A multi-objective genetic algorithm for yard crane scheduling problem with multiple work lines

Due to increasing ships and quay cranes, container terminals operations become more and more busy. The traditional handling based on work line is converted into pool strategy, namely loading and unloading containers with multiple work lines are operating simultaneously. In the paper we discuss the yard crane scheduling problem with multiple work lines in container terminals. We develop a multi-objective 0-1 integer programming model considering the minimum total completion time of all yard cranes and the maximization balanced distribution of the completion time at the same time. With the application of adaptive weight GA approach, the problem can be solved by a multi-objective hybrid genetic algorithm and the Pareto solutions can be finally got. Using the compromised approach, the nearest feasible solution to ideal solution is chosen to be the best compromised Pareto optimal solution of the multi-objective model. The numerical example proves the applicability and effectiveness of the proposed method to the multi-objective yard crane scheduling problem.     

Choice of loading clusters in container terminals

The storage allocation in the export yard of a container terminal determines the efficiency of container loading. Even if the yard manager has optimised the allocation of export containers to avoid rehandling, conflicts among loading quay cranes can still occur. Thus, all the possible handlings during loading must be considered when organising the yard space. In previous studies, the yard storage allocation has been assessed based on the subblock, which consists of several adjacent bays. However, to minimise all possible handlings in the loading process at the terminal, optimising more flexible storage clusters is also important. Thus, our aim in this research is to model the choice of loading clusters and derive a more flexible allocation strategy for organising the space in the export yard. A bi-objective model is built, which considers both the transportation distance and handling balance between blocks. A model aimed at minimising all possible handlings in the export yard for the loading process is also developed, and several of the insights derived can inform yard management in real-life operations. It is proven that the handling requirements have a significant effect on the choice of loading clusters, and yard managers should consider the various features of liner and loading processes when organising their storage space.     

Developing a dynamic rolling-horizon decision strategy for yard crane scheduling

As a hub for land and marine transportation, container terminals play an important role in global trade. In today’s competitive environment, container terminals should improve their service quality, i.e., effective space resource handling and equipment resource scheduling, for their prosperity or even survival. Although intensive researches were attempted on yard crane scheduling, the solutions from these approaches likely reached a local optimum, and thereafter a rational strategy towards global optimum was still lacking. Accordingly, it became an imperative to explore a rational strategy for this purpose. To resolve this problem, a novel dynamic rolling-horizon decision strategy was proposed for yard crane scheduling in this study. Initially, an integer programming model was established to minimize the total task delaying at blocks. Due to the computational scale with regard to the yard crane scheduling problem, a heuristic algorithm, along with a simulation model, was then applied. In this fashion, the simulation model was next investigated to alternate the periods and evaluate the task delaying. Subsequently, a genetic algorithm was employed to optimize the initial solutions generated. Consequently, computational experiments were used to illustrate the proposed strategy for yard crane scheduling and verify the effectiveness and efficiency of the proposed approach.     

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.     

Quay crane and yard truck dual-cycle scheduling with mixed storage strategy

In order to enhance the efficiency of port operations, the scheduling problem of the quay cranes and yard trucks is crucial. Conventional port operation mode lacks optimization research on efficiency of port handling operation, yard truck scheduling, and container storage location. To make quay crane operations and horizontal transportation more efficient, this study uses a dual-cycle strategy to focus on a quay crane and yard truck scheduling problem in conjunction with a mixed storage strategy. A dispatching plan for yard trucks is considered, as well as the storage location of inbound containers. Based on the above factors, a mixed-integer programming model is formulated to minimize vessels’ berth time for completing all tasks. The proposed model is solved using a particle swarm optimization-based algorithm. Validation of the proposed model and algorithm is conducted through numerical experiments. Additionally, some managerial implications which may be potentially useful for port operators are obtained.     

Genetic mechanism-based coupling algorithm for solving coordinated scheduling problems of yard systems in container terminals

This study develops models and methods utilized for solving the coordination scheduling problem in the yard of a container terminal. Based on the information shared by the yard storage subsystem and the YC scheduling subsystem, and the interaction between these subsystems, a coordination scheduling model, which is composed of a storage subsystem model, a YC scheduling subsystem and a coordinate controller model, is developed. A coupling algorithm, which is based on a genetic mechanism, is developed to solve the coordination scheduling problem. The algorithm adopts the genetic selection, crossover and mutation operations to adjust the yard storage plan and the YC scheduling plan. The performance of the coordination scheduling model and that of the proposed coupling algorithm are confirmed with reference to a numerical example.     

An optimization model for the container pre-marshalling problem

In most container yards around the world, containers are stacked high to utilize yard space more efficiently. In these yards, one major factor that affects their operational efficiency is the need to re-shuffle containers when accessing a container that is buried beneath other containers. One way to achieve higher loading efficiency is to pre-marshal the containers in such a way that it fits the loading sequence. In this research, we present a mathematical model for the container pre-marshalling problem. With respect to a given yard layout and a given sequence that containers are loaded onto a ship, the model yields a plan to re-position the export containers within the yard, so that no extra re-handles will be needed during the loading operation. The optimization goal is to minimize the number of container movements during pre-marshalling. The resulting model is an integer programming model composed of a multi-commodity flow problem and a set of side constraints. Several possible variations of the model as well as a solution heuristic are also discussed. Computation results are provided.     

Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption

Green transportation has recently been the focus of the transportation industry to sustain the development of global economy. Container terminals are key nodes in the global transportation network and energy-saving is a main goal for them. Yard crane (YC), as one type of handling equipment, plays an important role in the service efficiency and energy-saving of container terminals. However, traditional methods of YC scheduling solely aim to improve the efficiency of container terminals and do not refer to energy-saving. Therefore, it is imperative to seek an appropriate approach for YC scheduling that considers the trade-off between efficiency and energy consumption. In this paper, the YC scheduling problem is firstly converted into a vehicle routing problem with soft time windows (VRPSTW). This problem is formulated as a mixed integer programming (MIP) model, whose two objectives minimize the total completion delay of all task groups and the total energy consumption of all YCs. Subsequently, an integrated simulation optimization method is developed for solving the problem, where the simulation is designed for evaluating solutions and the optimization algorithm is designed for exploring the solution space. The optimization algorithm integrates the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, where the GA is used for global search and the PSO is used for local search. Finally, computational experiments are conducted to validate the performance of the proposed method.     

Storage yard management based on flexible yard template in container terminal

With the bottleneck of port operation moving from the quay side to the yard area, storage yard management is becoming increasingly important in the container terminal. This paper studies on storage yard management in container terminal, a flexible yard template strategy is proposed instead of the fixed yard template strategy. Based on the strategy, an integrated optimization model simultaneously considering space allocation and yard crane deployment for the tactical storage yard management is formulated. Besides, Numerical experiments are conduced to verify the effectiveness of the proposed strategy and mathematical model.     

海铁联运港口混合作业模式下轨道吊与集卡协同调度

在集装箱海铁联运港口中,铁路作业区作为连接铁路运输和水路运输的重要节点,其装卸效率将影响集装箱海铁联运的整体效率。首先,对比分析了“船舶-列车”作业模式和“船舶-堆场-列车”作业模式的特点,并结合海铁联运港口实际作业情况提出了混合作业模式。然后,以轨道吊完工时间最短为目标构建混合整数规划模型,既考虑了班列和船舶的作业时间窗约束,又考虑了轨道吊间干扰和安全距离、轨道吊和集卡接续作业和等待时间等现实约束。针对遗传算法在局部搜索能力方面的不足,将启发式规则与遗传算法相结合设计了求解轨道吊与集卡协同调度问题的混合遗传算法（HGA）,并进行了数值实验。实验结果验证了所提模型和混合算法的有效性。最后通过设计实验分析集装箱数量、岸边箱占比、轨道吊数量和集卡数量对轨道吊完工时间和集卡完工时间的影响,发现同等集装箱数量下岸边箱占比提高时,应通过增加轨道吊数量来有效缩短完工时间。     

An investigation into knowledge-based yard crane scheduling for container terminals

To resolve the problems of operational efficiency, energy consumption and operational cost of an entire container terminal, the yard crane scheduling secures a crucial position during terminal operational process. Accordingly, it is imperative to develop an efficient yard crane scheduling strategy. In this study, the knowledge acquisition was initially conducted. Subsequently, a knowledge sorting process, including the taxonomic tree generation and organization of acquired knowledge, was completed. Afterwards, the rules were extracted for the purpose of yard crane scheduling. Furthermore, a mechanism was deployed for knowledge reasoning. Consequently, a knowledge-based system was established with regard to yard crane scheduling. To this end, a case study was used to illustrate the proposed knowledge-based system.     

An optimization methodology for intermodal terminal management

A solution to the problems of resource allocation and scheduling of loading and unloading operations in a container terminal is presented. The two problems are formulated and solved hierarchically. First, the solution of the resource allocation problem returns, over a number of work shifts, a set of quay cranes used to load and unload containers from the moored ships and the set of yard cranes to store those containers on the yard. Then, a scheduling problem is formulated to compute the loading and unloading lists of containers for each allocated crane. The feasibility of the solution is verified against a detailed, discrete-event based, simulation model of the terminal. The simulation results show that the optimized resource allocation, which reduces the costs by [frac13], can be effectively adopted in combination with the optimized loading and unloading list. Moreover, the simulation shows that the optimized lists reduce the number of crane conflicts on the yard and the average length of the truck queues in the terminal.     

多场桥分区域平衡策划下的集装箱堆场箱位分配问题

集装箱码头堆场出口箱箱位分配和场桥调度对码头运营效率有重要影响. 为了合理分配箱位和调度场桥, 采用分区域平衡策划方法, 在给定批量任务下, 考虑场桥实际作业中的安全距离, 以均衡各场桥作业任务量和减少场 桥的非装卸时间为目标, 建立混合整数规划模型, 并设计遗传算法求解, 通过不同批量任务的实验分析验证所提出方法的有效性. 研究表明, 分区域平衡策划方法可以更好地解决箱位分配和箱区多场桥联合作业的优化问题.

     

Storage space allocation at marine container terminals using ant-based control

This paper presents a novel approach for allocating containers to storage blocks in a marine container terminal. We model the container terminal as a network of gates, yard blocks and berths on which export and import containers are considered as bi-directional traffic. For both export and import containers, the yard blocks are the intermediate storage points between gates (landside) and berths (waterside). Our model determines the route for each individual container (i.e. assign the container to a block to be stored) based on two competing objectives: (1) balance the workload among yard blocks, and (2) minimize the distance traveled by internal trucks between yard blocks and berths. The model utilizes an ant-based control method. It exploits the trail laying behavior of ant colonies where ants deposit pheromones as a function of traveled distance and congestion at the blocks. The route of a container (i.e. selection of a yard block) is based on the pheromone distribution on the network. The results from experiments show that the proposed approach is effective in balancing the workload among yard blocks and reducing the distance traveled by internal transport vehicles during vessel loading and unloading operations.     

Two-stage stochastic programming model for generating container yard template under uncertainty and traffic congestion

Yard template is a space assignment at the tactical level, which is kept unchanged within a long period of time and significantly impacts the handling efficiency of a container terminal. This paper addresses a yard template planning problem considering uncertainty and traffic congestion. A two-stage stochastic programming model is formulated for minimizing the risk of containers with no available slots in the designated yard area and minimizing total transportation distances. The first-stage model is formulated for assigning vessels in each block without considering the physical location properties of blocks, and the second-stage model is formulated for designating physical locations to all blocks. Subsequently, a solving framework based on genetic algorithm is proposed for solving the first-stage model, and the CPLEX (a commercial solver) is used for solving the second-stage model. Finally, numerical experiments and scenario analysis are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution approach.     

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.     

New bounds and algorithms for the transshipment yard scheduling problem

In a modern rail?Crail transshipment yard huge gantry cranes transship containers between different freight trains, so that hub-and-spoke railway systems are enabled. In this context, we consider the transshipment yard scheduling problem (TYSP) where trains have to be assigned to bundles, which jointly enter and leave the yard. The objective is to minimize split moves and revisits. Split moves appear whenever containers have to be exchanged between trains of different bundles, whereas revisits occur if a train has to enter the yard twice, because some container dedicated to this train was not available during its first visit. We extend the basic TYSP, so that additional real-world requirements of modern rail?Crail yards, e.g., the one currently constructed in Hannover-Lehrte, are considered. We provide complexity proofs for different problem settings and present several heuristic procedures as well as one exact algorithm. The paper concludes with computational results showing the efficiency of the proposed algorithms.     

Glass container production scheduling through hybrid multi-population based evolutionary algorithm

Driven by a real-world application in the capital-intensive glass container industry, this paper provides the design of a new hybrid evolutionary algorithm to tackle the short-term production planning and scheduling problem. The challenge consists of sizing and scheduling the lots in the most cost-effective manner on a set of parallel molding machines that are fed by a furnace that melts the glass. The solution procedure combines a multi-population hierarchically structured genetic algorithm (GA) with a simulated annealing (SA), and a tailor-made heuristic named cavity heuristic (CH). The SA is applied to intensify the search for solutions in the neighborhood of the best individuals found by the GA, while the CH determines quickly values for a relevant decision variable of the problem: the processing speed of each machine. The results indicate the superior performance of the proposed approach against a state-of-the-art commercial solver, and compared to a non-hybridized multi-population GA.     

A heuristic for retrieving containers from a yard

This paper presents a three-phase heuristic to solve for an optimized working plan for a crane to retrieve all the containers from a given yard according to a given order. The optimization goal is to minimize the number of container movements, as well as the crane's working time. After generating an initial feasible movement sequence, the second phase reduces the length of the sequence by repeatedly formulating and generating a binary integer program. With another mixed integer program, phase three reduces the crane's working time by adjusting the movement sequence through iterations. Numerical testing results show that the heuristic is able to solve instances with more than 700 containers, which is within the range of real-world applications. Moreover, the number of movements approaches the lower bound in most cases, and the resulting movement sequence is efficient.