirds of the scenarios.

Collaborative Optimization of Vessel Stowage Planning and Yard Pickup in Automated Container Terminals

The aim of this research is to minimize the number of container-rehandling operations in both the yard and on the ship in order to solve the problem of coordinated optimization of ship loading and yard container retrieval and enhance the loading efficiency of automated container terminals,. An optimization model that integrates the optimized decision-making of both the container retrieval order from the yard and the ship’s space allocation is developed, and an improved cuckoo algorithm is employed to solve the optimization problem. This paper examines the influence of container retrieval order from the yard on rehandling within the yard, as well as the effect of the ship’s space allocation on subsequent rehandling operations at the unloading port. Experimental results demonstrate that the solution derived from the comprehensive optimization model based on the improved cuckoo algorithm considerably reduces the overall number of overturned containers. This confirms that the proposed optimization model effectively enhances loading efficiency and reduces container rehandling both in the yard and on the ship. The analysis of experimental results indicates that the model and algorithm proposed in this paper have certain applicability.

Integrated Scheduling of Handling Equipment in Automated Container Terminal Considering Quay Crane Faults

Quay cranes (QCs) play a vital role in automated container terminals (ACTs), and once a QC malfunctions, it will seriously affect the operation efficiency of ships being loaded and unloaded by the QC. In this study, we investigate an integrated scheduling problem of quay cranes (QCs), yard cranes (YCs), and automated guided vehicles (AGVs) under QC faults, which is aimed at minimizing the loading and unloading time by determining the range of adjacent operational QCs of the faulty QCs and reallocating unfinished container handling tasks of QCs. A mixed integer programming model is formulated to dispatch QCs, YCs, and AGVs in ACTs. To solve the model, an adaptive two-stage NSGA-II algorithm is proposed. Numerical experiments show that the proposed algorithm can significantly reduce the impact of faulty QCs on productivity while maintaining its synchronous loading and unloading efficiency. The sensitivity analysis of ship scale, location, and number of faulty QCs indicates that the number of faulty QCs has a greater influence on the loading and unloading efficiency than their locations, and the impact of faulty QCs on the efficiency of small-scale ships is greater than that of large-scale ships.

The Influence of Intelligent Guided Vehicle Configuration on Equipment Scheduling in the Railway Yards of Automated Container Terminals

The Influence of Intelligent Guided Vehicle Configuration on Equipment Scheduling in the Railway Yards of Automated Container Terminals

Research on Energy Saving Effect of Parallel and Perpendicular Yard Layouts under Different Proportions of Transshipment at the Automated Container Terminal

The proportions of container transshipment is the key factor in determining the proportion of automated guided vehicle (AGV) and external container truck operations. In terms of parallel and perpendicular layouts of automated container terminals (ACTs), varying proportions of container transshipment result in different proportions of AGVs and external container truck operations, subsequently leading to distinct impacts on energy consumption (EC) for each ACT layout. This paper deemed EC as the primary evaluation criterion, established an EC model encompassing yard cranes (YCs) and container trucks, and investigated the EC of parallel and perpendicular layouts at different proportions of container transshipment. The results indicate that when the proportions of container transshipment were less than 17%, the parallel layout had lower EC; when it was between 17% and 21%, there was not much difference between the two layouts; when it was greater than 21%, the perpendicular layout had lower EC. This conclusion was based on an ideal environment established in this paper. When making decisions, decision makers should use this model as a starting point and adapt it flexibly to the actual situation of the port, in order to arrive at a reasonable and feasible plan.

Mathematical modeling and optimizing of yard layout in automated container terminals

The yard layout significantly influences the performance of automated container terminals (ACTs), especially those employing a perpendicular stack configuration, where the yard blocks are perpendicular to the quay line. Thus, this study is dedicated to optimizing the yard layout tailored for ACTs with such configurations. First, we develop cycle time formulas for the automated stacking crane (ASC) by considering the dependency between its successive handling operations, highlighting the impact of the previous operation on the subsequent one. Further, to address the interferences between the two ASCs within a yard block, a novel hybrid simulation-analytical approach is proposed. Then, utilizing the derived ASC cycle time formulas, an analytic model is constructed based on the G1/G/1 queuing theory to optimize the yard layout design parameters (including the number of yard blocks, bays per block, and rows and tiers per bay) from the perspective of both cost-effectiveness and operational efficiency. In the numerical experiments, optimal yard layouts for two ACTs of different sizes are generated by utilizing the analytic model, and design insights are derived through the sensitivity analysis. The experiments underscore the importance of considering the dependency between successive handling operations and ASC interferences in the design of yard layouts for ACTs.

Research on Train Loading and Unloading Mode and Scheduling Optimization in Automated Container Terminals

In some automated container terminals, railway lines have been implemented into the port, saving container transfer time. However, the equipment scheduling level of the railway yard needs to be improved for managers. In the equipment scheduling of loading and unloading containers for railway trains, the operation modes “full unloading and full loading” and “synchronous loading and unloading” are often adopted. Due to the long length of the railway yard and the line of one train, there are two ways to arrange loading and unloading tasks for automated rail-mounted gantry cranes (ARMGs): one is to pre-assign tasks for ARMGs, and the other is to not pre-assign tasks for ARMGs. To investigate the efficacy of these different operation modes and methods of assigning tasks, this study formulated three mixed-integer linear programming (MILP) models with the goal of minimizing the ARMG task completion time. An adaptive large neighborhood search algorithm was used to tackle the scheduling problem. The scheduling effects of different operation modes and methods for assignment tasks were compared in terms of their calculation time and the completion time of ARMG tasks. Notably, the findings reveal that, with an increase in the number of tasks, the “pre-assign” task arrangement had a limited effect on the completion time of the ARMG tasks, made the calculation time shorter, and reduced the complexity of the problem. From the perspective of the completion time of ARMG tasks, the time under the “synchronous loading and unloading” operation mode was less than that of the “full unloading and full loading” operation mode. Therefore, it is recommended that the managers of the railway yard in an automated container terminal adopt the “synchronous loading and unloading” operation mode but determine the task assignment method according to decision time requirements. In addition, when the number of tasks is large, to decrease the time to complete ARMG tasks, the manager can adopt the “non-pre-assign” task distribution method.

A hybrid SgDT framework for risk analysis of container-handling operations at automated container terminals

With the booming maritime economy and the steady expansion of port production, automated container terminals (ACTs) have rapidly developed worldwide. The safety and stability of container-handling operations at ACTs are crucial for maintaining high efficiency and performance in maritime transportation and global supply chains. However, few studies have focused on the quantitative risk analysis of container-handling operations at ACTs. This study proposes a hybrid SgDT (STPA-grey-DEMATEL-TAISM) approach to analyze risk factors and their coupling interrelationships within ACT handling operations. Initially, 22 factors influencing ACT handling operations were identified based on the system theoretic process analysis (STPA) model and literature. Subsequently, grey theory and decision-making test and evaluation laboratory (DEMATEL) were fused to visualize the cause-effect relationships of these factors. Finally, the total adversarial interpretive structural model (TAISM) was adopted to extract the hierarchical interrelationships among these factors using an adversarial hierarchical structure. The findings showed that poor supervision, communication network failures, sensor malfunctions, equipment malfunctions, lack of safety awareness, and interruptions of the power supply are the most critical factors influencing ACT handling operations. This study provides theoretical insights and practical implications for managers to enhance ACT handling risk management.

Co-optimization of the operation and energy for AGVs considering battery-swapping in automated container terminals

Since the number of containers in automated container terminals (ACTs) has been growing, the operation of ACT relies on a large amount of electric power, which comes from the terminal energy system. As the main horizontal transportation equipment in ACTs, the scheduling of automated guided vehicles (AGVs) has become increasingly important. Even though AGV scheduling has been studied, the battery-swapping procedure is often overlooked, which hinders operation efficiency and the usage of renewable energy. We propose a co-optimization problem of the operation and energy for AGVs considering battery-swapping in this paper. A multi-objective model is constructed to minimize the makespan of AGVs and the operation and maintenance cost of energy system. In addition, we create a discrete multi-objective whale optimization algorithm to address this co-optimization problem. A load-balancing-based method for population initialization is developed to make the distribution of solutions more uniform in this algorithm. With the two-dimensional matrix encoding scheme, an individual right-shifted method is proposed for decoding. The best solution is assessed and selected through a fitness evaluation mechanism based on fuzzy correlation entropy. The bubble-net attack based on opposition-based learning is implemented to enhance the capacity for local intensification. Numerous tests confirm that the proposed method can guarantee the operation efficiency, lower the total cost of the energy system, and promote sustainable development in ACTs.

Recognition and Prediction of Multi-Level Handling Complexity at Automated Terminals Based on ARIMA

Accurate recognition and prediction of the multi-level handling complexity in automated container terminals (referred to as “automated terminals”) is a prerequisite for improving the effectiveness of scheduling and realizing intelligent operation and maintenance. According to the operating characteristics of the automated terminal equipment, the operating network is constructed of automated terminals that describe the characteristics of operating complexity. We use K-medoids and a light gradient boosting machine (LightGBM) to construct a K-LightGBM model that recognizes multi-level handling complexity. The key lies in the reasonable construction of prediction models. A hyper-heuristic autoregressive integrated moving average (ARIMA) model is proposed to address the problem that the ARIMA is ineffective in predicting nonlinear data. We combine ARIMA and the LightGBM model to establish an ARIMA-LightGBM model to predict multi-level handling and residuals. To improve accuracy, we propose the two residual prediction strategies of direct prediction and limited residual boundary prediction based on the residuals generated by ARIMA. We propose a hyper-heuristic algorithm based on a gradient descent-trust region (GD-TR) to compute the weights of predicted values under the two strategies, which improves the global search capability by GD and TR. The particle swarm optimization algorithm, simulated annealing algorithm, and ant colony optimization algorithm are low-level heuristics. Simulation results show that the proposed model possesses the lowest root mean square error on all characteristics compared to ARIMA, E-ARIMA, and ARIMA-LSTM. Therefore, the proposed model is very effective in improving the accuracy of predicting the multi-level handling complexity at automated terminals.

Optimization of Joint Scheduling for Automated Guided Vehicles and Unmanned Container Trucks at Automated Container Terminals Considering Conflicts

Port development is a critical component in constructing a resilient transportation infrastructure. The burgeoning integration of automated guided vehicles (AGVs) within container terminals, in conjunction with the orchestrated scheduling of unmanned container trucks (UCTs), is essential for the sustainable expansion of port operations in the future. This study examined the influence of AGVs in automated container terminals and the synergistic scheduling of UCTs on port operations. Comparative experiments were meticulously designed to evaluate the feasibility of integrated scheduling schemes. Through the development of optimization models that consider conflict-free paths for both AGVs and UCTs, as well as strategies for conflict resolution, a thorough analysis was performed. Advanced genetic algorithms were engineered to address task-dispatching models. In contrast, the A* optimization search algorithm was adapted to devise conflict-free and conflict-resolution paths for the two vehicle types. A range of scaled scenarios was utilized to assess the impact of AGVs and UCTs on the joint-scheduling process across various configuration ratios. The effectiveness of the strategies was appraised by comparing the resultant path outcomes. Additionally, comparative algorithmic experiments were executed to substantiate the adaptability, efficacy, and computational efficiency of the algorithms in relation to the models. The experimental results highlight the viability of tackling the joint-scheduling challenge presented by AGVs and UCTs in automated container terminals. When juxtaposed with alternative scheduling paradigms that operate independently, this integrated approach exhibits superior performance in optimizing the total operational costs. Consequently, it provides significant insights into enhancing port scheduling practices.

A novel multi-attention reinforcement learning for the scheduling of unmanned shipment vessels (USV) in automated container terminals

To improve the operating efficiency of container terminals, we investigate a closed-loop scheduling method in an autonomous inter-terminal system that employs unmanned shipment vessels (USVs) to transport containers among operational berths (Dedicated to USVs) in seaport terminals. Our USVs scheduling model is developed by considering energy replenishment, time windows, and berth restrictions, aiming to obtain cost-saving USV transportation solutions and conflict-free paths. To solve this optimization model more efficiently, we propose the multi-attention reinforcement learning (MARL) algorithm by integrating an encoder-decoder framework and an unsupervised auxiliary network. The MARL algorithm provides instant problem-solving capabilities and benefits from extensive offline training. Experimental results demonstrate that our method can obtain efficient solutions for our USVs scheduling problem, and our algorithm outperforms other compared algorithms on computing time and solution accuracy.

A hierarchical solution framework for dynamic and conflict-free AGV scheduling in an automated container terminal

Container terminals worldwide are experiencing their transitions into automated and intelligent terminals in the face of the ever increasing container handling demand and cost pressure. A key to cost-effective operations in automated container terminals is the efficient AGV scheduling algorithm that enables on-time fulfillment of container loading and discharging tasks. In this paper, we study an integrated task assignment and path planning problem for AGV scheduling in an automated container terminal. We propose a hierarchical solution framework to empower dynamic AGV scheduling, where the higher level employs a reinforcement learning algorithm for dynamic task assignment and the lower level makes use of a tailored path generation algorithm to generate low-cost and conflict-free paths for AGVs to serve the tasks. Additionally, we propose a container matching heuristic and a two-layer grid map to enhance the learning ability of the reinforcement learning algorithm. We compare the performance of the hierarchical solution framework against various benchmark methods on problem instances of practical scales. The results show that our approach is effective in reducing task delays and mitigating path conflicts, making the task assignment and path planning decisions more applicable for AGV scheduling in an automated container terminal.

Improved Benders decomposition for stack-based yard template generation in an automated container terminal

Automated container terminals have become popular solutions to the growing need for container handling. We propose a two-stage mathematical model to design an efficient yard template for automated container terminals, using stacks as the allocation unit. In the first stage, a bi-objective model is developed to optimize landside and seaside operations in the terminal. A Benders decomposition algorithm is designed to solve the model. The proposed algorithm is improved by several acceleration strategies, including valid inequalities, pareto-optimal cuts, and ε−optimal approach. Drawing upon the solution derived from the first stage, the second stage assigns containers to specific bays, which is partitioned into multiple subproblems by capitalizing on the structure of it to enhance solvability. Numerous numerical experiments are conducted to verify the efficiency of the proposed algorithm. In addition, scenario analysis demonstrate that the stack-based yard template is more efficient than a cluster-based yard template for automated container terminals with multiple targets.

A multi-agent reinforcement learning approach for ART adaptive control in automated container terminals

Intelligent transportation equipment represented by Artificial Intelligence Robot of Transportation (ART) has been gradually applied to automated container terminals (ACT). ARTs have higher intelligence and distributed autonomous decision-making capability compared to AGVs. The production and operation of ACT require strict loading plans based on container types and destination ports, which puts strict requirements on the ART loading task sequence. ART travel time from the yard to the quay is highly uncertain due to special scenarios such as traffic flow convergence and turning in narrow areas, which will lead to long waits for ART due to out-of-sequence arrivals at the quay. Therefore, it is necessary to regulate the speed of ART to adjust the timing of ART arriving at the quay. However, existing scheduling systems tend to ignore the impact of ART speed and loading sequence constraints on terminal operations. To optimize the multi-ART control problem of ACT, the waiting time of ART at the quay is taken as the optimization goal, and the speed regulation of ART during each task is defined as a Markov decision process. Then, by incorporating reinforcement learning and a multi-agent approach, an adaptive decision-making method is developed to generate the optimal speed regulation strategy. This method interacts with the multi-agent simulation environment to obtain real-time status information and learns strategies online to improve the intelligence of individual agents and the collaborative capabilities of the agent group. Finally, a multi-agent simulation model embedded with a reinforcement learning agent is established on AnyLogic software. Simulation results show that this method can effectively reduce the waiting time of ART, improve the coherence and efficiency of loading operations, and exhibit good performance in online scenarios that consider emergency tasks. The results indicate that the speed of ART in ACTs with parallel layout of the yard can impact the operational efficiency of the system. Furthermore, by appropriately defining the reward function and training method, collaborative behaviour among equipment can be enhanced, ultimately optimizing the overall performance of the system.

Optimizing multiple equipment scheduling for U‐shaped automated container terminals considering loading and unloading operations

AbstractU‐shaped automated container terminals (ACTs) represent a strategic design in port infrastructure that facilitates simultaneous loading and unloading operations. This paper addresses the challenges of scheduling multiple types of equipment, such as dual trolley quay cranes (DTQCs), automated guided vehicles (AGVs), double cantilever rail cranes (DCRCs), and external trucks (ETs) in U‐shaped ACTs. This paper proposes a mixed integer linear programming model for optimizing the multiple equipment scheduling, aiming to minimize container completion time and AGV waiting time simultaneously. This paper customizes a hybrid genetic‐cuckoo optimization algorithm (HGCOA) with double‐point crossover and Lévy flight Cuckoo search strategies. Extensive numerical results show that the proposed HGCOA outperforms the benchmark genetic algorithms in terms of solution quality and computational time while significantly improving efficiency without substantial sacrifices in solution quality compared with the exact solution method. Overall, this study presents a promising solution for enhancing coordination and operation efficiency in U‐shaped ACTs.

Improvement of Container Dynamic Allocation for Automated Container Terminals in the Post-Epidemic Era

Improvement of Container Dynamic Allocation for Automated Container Terminals in the Post-Epidemic Era

Energy-efficiency and safety-driven multidimensional evolutionary game for AGVs transportation at automated container terminals

The velocity, acceleration, and number (VAN11VAN: The velocity, acceleration, and number.) of AGVs are crucial factors that impact the efficiency, energy consumption, and safety (EES22EES: The efficiency, energy consumption, and safety.3 ACT: Automated container terminal.) of horizontal transport in automated container terminals (ACT3s). This study utilizes multidimensional and evolutionary game theories to address these factors. Specifically, it considers the probability of AGV fault, treats the resulting faults as input costs, and regards the managers' decision preferences as benefits. According to different strategy combinations, a multidimensional evolutionary game based AGVs transportation model is established to analyze their effects on EES. Stability analysis and numerical simulation show that various strategy choices influence the evolutionary trends in EES of horizontal transportation. The findings demonstrate that reasonably setting the VAN of AGVs in the right range for a certain number of tasks can maximize the benefits of EES. The increases in efficiency and energy consumption benefits would accelerate the modification of velocity and acceleration strategies. The rise in safety benefit would also enhance the probability of adjusting AGV number. As the fault probability increases, the benefits for EES decreases. This study provides management insights into scheduling optimization and strategic adjustment for ACTs.

Real-time AGV scheduling optimisation method with deep reinforcement learning for energy-efficiency in the container terminal yard

The increasing vessel size and automation level have shifted the productivity bottleneck of automated container terminals from the terminal side to the yard side. Operating an automated container terminal (ACT) yard with a big number of automated guided vehicles (AGV) is challenging due to the complexity and dynamics of the system, severely affecting the operational efficiency and energy use efficiency. In this paper, a hybrid multi-AGV scheduling algorithm is proposed to minimise the energy consumption and the total makespan of AGVs in an ACT yard. This framework first models the AGV scheduling process as a Markov decision process (MDP). Furthermore, a novel scheduling algorithm called MDAS is proposed based on multi-agent deep deterministic policy gradient (MADDPG) to facilitate online real-time scheduling decision-making. Finally, simulation experiments show that the proposed method can effectively enhance the operational efficiency and energy use performance of AGVs in ACT yards of various scales by comparing with benchmarking methods.

Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework

The rapid development of shipping trade pushes automated container terminals toward the direction of intelligence, safety and efficiency. In particular, the formulation of AGV scheduling tasks and the safety and stability of transportation path is an important part of port operation and management, and it is one of the basic tasks to build an intelligent port. Existing research mainly focuses on collaborative operation between port equipment and path optimization under environmental perception, while there is relatively little research on optimization of path smoothness and safety. Therefore, we propose a path optimization model based on the artificial potential field and twin delayed deep deterministic policy gradient (APF-TD3) framework for the port environment. Firstly, we obtain the scheduling task plan of a single AGV by enumeration. Secondly, according to the artificial potential field (APF) algorithm to generate repulsion for obstacles in the harbor and attraction for container storage at the target point with the position information of the AGV as the input data of the reinforcement learning algorithm is inputted into the twin delayed deep deterministic policy gradient algorithm (TD3). Then TD3 selects the optimal action strategy for the AGV according to the input AGV state information and the designed reward mechanism as well as executes the action. Through repeated execution, the optimal action for the next step is selected at each point to generate a path with start and end points. We validate the model by simulating the scale of containerized cargo in the port i.e. small scale, medium scale and large scale scenes. The experimental results show that the method has the shortest path length of 27.519 m, 270.847 m, and 496.389 m compared to artificial potential field and deep deterministic policy gradient (APF-DDPG), APF, and rapidly-exploring random tree (RRT) algorithms, which also have significant advantages in terms of path security and path smoothness. This framework could respond to the scheduling and transportation tasks of single AGV in different environmental layouts and guarantee the smoothness and safety of the path based on the optimization of the path, which promotes the efficient operation and management of ports.