Container Handling Research Articles

Priority rules for handling containers to improve energy consumption and terminal efficiency

AbstractThis paper addresses the optimization of the yard crane handling processes in a container terminal to reduce energy consumption and improve overall system performance. More precisely, the paper presents and evaluates different sequencing rules, based on predefined priorities, to organize the rail yard to minimize moves during the rail loading operations. The minimization of overall energy consumption and maximum tardiness are considered, simultaneously assessing these two components of the objective function to better understand how they interact and how they can be optimized together. As a novel issue in optimization, a hill climbing algorithm is implemented, searching for the yard configuration that most improves the efficiency of container handling while being able to integrate different management rules of the terminal. The reference case study is the PSA Pra terminal in Genoa, Italy. A full rail yard with known delivery times, and crane operating along a single stack, is the operative scenario. Random due time sequences are generated during test instances, while technical data of crane are used. Moreover, crane movements involve both loading and unloading along multiple axes. From the results, the best priority rules improve energy consumption and lateness of the initial configuration of the yard by up to 55%, thus allowing the terminal management to reorganize the storage areas accordingly and improve their efficiency. The proposed priority rules bridge the gap between theoretical optimization procedures and container terminal practices.

Digital twin-driven proactive-reactive scheduling framework for port multi-equipment under a complex uncertain environment

The pervasive uncertainties in multiple port equipment scheduling frequently result in container handling delays or ineffective plans. To address the complexities and uncertainties of port multiple equipment integrated scheduling problem, this paper introduces a Digital Twin-driven (DT-driven) proactive-reactive scheduling framework for the first time. This framework is designed to promptly respond to uncertainties in the scheduling process and provide a transparent visualization of operational information. It specifically tackles the integrated scheduling problem of port quay cranes, Intelligent Guided Vehicles (IGVs), and yard cranes, considering uncertainties such as fluctuations in operating time, equipment failures, and IGV route conflicts. By developing a virtual container port simulation, which features a U-shaped port layout and double-cycling mode drawn from real-world scenarios, the paper evaluates the proposed framework's effectiveness. The experimental results demonstrate that the digital twin framework method significantly improves efficiency and conserves energy. Additionally, in large-scale conditions, the makespan difference between the DT-driven approach and the non-DT-driven approach is as much as 19.56 %. In terms of energy consumption savings, the DT-driven approach's scheduling plan can save 3.67 % of energy consumption under large-scale conditions. Moreover, as the fluctuation index increases, the energy consumption savings become even more significant. This paper also discusses the potential implications of adopting this framework for port companies, highlighting its benefits in enhancing operational and energy efficiency and its incorporation into port management systems. The sensitivity analysis can offer guidance to port companies on optimal equipment allocation strategies.

Simulation of Ship Berthing Operation at Luojing Container Terminal Under Extreme Sea Conditions

The Luojing Port Area of the Port of Shanghai, specifically the coal terminal and ore terminal, used to be the main port area for coal and ore bulk cargo transportation services in the Port of Shanghai.To enhance the container handling capacity at Shanghai port, this study conducted a series of simulation tests at Luojing Container Terminal. The tests were designed according to the terminal's specifications, taking into account the limit berthing wind direction and wind speed (levels 6 and 7). This study selected an appropriate representative ship type for the comprehensive simulation tests, and it thoroughly tested the berthing limits under various extreme conditions using an advanced navigation simulator. The experiment obtained the motion parameters and trajectory of the simulated ship. Based on these results, this study analyzed and evaluated the safety of the rotary waters and berthing operations, ensuring they met the safety assessment requirements for wharf engineering. The study examined the berthing time window, berthing mode, boundary conditions, and safety guarantee measures under extreme sea conditions at Luojing Container Terminal. Finally, By analyzing the berthing simulation trajectory diagrams, tugboat usage, and vessel maneuvering data under the eight extreme berthing conditions, this study formulated a safe berthing plan for ships.

Artificial Intelligence in Indonesian Ports: Opportunities and Challenges

The primary objective of this study is to ascertain the potential advantages and obstacles associated with implementing artificial intelligence (AI) within the context of Indonesian ports. The study utilises qualitative methodologies, namely conducting in-depth interviews with port managers, technicians, and operational staff. Additionally, it involves the analysis of secondary data derived from yearly port reports, case studies, and academic literature. The findings demonstrate that artificial intelligence (AI) can augment operational efficiency, boost ship traffic management, and automate container handling activities within Indonesian ports. Nevertheless, integrating artificial intelligence (AI) within Indonesian ports is currently in its nascent phase, encountering challenges about physical limitations, human capital, and regulatory frameworks. The investigation also examines using interconnected technologies, including the Internet of Things (IoT), blockchain, and augmented reality (AR), to provide additional advantages to the port sector. In summary, via a comprehensive comprehension and effective utilisation of the opportunities afforded by artificial intelligence (AI), Indonesian ports have the potential to position themselves as frontrunners in the international maritime sector.

Integrated optimization of truck appointment quotas and container relocations for multiple blocks considering transshipment containers handling

The container terminal is a crucial transshipment node in the sea and land cargo transport supply chain, and the efficiency of container collection and distribution has always been a research hotspot. In this paper, we consider the impact of transshipment containers resulting from various uncertainties and integrate the operations of external truck pickups with the handling of transshipment containers in the import yard of the container terminal. In light of the varying number of transshipment containers and transit times for different blocks, coupled with the unpredictable arrival order of external trucks leading to the unknown number of relocations, it is proposed to dynamically adjust the upper limit of the appointed quota for each block. We establish a two stage mixed integer programming model for the truck appointment quota and the block relocation problem. The objective functions aim to minimize the number of relocations and minimize the number of yard crane moves under the maximal appointment quota, respectively. This model is utilized for dynamically regulating the upper limit of the appointed quota for each block and determining the time intervals for picking up the appointed and transshipment containers, along with the yard crane scheduling scheme. The problem is solved using CPLEX and the improved variable neighborhood search algorithm (IVNS). The IVNS incorporates the correction strategy, decoding strategy, and scheduling rules designed to align closely with the characteristics of the problem. Through scheme comparison and sensitivity analysis, five management insights are proposed for terminals. Experimental results demonstrate that the approach presented in this paper effectively enhances the utilization efficiency of yard cranes, reduces yard congestion, and yields benefits for both terminals and trucking companies.

Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations

Optimizing the Allocation of Quay Cranes and Prime Movers for Container Handling Operations

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.

Liner Schedule Design under Port Congestion: A Container Handling Efficiency Selection Mechanism

Port congestion significantly impacts the reliability of container ship schedules. However, the existing research often treats vessel time in port as a random variable, failing to systematically consider the complex impact of port congestion on ship schedules. This study addresses the issue of container ship schedule design under port congestion. Vessel waiting times in ports are predicted and quantified by queueing theory, along with information on vessel schedules, cargo handling volumes, and available port operating time windows. We propose a mechanism for selecting container handling efficiencies for arriving vessels, thereby determining their in-port handling times. By jointly considering the uncertainty of vessel waiting and handling times in port, we establish a mixed-integer nonlinear programming model aimed at minimizing the total cost of liner transportation services. We linearize the model and solve it using CPLEX, ultimately devising a robust ship schedule. A simulation analysis is conducted on a real liner shipping route from Asia to the Mediterranean, revealing that extreme weather events, geopolitical conflicts, and other factors can lead to severe congestion at certain ports, necessitating timely adjustments to vessel schedules by shipping companies. Moreover, such events can impact the marine fuel market, prompting shipping companies to adopt strategies such as increasing vessel numbers and reducing vessel speeds in response to high fuel prices. Additionally, the container handling efficiency selection mechanism based on information sharing enables shipping companies to flexibly design liner schedules, balancing the economic costs and service reliability of container liner transportation.

Data-driven study of container handling charges in coastline ports of China

With the rapid development of networking and digitization, the data generated by various industries has shown exponential growth. The seaport industry is the fundamental industry to support the operation of the national economy, and undertakes the vast majority of China's foreign trade, materials, and coal transport along the sea. In response to some of China's major seaports of container handling charges for different extent of the upward event. In this paper, by researching the main seaports of China's coastal ports, we have obtained the level of container handling charge, and operating cost of container section. Meanwhile, this paper combines the reform history of China's port container charging policy and the comparison of domestic and foreign charging levels to discover the cost structure of the container handling charges. It establishes the benchmark for the optimization of the container handling charges. Utilizing data analysis related to production and operations, this paper proposes an optimization method of container handling charges based on the cost analysis of container port companies with macro and micro coupling and puts forward effective suggestions on this basis. This paper suggests the establishment of a mechanism for regulating container handling charges. It also proposes a mechanism for monitoring and regular announcement of container costs.

Dynamic berth allocation under uncertainties based on deep reinforcement learning towards resilient ports

With the evolving global trade landscape and the post-pandemic effects, the resilience of ports has become paramount. The unforeseen disturbances bring substantial challenges, especially in berth allocation, a vital task ensuring seamless resilient port operations. The unpredictability of vessel arrivals and the variability in loading/unloading times intensify these issues, pushing traditional static allocation methods beyond their limits. Fortunately, the advent of smart ports has led in an era of big data availability, enabling the application of advanced deep reinforcement learning (DRL) techniques. To capitalize on this shift, this research presents a DRL-based methodology specially designed to solve the berth allocation problem with the uncertainties in vessel arrival and container handling time to enhance port resilience. A Markov Decision Process model (MDP) of the berth allocation problem is established to minimize the mean waiting time with tailored state space, rule-based action space, and reward function to address the issue. An offline training method is designed to train the agent in selecting the optimal action based on the current state of the port berth system at each decision point even in uncertain environments, deep Q-network (DQN) is implemented for this problem. Comprehensive experiments across different problem scales are conducted to validate the effectiveness and generality of the proposed method in solving berth allocation challenges under uncertain conditions. Furthermore, the trained model also performs better than other methods in different vessel congestion levels through learning.

Evaluation of the factors causing container lost at sea through fuzzy-based Bayesian network

To date, many high-profile accidents involving the loss of large numbers of containers overboard have occurred during ocean voyages. It is estimated that an average of 1629 containers are lost at sea each year, and the statistics also highlight that the number of lost shipping containers has increased by two-thirds in the last five years. It is important to consider that falling containers are not only a threat to the safety of shipping but also a potential health and environmental hazard. In this direction, there are ongoing initiatives to improve container handling and reduce the risk of container loss. This paper focuses on container loss at sea, which is a serious risk in terms of marine pollution and financial loss, and seeks to evaluate this problem by identifying its causes. A Fuzzy Bayesian Network (FBN) model is therefore created to provide an approach for determining the factors and weights to be considered by seafarers during container transportation when assessing the causes of a container falling overboard. As a result of the research process, a model for assessing the risk of container falls is developed and the probabilistic relationships between the causes of container falls overboard are revealed. According to the analysis of the model, improper stuffing (15%), misdeclaration of container weight (12.6%), and container structural resistance (11.1%), are the three most risky root causes of container falls. In addition, the most obvious finding to emerge from this study is that the causes of container losses overboard are strictly related to both the process of the lashing and securing of the cargo and to the stability of the ship. Taken together, the results indicate that to prevent container falls at sea, it is crucial to avoid poor stability and ensure proper cargo securing. Consequently, FBN model developed for the study is expected to help improve the safety of container transport and reduce the risk of container loss by providing a comprehensive probabilistic risk analysis of container operations and predicting the risk of container loss if undesirable factors arise.

Integrated Scheduling of Yard and Rail Container Handling Equipment and Internal Trucks in a Multimodal Port

Integrated Scheduling of Yard and Rail Container Handling Equipment and Internal Trucks in a Multimodal Port

A 3D World Interpreter System for Safe Autonomous Crane Operation

In an effort to improve short-sea shipping in Europe, we present a 3D world interpreter (3DWI) system as part of a robotic container-handling system. The 3DWI is an advanced sensor suite combined with AI-based software and the communication infrastructure to connect to both the crane control and the shore control center. On input of LiDAR data and stereo captures, the 3DWI builds a world model of the operating environment and detects containers. The 3DWI and crane control are the core of an autonomously operating crane that monitors the environment and may trigger an emergency stop while alerting the remote operator of the danger. During container handling, the 3DWI scans for human activity and continuously updates a 3D-Twin model for the operator, enabling situational awareness. The presented methodology includes the sensor suite design, creation of the world model and the 3D-Twin, innovations in AI-detection software, and interaction with the crane and operator. Supporting experiments quantify the performance of the 3DWI, its AI detectors, and safety measures; the detectors reach the top of VisDrone’s leaderboard and the pilot tests show the safe autonomous operation of the crane.

A MODAL SHIFT, BUT HOW?

In this paper, we investigate the conditions that must be met for modal shift (i.e., the transfer of freight transport from road to rail). In addition to describing the competitiveness conditions, the paper makes technical and organizational proposals for rail freight traffic management. One of the most important conditions is that rail-road intermodal freight transport must be competitive in time and price with unimodal road freight transport. The freight transport model presented in this article provides a solution to this problem and an approach to estimating the additional intermodal freight traffic. Another important criterion is the relatively dense network of road-rail links, known as intermodal transshipment points (ITPs) along main railway lines. The proposed model can be compared to the freight transport model described in the term Physical Internet, with the addition that the objective is to minimize road haulage when locating ITPs (or hubs). ITPs are rail-road terminals or, more precisely, transshipment points, which differ substantially from the commonly used continental terminals. The third condition to be met is a horizontal container handling procedure that can be applied efficiently (i.e., at low cost) under the railway catenary and is capable of handling intermodal units used in continental traffic and maritime freight. Finally, an example for the Visegrád countries is presented. The essence of the example is the potential additional freight traffic or modal shift that could be included in the proposed ITP network. We believe that a modal shift could occur for up to 50% of the indicated heavy goods vehicle (HGV) traffic if the offer is competitive.

Reception and management of agrochemical containers in the CATs

The handling of agrochemical containers is subject to compliance with current laws for the operation and certification of systems that reduce contamination risks. The objective of this research is to design a computer system that allows greater control in the Temporary Collection Centers (CAT), from the reception of containers to the exit of waste to destination companies. For the development of the project, an exhaustive analysis was carried out in each of the processes. Requirements gathering techniques and the design of diagrams will be applied to detail the context of the processes using the Unified Modeling Language (UML). The database was designed in MySQL and the programming was carried out in Visual Studio 2023. Results were obtained in various reports and statistics that allow managing the capacity of the CATs and having a reliable record of the origin of the containers, as well as a control and follow-up in the exit and delivery of waste to final destination companies. The project is a challenge of environmental impact and social responsibility that requires the intervention of different sectors, public and private, as well as the direct collaboration of agricultural producers.

Operational Performance Model of Container Cranes in Stevedooring Process at The New Container Terminal Makassar 2

Purpose: This research scrutinizes the effects of increasing container flow on the operational performance of port services at TPK New Makassar Terminal 2, focusing on the influence of loading and unloading equipment on the utilization and productivity of Rubber Tyred Gantry (RTG) Cranes. Methodology: Structural Equation Modeling (SEM) using AMOS software and statistical analysis with SPSS are employed to determine the relationships between container handling equipment and operational performance indicators. The study integrates multiple linear regression, F test, and T test to examine the causality between equipment usage and effective time in the loading and unloading processes. Results: The study reveals a significant correlation between the number of loading and unloading actions and the usage of RTG cranes. It was found that the number of head trucks and reach stackers also impacts RTG productivity, but to a lesser extent. The regression model developed indicates a clear influence of loading and unloading frequency on the effective time of operations. Conclusion: Conclusions drawn from the analysis highlight the critical role of loading and unloading equipment in optimizing port operational performance. The results suggest that an increase in the number of such equipment correlates positively with the efficiency of RTG Cranes and effective time management at the terminal. Originality/Value: By integrating various methodological approaches, this study offers comprehensive insights into port operational dynamics, contributing to the strategic planning for equipment allocation and operational scheduling to enhance productivity at TPK New Makassar Terminal 2.

A Multi-Stage Approach for External Trucks and Yard Cranes Scheduling with CO2 Emissions Considerations in Container Terminals

Background: In container terminals, optimizing the scheduling of external trucks and yard cranes is crucial as it directly impacts the truck turnaround time, which is one of the most critical performance measures. Furthermore, proper scheduling of external trucks contributes to reducing CO2 emissions. Methods: This paper proposes a new approach based on a mixed integer programming model to schedule external trucks and yard cranes with the objective of minimizing CO2 emissions and reducing truck turnaround time, the gap between trucking companies’ preferred arrival time and appointed time, and the energy consumption of yard cranes. The proposed approach combines data analysis and operations research techniques. Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. Additionally, a two-stage mathematical model is applied. The first stage employs a bi-objective mathematical model to plan the arrival of external trucks at the terminal gates. The second stage involves a mathematical model that schedules yard cranes’ movements between different yard blocks. Results: The results show that implementing this methodology in a hypothetical case study may lead to a substantial daily reduction of approximately 31% in CO2 emissions. Additionally, the results provide valuable insights into the trade-off between satisfying the trucking companies’ preferred arrival time and the total turnaround time. Conclusions: The integration of data clustering with mathematical modeling demonstrates a notable reduction in emissions, underscoring the viability of this strategy in promoting sustainability in port-related activities.

Yard Space Allocation Algorithm for Unloading Containers at Marine Terminals

The issue of unloading efficiency for containers is the operational bottleneck for most traditional container terminals. In addressing the intricate challenges of space allocation in container yards during ship unloading, this study focuses on the real-time, dynamic decision-making needs that are currently unmet by existing planning methods. To tackle this, the article introduces a novel model for container space allocation that aims to maximize the “attractiveness” of yard spaces. This model factors in key considerations like the allocation of container handling equipment resources, the rate of container handling equipment traversing the yard, and container handling equipment operations across containers. A unique Monte Carlo tree search (MCTS)-based algorithm is developed to solve this multi-objective problem. The algorithm’s efficacy is rigorously tested via numerical experiments, where it outperforms existing approaches like UCT-MCTS, AMAF-MCTS, and manual scheduling plans using practical engineering examples. This research not only provides a more dynamic and efficient method for yard space allocation but also offers empirical evidence to support its practicality and effectiveness.

Enhanced Ant Colony Algorithm for Discrete Dynamic Berth Allocation in a Case Container Terminal

Berth allocation is a critical concern in container terminal port logistics, involving the precise determination of where and when arriving vessels should dock along a quay. With berth space limitations and a continuous surge in container handling demands, ensuring an effective berth allocation is paramount for the smooth and efficient operation of container ports. However, due to the randomness of vessel arrival times and uncertainties surrounding container ship loading capacities, berth allocation problems (BAP) often present discrete and dynamic challenges. This paper addresses these challenges by considering real-world terminal operational factors, formulating relevant assumptions, and establishing a model for dynamic berth allocation and efficient ship berthing scheduling. The primary motivation stems from the parallels observed between the BAP problem and ant foraging path selection, leading to the proposal of a novel Parallel Search Structure Enhanced Ant Colony Algorithm (PACO). A proper set of parameters of the algorithm are selected based upon sensitivity analyses on the convergence and parallelism efficiency of the algorithm. To validate our method, a real-world case-container terminal operation in Shanghai Port was studied. The experimental comparison results show that the PACO algorithm outperforms other commonly used algorithms, making it more effective and efficient for the Discrete Dynamic Berth Allocation Problem (DDBAP).