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.     

考虑箱区作业均衡的自动化码头集装箱船舶配载计划

在考虑箱区作业均衡的基础上,研究自动化码头集装箱船舶的配载计划问题.以堆场翻箱时间最小、集装箱水平运输时间最短以及堆场箱区作业不均衡水平最小为目标建立0-1整数规划模型.使用CPLEX工具精确求解5个不同规模的算例得到配载结果,并对比是否考虑箱区作业均衡的配载方案.结果显示5组算例的翻箱量均为0,有效地满足翻箱时间最小的目标.在配载计划中考虑箱区作业均衡,所得的箱区作业不均衡指数较不考虑箱区作业均衡有一定程度的降低,最高降低19.0个百分点,平均降低7.2个百分点.结果表明所提模型可有效解决自动化码头配载计划问题,保证箱区作业的均衡性,提升码头的装船作业效率.     

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。     

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。     

堆场集装箱翻箱的PCNN优化控制算法

集装箱的堆存状态与理想发箱顺序很难保持一致, 翻箱操作是不可避免的. 为降低堆场的翻箱率, 提高作业效率, 在获得集装箱的取箱顺序前提下, 将每取一个集装箱所产生的可能状态视为一个状态结点, 所需的翻箱次数加一个基数作为状态结点间的连接权, 把翻箱优化问题转化为最短路径求解问题. 脉冲耦合神经网络(Pulse-coupled neural network, PCNN)具有独特的自动波并行传播的特性, 适用于求解大规模实时问题, 能一次求出源点到其他所有目标点的最短路径, 从而获得最优的翻箱方案. 其所需要的计算量仅正比于最短路径的长度, 与路径图的复杂程度及路径图中的通路总数无关. 这为建立集装箱的智能控制系统奠定了坚实的理论基础.     

A*算法在轨道式龙门起重机提箱作业优化中的应用

集装箱翻箱问题是影响集装箱码头堆场机械操作效率的一个重要的因素。为了解决在堆场中应用不同装卸机械所产生的相关问题,本文选取轨道式龙门起重机作为堆场装卸机械,建立以码头堆场机械作业时间最短为目标,满足堆场实际作业要求的提箱优化数学模型。应用启发式算法A*算法对问题进行求解,并通过对实例的研究,验证A*算法的正确性和有效性。     

A＊算法在轨道式龙门起重机提箱作业优化中的应用

集装箱翻箱问题是影响集装箱码头堆场机械操作效率的一个重要的因素。为了解决在堆场中应用不同装卸机械所产生的相关问题,本文选取轨道式龙门起重机作为堆场装卸机械,建立以码头堆场机械作业时间最短为目标．满足堆场实际作业要求的提箱优化数学模型。应用启发式算法A＊算法对问题进行求解,并通过对实例的研究,验证A＊算法的正确性和有效性。     

基于网络模型的集装箱预翻箱问题研究

集装箱翻箱问题涉及集装箱装卸工作的顺畅与否。以此为研究对象,首先建立了基于网络模型的集装箱翻箱模型,并用算例进行了计算试验。在此基础上,对约束进行了改正,建立了改正的集装箱预翻箱模型,并进行了计算验证,证明了其在计算时间上的压缩。根据实际的单贝集装箱堆存情况,进行了实际堆存状态翻箱的计算试验,取得了阶段性成果。对单贝集装箱的堆存数与翻箱次数的关系进行了研究,发现在堆存数达到17时有较好的堆场利用率和较少的翻箱次数。     

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。     

考虑箱区利用率的出口箱资源配置启发式算法

作为集装箱运输的枢纽,堆场的管理是整个集装箱码头管理的重中之重。对出口集装箱堆场的资源配置研究可以提高出口集装箱的装载效率,提高集装箱码头堆场的作业水平。针对当前堆场资源配置研究中较少考虑箱区作业量平衡与码头运营成本关系的研究现状,在不同箱区作业量差异水平下,设计了以成本最低为目标的出口箱堆场资源配置启发式算法。进行了不同规模的算例实验,并进行了比较。     

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.     

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.     

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.     

基于MAS的集装箱自动化码头协同作业系统模型

ZPMC新型集装箱自动化码头使用低架桥轨道和电动小车构成立体装卸系统,将集装箱的装卸分解为多个起重设备和水平运输设备的协作,实现了集装箱的自动装卸。针对该码头的运作特点,采用MAS(multi-agent system,多智能体系统)方法,将码头中的装卸设备抽象为具有一系列属性和方法的智能体,提出船舶、堆场、任务管理、设备管理等智能体,构建出自动化码头协同作业系统模型。在系统的实现层类图中详细阐述了各agent的属性方法、交互关系,结合就近原则及时间估计优化策略,给出了集装箱装卸最短路径的搜索方案,通过实例分析说明协同作业系统MAS模型能较好地体现该码头分布、动态的运作特征。该研究将对ZPMC集装箱自动化码头的调度优化提供一定的理论指导,同时对完善MAS方法在集装箱码头生产作业中的应用体系起到积极的推动作用。     

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.     

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.     

Routing straddle carriers for the loading operation of containers using a beam search algorithm

This paper discusses how to route straddle carriers during the loading operation of export containers in port container terminals. The objective of the routing is to minimize the total travel distance of straddle carriers in the yard. The routing problem is comprised of the container allocation problem and the carrier routing problem. In the container allocation problem, containers in the yard are divided into multiple classes, each of which will be loaded by a quay crane. The container allocation problem is formulated as a transportation problem. In the carrier routing problem, the sequence of yard-bays that a carrier visits is determined. A beam search algorithm is developed for the carrier routing problem. A numerical experimentation is carried out in order to evaluate the performance of the algorithm.     

基于神经网络的集装箱船港口作业时间预测模型

集装箱船港口作业时间是制作泊位计划的一个重要依据,而集装箱船港口作业时间获取的主要来源是预测。传统预测方法是用装卸集装箱量除以岸桥装卸效率,预测精度较低,且受多种因素的影响,具有复杂的非线性特点。而神经网络在解决复杂的非线性问题方面具有很强的建模能力,所以选取神经网络建立集装箱船港口作业时间预测模型。通过真实数据对预测模型进行训练学习,用测试数据集对模型进行验证,并且与传统预测方法相对比,结果表明了该预测模型在某集装箱港口预测应用的有效性。     

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。