基于网络流的自动化集装箱码头堆场空间分配

为解决自动化集装箱码头堆场空间分配问题,突出自动化集装箱码头堆场的布局特征以及制定堆存计划时的特点,将堆存计划视为一个具有时间和空间维度的网络优化问题,提出一种基于网络流的自动化集装箱码头堆场空间动态分配的模型,并使用禁忌搜索算法来求解。结果表明,通过该方法可以确保同一时间段内作业的进口箱和出口箱均衡的分配在箱区中,同时满足场桥的"重进重出"。最后对CPLEX和禁忌搜索算法求解的结果进行比较,证明了禁忌搜索算法的优越性。     

基于预约和图表示的集装箱出口箱位优选

针对预约交箱机制下集装箱堆场箱位优选问题,提出一种交箱次序与箱位分配的三维图表示法;基于图表示,提出压箱量和龙门吊大车行驶距离的期望求解方法.在此基础上,构建基于图的集装箱堆场出口箱位优选模型,优化堆场龙门吊行车成本和压箱量.开发了改进禁忌搜索算法,利用图表示特性缩小搜索空间并优选搜索方向,提高收敛速度.实验结果表明,所提出的算法能够在合理的时间内获得满意解,较确定性模型可减少堆场作业成本20%以上.     

基于滚动窗策略的港口海铁联运集装箱转运优化

为了解决港口海铁联运转运集装箱作业规模过大的问题,采用滚动窗策略方法研究港口船舶与列车之间转运进口集装箱作业问题,在每个窗口内建立以列车在港停留时间和集装箱在堆场的堆存时间总时间最小为目标的整数规划模型,设计双层遗传算法进行求解。在此基础上,分别讨论了以固定任务数量和固定时间长度为滚动窗口的情况,对比发现以固定时间长度为窗口的滚动窗策略更适用,并将其与已有的调度策略研究成果作比较分析。最后,设置实验比较双层遗传算法和单层遗传算法,并对设备的工作能力进行灵敏度分析。结果表明,滚动调度策略可以灵活解决大规模集装箱转运问题,双层遗传算法的解优于单层遗传算法的解,增加装卸线数和轨道起重机工作能力可以提高集装箱转运效率。     

集装箱装船顺序优化模型及遗传算法

集装箱装船顺序问题是NP完全性问题,建立集装箱船配载数学模型,运用遗传算法,并实验确定适合该模型遗传算法的参数范围。将稳性、减少翻箱、可操作性等重要因素分解为评估策略,按优先级将各种评估策略划分等级,尤其将稳性问题具体到船的每行及每个载位,以寻找最优解。实验结果表明,只要将遗传算法中的参数选择合理,此方案则符合船舶配载要求,且集装箱装船顺序得以优化。     

基于多目标遗传算法的集装箱码头泊位—岸桥分配问题研究

为获得合理的集装箱码头泊位—岸桥分配方案,建立了以最小化船舶在港时间和码头生产成本为目标的优化模型。提出一种多目标遗传算法用于求解该模型,算法中采用染色体组的方式表示可行解,给出了多个约束条件下的交叉算子运算规则,个体的各目标值结合岸桥分配启发式算法求得,并应用Pareto分级方法进行适应度值评价;同时给出了最终实施方案的选择策略。试验算例表明,与单目标优化相比,提出的优化方法能获得使码头综合效益较大的满意解。     

基于启发式算法的集装箱预翻箱问题仿真研究

关于根据已知的集装箱配载图对堆场的集装箱进行预翻箱速度优化问题,为减少倒箱量,缩短船舶在港时间,以集装箱预翻箱过程为研究对象,对其进行仿真研究,达到提高集装箱码头装船效率的目的,提出了相应的启发式算法,并通过算例验证了算法的有效性.利用eM-plant软件分别建立了单贝位和多贝位的预翻箱仿真模型,通过对预翻箱过程仿真时间的统计验证了仿真模型的有效性,并与现有的启发式算法效能进行了比较,效果更好.仿真结果表明,改进方法可为解决目前集装箱堆场预翻箱问题提供了更有效的决策依据.     

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.

     

考虑箱区作业均衡的ACT船舶配载鲁棒优化研究

基于自动化集装箱码头堆场与传统堆场在布局与机械配置方面的区别，将箱区作业均衡因素引入船舶配载问题中，以最小化装船作业时间和箱区作业均衡为目标建立考虑箱区作业均衡的自动化集装箱码头船舶配载混合整数规划模型；同时引入解的保守性参数，将确定性模型转化为考虑桥吊作业时间不确定性的自动化集装箱码头船舶配载鲁棒优化模型，再设计基于编号与排序的禁忌搜索算法对算例进行求解。结果证明了模型的正确性与算法的有效性，且各案例在不考虑箱区作业均衡因素时得到的箱区作业不均衡箱量比考虑该因素时得到的箱区作业不均衡箱量平均高出17.8%，不确定性情况下，“集中配载”现象更加显著。     

动态环境下集装箱码头堆场出口箱箱位分配建模与算法研究

箱位分配是集装箱码头运作优化管理的关键问题.针对出口箱交箱时间的动态不确定性,提出两级调度策略,将出口箱位分配问题分解为贝位分配和贝内具体箱位分配,分别建立规划模型优化龙门吊大车行使距离和翻箱量,并针对两级调度策略特点分别设计开发基于禁忌搜索的求解算法.模拟实验表明,所提出的方法能够适应动态条件变化,改善调度效果,并在合理时间内获得满意解.     

Ad hoc网络在集装箱堆场管理上的应用

在集装箱堆场中,由于集装箱的数量很大,或翻箱作业等使得很难及时准确掌握集装箱的具体位置。该文分析了集装箱堆场管理方面的特点和Ad hoc网络的特点,提出了一种基于Ad hoc网络的集装箱堆场的无线通信网络的模型及其中的关键技术和问题。通过仿真,可以看出该方案解决了集装箱堆场管理中对集装箱具体位置的追踪问题。     

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.     

面向哈佛体系结构的集装箱码头场桥作业调度

集装箱码头堆场作业调度问题一直是国内外相关研究的热点和难点,但由于码头作业的动态性、开放性、强耦合性和复杂性,堆场主要装卸设备场桥的调度配置问题一直未能有较好的解决方案。故提出面向哈佛体系结构的基于Agent建模和仿真模式,并将计算机操作系统中的磁盘臂调度算法和基于仿真的优化思想引入到上述模型中。通过构建相应的多Agent系统仿真得出敏捷高效鲁棒的场桥调度和配置解决方案,从而帮助集装箱码头提高服务水平和竞争力。     

考虑时间窗的集装箱港口场桥全局调度优化

合理高效的场桥调度计划有助于减少场桥与集卡相互等待的时间从而提高港口的运营效率,考虑到在实际操作中会出现多箱区多场桥同时工作、互相冲突等情况,建立了以场桥移动成本和延误成本最小化为目标的数学模型,利用计划时间段和时间窗的概念对场桥作业进行约束,通过遗传算法编码进行求解,并将计算结果与实际操作及其他算法的优化结果相对比,进而验证该模型和算法的有效性和稳定性。     

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.     

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.     

Re-marshaling export containers in port container terminals

In order to speed up the loading operation of export containers onto a ship, the re-marshaling operation is an usual practice in port container terminals. It is assumed that the current yard map for containers is available and a desirable bay layout is provided. A methodology is proposed to convert the current bay layout into the desirable layout by moving the fewest possible number of containers and in the shortest possible travel distance. The problem is decomposed into three sub-problems such as the bay matching, the move planning, and the task sequencing. The bay matching is to match a specific current bay with a bay configuration in the target layout. In the move planning stage, the number of containers to be moved from a specific bay to another is determined. The completion time of the re-marshaling operation is minimized by sequencing the moving tasks in the final stage. A mathematical model is suggested for each sub-problem. A numerical example is provided to illustrate the solution procedure.     

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.     

Scheduling of collaborative operations of yard cranes and yard trucks for export containers using hybrid approaches

Optimizing collaborative operations for yard cranes (YCs) and yard trucks (YTs) is vital to the overall performance of a container terminal. This research investigates four different hybrid approaches developed for dealing with yard crane scheduling problem (YCSP) and yard truck scheduling problem (YTSP) simultaneously for export containers in the yard side area of a container terminal. First, these approaches use a load-balancing heuristic to assign containers to YCs evenly. Following this, each of them employs a specific heuristic/metaheuristic, such as genetic algorithm (GA), particle swarm optimization (PSO) or subgroups PSO (SGPSO), to generate alternative container loading sequences for each YC. Finally, a simulation model is used to simulate loading and transporting of these export containers, evaluate alternative planning results, and finally output the best planning result. Experiments have been conducted to compare these hybrid approaches. The results show Hybrid4 (SGPSO) outperforms Hybrid1 (Sort-by-bay), Hybrid2 (GA), and Hybrid3 (PSO) in terms of makespan.