Container Terminal Yard Research Articles

Real-Time Batch Optimization for the Stochastic Container Relocation Problem

The container relocation problem (CRP) is an important factor affecting the operation efficiency of container terminal yards, and it has attracted much attention for decades. The CRP during the pickup operations of import containers is still an intractable problem for two reasons: the first is that the solution efficiency of the algorithms developed in the existing literature cannot meet the real-time operation requirements; the second is that the pre-optimized operation plan cannot cope with the changes in the real-time operation scenarios caused by the uncertainty of the arrival time of external trucks. This paper proposes an optimization method for the real-time operation scenario which aims to solve the most reasonable operation plan quickly according to the arrivals of external trucks, in which a dynamic upper bound of the optimal solution is derived based on the dynamic programming model of the import containers’ CRP, and an approximate optimal solution can be obtained by minimizing this dynamic upper bound. A heuristic algorithm based on three relocation rules is developed to implement this method, considering the adjustment of the pickup sequence of the target containers. Numerical experiments show that (1) when the number of a batch of target containers is less than 10 (excluding target containers that can be directly picked up), the method proposed in this paper can solve the problem quickly to meet the demand of optimizing real-time pickup operations; (2) compared with other outstanding algorithms, the quality of the solutions obtained by this method is also improved; and (3) this method can be applied to the most container terminals for optimizing real-time pickup operations.

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.

UAV Path Planning for Container Terminal Yard Inspection in a Port Environment

Container terminal yards are automated terminal yards. The rail-mounted gantry crane (RMGC) and rail-mounted gantry crane tracks (RMGCTs) that frequently operate in terminal yards need to be inspected regularly to ensure the safe operation of container transportation in the yard. This paper proposes a framework for the path planning of RMGCT visual inspection using a UAV in container terminal yards in a port environment. The framework consisted of two main aspects. First, a global path to all inspection viewpoints was planned according to the inspection requirements using the A* algorithm and the improved minimum snap method. Second, the intelligent bidirectional rapidly exploring random trees star (IB-RRT*) algorithm was introduced to plan the local path during the flight of the UAV. Finally, the feasibility of the path-planning framework was demonstrated using simulation experiments.

Joint scheduling optimization model for yard cranes and AGVs considering running routes

The escalation in global consumer demand has prompted the continual expansion of container throughput, which drives container terminals to bolster their production capacities. Globally, ports worldwide have been adopting automated equipment and embracing digital operational environments to reinforce their logistics system efficiency and curtail transportation costs. Enhancement of the efficiency of container transportation at automated terminals depends on terminal loading and unloading operations. The efficiency of these operations directly hinges on the automated mechanical equipment deployed at terminals. Consequently, optimization of resource allocation and production scheduling of loading and unloading equipment at terminals has become an essential and effective approach. In this regard, automated yard cranes, which are responsible for rear-end handling, and automated guided vehicles (AGVs), which transport containers between the yard and the terminal quay, have been proven indispensable. This paper principally delves into the scheduling optimization of mechanical equipment in automated container terminal yards, with a unique focus on the coordinated scheduling optimization of yard cranes and AGVs while also accounting for AGV path planning. This research centers on the automated container terminal yard system and the challenge in yard crane scheduling. This work also establishes a grid-based terminal network model and validates the model and algorithm using practical examples. Optimal solutions are obtained for the joint scheduling of yard cranes and AGVs, accompanied by strategies on rational AGV path selection. The findings provide valuable references and theoretical underpinnings for operational scheduling and enhancement of the efficiency in container transport in automated container terminal yards. Furthermore, these results facilitate the overall improvement of production efficiency at automated terminals and the reduction of operational costs, thus carrying theoretical and practical significance.

Inbound container remarshaling problem in an automated container terminal

In the container terminal yard, operators typically rearrange containers during the idle time of the yard cranes (so-called remarshaling operation) to improve the efficiency of future container retrieval. In this study, we address the inbound container remarshaling problem in an automated container terminal, which is aimed at determining the optimal container movement sequence during the remarshaling operation to minimize the expected time for retrieving all containers in the future. The randomness of the inbound containers’ future retrieval order and the maximum available time of the remarshaling operation are both considered. Two new integer linear programming models are proposed to formulate the problem. Numerical experiments show the outperformance of the proposed models over the existing mixed-integer programming model in the literature. Especially, one of the proposed models demonstrates considerably high computational efficiency, which is capable of solving practical-sized instances to optimality in just a few hundred of milliseconds. In addition, we investigate the bi-objective remarshaling problem in order to examine the relationship between the minimum future retrieval time and the maximum available remarshaling time.

A New Synchronous Handling Technology of Double Stack Container Trains in Sea-Rail Intermodal Terminals

With the advantages of large volume, low unit transportation costs, as well as sustainable and stable transport capacity, China in recent years has actively promoted the innovative pilot of double-stack container sea-rail intermodal transport in the Ningbo-Zhoushan port. In this study, a new synchronous handling technology is proposed to improve the handling efficiency of double-stack container trains at sea-rail intermodal terminals. This research primarily focuses on the design of an LDAGV (Automatic Guided Vehicle with Loading and Discharging Function) and a new special articulated flat car for double-stack container trains, while also optimizing the overall layout of the container terminal yard. It then evaluates nine double-stack container stacking forms based on the requirements of transport gauge and center of gravity height. Finally, using data from the Ningbo Beilun No. 3 container terminal, a cost-benefit analysis is performed to compare the traditional handling scheme for common double-stack container trains and the new synchronous handling scheme for double-stack container trains with new special articulated flat car. The results show that the application of new synchronous handling technology has obvious advantages in terms of reducing the handling time and operation cost of double-stack container trains in sea-rail intermodal container terminals, as well as enriching the stacking forms of double-tier containers on the new special articulated flat car, thus reducing the difficulty of collecting cargoes and the organization of container source.

Storage Space Allocation and Twin Automated Stacking Cranes Scheduling in Automated Container Terminals

To solve the problem of storage space allocation for both inbound and outbound containers and twin automated stacking cranes scheduling in the automated container terminals, a new repositioning strategy is designed and a cooperative optimization model is established. The model considers the constraints of safety distance and handshake area capacity. The objective of the model is to minimize the makespan of all storage requests and the total rehandling time during the retrieval process, which is calculated according to the priority of inbound and outbound containers defined by the early retrieval and early loading rule respectively. A variable neighborhood search based hybrid genetic algorithm is designed to solve the model. Numerical experiments show that the sum of the makespan of all storage requests and the total rehandling time during the retrieval process is reduced by approximately 10% compared to the traditional repositioning strategy by optimizing the sequence of container repositioning from the handshake area to the seaside or landside designated storage space. The research is beneficial to improve the operation efficiency of automated container terminal yards.

Many-Objective Container Stowage Optimization Based on Improved NSGA-III

The container ship stowage planning problem (CSPP) is a very complex and challenging issue concerning the interests of shipping companies and ports. This article has developed a many-objective CSPP solution that optimizes ship stability and reduces the number of shifts over the whole route while at the same time considering realistic constraints such as the physical structure of the ship and the layout of the container yard. Use the initial metacentric height (GM) along with the ship’s heeling angle and trim to measure its stability. Meanwhile, use the total amount of relocation in the container terminal yard, the voluntary shift in the container ship’s bay, and the necessary shift of the future unloading port to measure the number of shifts on the whole route. This article proposes a variant of the nondominated sorting genetic algorithm III (NSGA-III) combined with local search components to solve this problem. The algorithm can produce a set of non-dominated solutions, then decision-makers can choose the best practical implementation based on their experience and preferences. After carrying out a large number of experiments on 48 examples, our calculation results show that the algorithm is effective compared with NSGA-II and random weighted genetic algorithms, especially when applied to solve many-objective CSPPs.

Space planning considering congestion in container terminal yards

This paper proposes a nonlinear mathematical model for solving the problem of allocating storage space to future container arrivals in container terminal yards by considering the waiting times of trucks in the yard, called stochastic space allocation model (SSAM). Three heuristic procedures based on the Frank-Wolfe algorithm and the subgradient optimization method are proposed to efficiently solve the problem. From the numerical experiment, it is found that the proposed algorithms for SSAM provide solutions with the gaps between the objective values and the optimal values are no greater than 0.69%. It was also found that the best algorithm among three proposed algorithms (FWMP-S) obtained the average 7.89% improvement in the objective value compared with a previous model with a handling capacity constraint instead of considering waiting time (DSAM). It is found that the SSAM reduces the average system time of transporters for ship operation and that of road trucks by 17.39 and 7.14%, respectively, compared with the DSAM. This study proposes the most appropriate heuristic algorithm depending on the problem characteristics of the SSAM.

Container Terminal Berth and Yard Collaborative Allocation for Import and Export Synchronous Operations by Computational Logistics

Container terminal operators have practical and clear requirements on improving operational efficiency and service level to address running challenges. The resource allocation and collaborative scheduling of quayside berth and storage yard are vital issues in the optimization of container terminal handling systems (CTHSs). The allocation of berth in quayside and blocks on storage yard directly affects the running efficiency of CTHS. Consequently, we focus on the berth and yard collaborative allocation for import and export synchronous operations (BYCA-IESOs) and propose the BYCA-IESO research model based on synchronous or asynchronous integrated scheduling strategy by computational logistics. The BYCA-IESO model is a mixed integer quadratic programming model to minimize multiple time consumption of calling liners and the total horizontal transferring distances of yard trailers at container terminals. Furthermore, we design other two allocation policies for comparison on the BYCA-IESO, which are both multistage scheduling strategy profiles. CPLEX 20.1.0 is applied to implement and solve the model and tactics of BYCA-IESO. Accordingly, the feasibility, validity, and relative merits of the models and strategies are obtained and verified by analyzing and comparing multiple numerical experimental results.

Carbon-efficient scheduling problem of electric rubber-tyred gantry cranes in a container terminal

Green container terminals have gained much attention to satisfy the demand for sustainable and environmentally friendly transport. As one of the major polluters in container terminals, rubber-tyred gantry cranes have already been widely replaced by electric rubber-tyred gantry cranes (ERTGs). This article exploits the features of ERTGs and studies the ERTG scheduling problem in container terminal yards considering the carbon dioxide emissions and task delays simultaneously. The problem is converted into a selective vehicle routing problem with soft time windows (SVRPSTW) and formulated as a mixed integer linear programming model. A column generation algorithm embedded in a branch-and-bound framework is employed to solve the problem. A hybrid acceleration strategy combining the label-setting algorithm with a tabu search algorithm is proposed. Finally, the proposed method is validated via computational experiments. The proposed method provides an efficient tool for carbon-efficient scheduling of ERTGs and thus contributes to the development of green ports.

Deployment and retrofit strategy for rubber-tyred gantry cranes considering carbon emissions

Driven by green port initiatives, container terminal operators have been substituting environmentally and economically inefficient diesel-powered rubber-tyred gantry cranes (RTGs) with new or retrofitted electric ones. Such an investment is closely related to operational activities, and the process of retrofitting should be carried out under the premise of satisfying the terminal yard container handling requirement. An integer programming model is proposed to help decision-makers determine the investment timing and deploy available RTGs to achieve a CO2 emissions reduction target. Considering the distinctive characteristics of different types of RTGs, we develop a deployment plan as well as an RTGs purchase and retrofit strategy. Moreover, the decisions satisfy an operation workload requirement. A tailored Corridor Method (CM) is used to accommodate large scale real-world instances. The application is illustrated with a modestly sized container terminal yard. Results confirm that electrifying diesel-powered RTGs is effective in curbing CO2 emissions and reducing energy costs. Our numerical experiments also highlight the importance of an appropriate emissions reduction target.

The inbound container space allocation in the automated container terminals

In this paper, we study an inbound container space allocation problem in the automated container terminals using simulation-embedded optimization. We aim to allocate the container terminal yard space for a batch of arrived inbound containers so as to minimize the total AGV (Automated Guided Vehicle) waiting time in the space allocation process and the external truck waiting time in the future container retrieval process. We propose an integer programming model to characterize the problem and prove the NP-hardness of the problem. A simulation module is employed to estimate the container rehandle number happened in the retrieval process. A simulation-embedded genetic algorithm is developed to solve the problem. Numerical experiment results show that (1) The proposed algorithm can significantly affect the performance of the allocation decision and achieve a trade-off between fast computation and good solution quality. (2) The space allocation schemes of the inbound containers are affected by different factors, such as initial block layout, quantity of arriving containers, and containers’ arriving information. (3) The automated container terminal operators who care about the external truck waiting time may consider the simulation-embedded approach when the block yard is congested.

Container storage space assignment problem in two terminals with the consideration of yard sharing

Facing the shortage of storage space of container terminal yard, a yard sharing strategy that uses dry port's surplus storage space to ease container congestion is proposed. This novel strategy can address the container storage space assignment problem for inbound containers. The problem is studied based on the storage yard of the combined container terminal and dry port. First, a multiple-objective mixed integer programming model that considers yard sharing strategy with the objectives of minimizing total travel distance, minimizing imbalance in number of containers, maximizing shared storage space of the dry port is formulated to obtain optimal solutions. Second, a non-dominated sorting genetic algorithm II (NSGA-II) is proposed. Next, the performance of the algorithm is verified by a set of instances. Numerical experiments are conducted to elucidate the problem with yard sharing strategy intuitively. Furthermore, the performance of the model in four aspects proclaims the advantages of yard sharing strategy and certifies the comprehensiveness. Finally, sensitivity analysis is conducted by two aspects which are weight coefficient and feasible distance to verify the efficiency of the proposed method.

REVIEW ON CONTAINER DEPOT OPERATIONS

Container depot provides various services to support the containerization industry such as empty container storage, maintenance and repairing (M & R), on-hiring and off-hiring of containers for shipping liners’. Empty container and ex-laden container will be repair and clean based on their condition before storage and releasing to exporter. Empty containers are arranged by container shipping liners by appointing hauliers’ to repositioning these containers from seaport container terminal yard to on dock (within the port area but outside container terminal yard) and container depot.

Storage pricing model of container yards under fluctuating demand

ABSTRACT This paper discusses three types of relationship modes between a container terminal yard and a remote container yard. Considering the variability of demand, a corresponding storage pricing model based on game theory for inbound containers is developed to describe customer behaviour and pricing strategies of yards. The results of theoretical derivation and numerical study indicate that both sides will profit more in the cooperative mode, and the operational efficiency of terminals will be improved. The revenue-sharing contract designed in this paper leads to supply chain coordination. Perfect win-win coordination can be realized when the contract parameters meet specific conditions.

Using path optimum algorithm to solve slot selection problems for outbound containers of different attributes in container terminals

The outbound container slot selection problem (OCSSP) is a hot topic in the field of port research and of great significance to improving the operational efficiency of container yards. With the initial stacking state and extractive sequence of containers being known, the goal is to accomplish the task of extracting all containers in the container block with a minimum number of container relocations. OCSSP is an NP-hard problem. To obtain more practical research results, this paper proposes an improvement to the existing model and presents an improved version of the path optimum algorithm that considers container attributes. With the objective of improving the overall operational efficiency of container terminal yards, this study analyzed some practical issues related to container extraction by taking into consideration the influences of extraction priority levels, movements of obstruct containers, the Manhattan distances of gantry crane, and other factors on the model. Finally, the paper distills some scheduling strategies applicable to OCSSP in the hope of providing a theoretical basis and practical guidance to container port operators for the better allocation and optimization of coastal container ports resources.

Mathematical modeling of yard template regeneration for multiple container terminals

As a tactical-level plan, the yard template determines the space allocation in a container terminal yard for all the arriving shipping liners. Generally speaking, the yard template will not change and it may last a matter of years. However, it has to be changed to response to the alteration of shipping liner. This paper investigates the problem of yard template regeneration for container port, which contains multiple container terminals. Firstly, the concept of yard template regeneration as well as the main influencing factors is proposed. Secondly, a multiple-objective mixed integer programming model is formulated which fully considering the minimum transportation cost, minimum template disturbance and maximum space utilization. Moreover, a case study is conducted to intuitively illustrate the regeneration problem of yard template. Finally, performance analysis and sensitivity analysis are performed to validate the effectiveness of the model.

Carbon-efficient deployment of electric rubber-tyred gantry cranes in container terminals with workload uncertainty

Rubber-tyred gantry cranes are one of the major sources of carbon dioxide emissions in container terminals. In a move to green transportation, the traditional diesel powered cranes are being converted to electric ones. In this paper, we study the deployment of electric powered gantry cranes (ERTGs) in container terminal yards. Cranes always move in-between blocks to serve different workload. ERTGs use electricity for most movements but switch to diesel engines to allow inter-block transfers between unaligned blocks. We exploit this feature and propose to consider simultaneously the CO2 emissions and workload delays to develop carbon-efficient deployment strategies. Moreover, unlike previous works we consider the workload uncertainty, and model the problem as a two-stage stochastic program. A sample average approximation framework with Benders decomposition is employed to solve the problem. Multiple acceleration techniques are proposed, including a tailored regularised decomposition approach and valid inequalities. A case study with sample data from a major port in East China show that our proposal could reduce significantly CO2 emissions with only a marginal compromise in workload delays. Our numerical experiments also highlight the significance of the stochastic model and the efficiency of the Benders algorithms.

Integrated Yard Space Allocation and Yard Crane Deployment Problem in Resource-Limited Container Terminals

Yard storage space and yard crane equipment are the core resources in the container terminal yard area. This paper studies the integrated yard space allocation (outbound container space) and yard crane deployment problem in resource-limited container terminals where yard space and yard cranes are extremely scarce. Two corresponding counterstrategies are introduced, respectively, and the integrated problem is solved as mixed integer programming. The model this paper formulated considers the container volume fluctuation of the service line, and the objective is a trade-off between yard sharing space and terminal operation cost. In numerical experiments, this paper tries to reveal the management meaning in practical operation of container terminal and provides decision support for terminal managers; therefore a series of scenarios are presented to analyze the relations among the yard sharing space, the number of yard cranes, the size of yard subblock, and the cost of terminal operation.