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。     

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。     

以自动化码头为中心的船舶配载优化

针对自动化码头船舶配载效率较低的问题,为了提高设备资源利用率,提出了一种基于船舶配载特点设计的固定集搜索（FSS）算法。首先,在考虑一般船舶配载原则的基础上,以桥吊作业计划为依据,引入箱区作业均衡因素,将最小化箱区翻箱量、总装船时间以及尽可能的箱区作业均衡作为目标,建立自动化码头船舶配载混合整数规划模型;其次,通过固定较优解中多次出现的元素来寻求最优解。实验结果表明,不同规模的实例下,FSS算法相较于Cplex,翻箱量和不均衡箱数分别平均减少了22.3%和11.7%,目标函数值平均优化了6.5%;所提固定集搜索算法与粒子群优化（PSO）算法、遗传算法（GA）和蚁群优化（PSO）算法相比,目标函数值平均优化了2.1%,凸显了FSS算法更高的配载效率。而为了增加案例的多样性,对箱区堆栈分布和比例进行了调整。在这个情况下,FSS算法的不均衡箱数相较上述三种算法平均减少了19.3%,设备资源利用率更高。     

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。     

自动化码头堆场穿越式双ASC调度

随着人工智能等科技的发展,海铁联运的运输模式加快了集装箱码头自动化的发展进程,自动化已经成为集装箱码头发展的必然趋势。对自动化集装箱码头堆场内可穿越式双自动堆垛起重机（ASC）在同一箱区内进行作业的过程进行研究,考虑不同大小的ASC的运行速度和单位时间内耗能不一样,以及在同一贝位两个ASC存在冲突的情况,建立多目标混合整数模型,得出完成所有集装箱任务的总时间和总耗能。并通过改变不同的参数来设计三组实验,实验结果表明,可以通过调整集装箱任务量、双ASC的速度、双ASC装载和空载的耗能之比来减少码头作业集装箱任务的总时间和总能耗。     

集装箱装船顺序的多目标整数规划优化模型

为了解决集装箱堆场装船顺序问题,根据集装箱船舶和配载的实际情况,考虑集装箱的航程,结合集装箱的装载位置,以集装箱堆场的翻箱率、船舶卸载时的翻箱率以及装船后的稳性为目标,建立了装船顺序的多目标规划模型,并基于粒子群算法构造了求解算法,通过MATLAB进行仿真实验,数据结果表明,该模型具有一定的的合理性与可行性。     

自动化码头AGV充电与作业的集成调度研究

为了提高自动化集装箱码头AGV（Automated Guided Vehicle）的作业效率,根据采用电力驱动的AGV作业时的充电需求和运输过程的特性,考虑了垂岸式集装箱堆场布局和AGV充电过程对实际作业的影响,以最大化AGV充电利用率、最小化最末任务完成时间、最小化AGV空载时间为目标,以AGV充电后的续航能力等为约束条件,以遗传算法为研究方法,构建了考虑充电过程的自动化码头AGV作业的调度模型。通过算例分析,对比了遗传算法与混合整数规划算法的求解效果,分析了参与运输的AGV数量对运输时间的影响,也验证了遗传算法给出的调度方案的可信性。最后得出结论：针对该问题,遗传算法可以快速、高效地给出值得信赖的AGV调度方案。     

不确定环境下码头堆场设备集成调度优化

集装箱码头堆场设备调度优化中,对确定条件下的内集卡和场桥的联合调度研究较多,且没有考虑外集卡的随机到达情况。考虑内集卡和场桥作业过程中的不确定性因素,包括：内集卡行驶速度,场桥行走速度和作业时间,并考虑外集卡随机到达堆场对于内集卡调度作业的影响,构建了不确定因素条件下的堆场设备集成调度优化模型,其优化目标是在考虑外集卡随机到达的情况下,最优化堆场设备的作业时间。设计了求解模型的粒子群算法,并比较了一般确定性模型和考虑不确定因素优化模型的结果。算例结果表明,所建立的模型和算法能有效真实地反映不确定因素对集装箱码头堆场设备作业的影响。     

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.

     

大规模集装箱装载问题

对于大规模三维装箱问题,利用区域分裂法的基本思想,将复杂的大规模装箱问题通过货物的重组分解为若干个小区域上的问题,然后通过合并小区域之间的解得到整个区域的解。该方法能分解大型问题为小型问题,复杂区域问题为简单区域问题。在传统演化算法的基础上,引入了模拟退火产生新个体思想对其进行改进,提高了算法的运行效率和解的有效性。通过对某港口案例进行测试,结果证实该算法能满足实际装载需求。     

基于区块链原理的集装箱共享模式下集装箱运输流程建模与仿真

针对集装箱存量共享与增量共享均不能有效实施的问题，提出基于区块链原理的集装箱共享模式。首先，阐述基于区块链原理的集装箱共享模式的运作机制；其次，分析在该模式的作用下，集装箱运输流程发生的变化；然后以Petri网理论为基础，使用CPN Tools分别建立传统模式与基于区块链集装箱共享模式下的集装箱运输流程的赋时着色Petri网（CPTN）模型；最后，对模型进行仿真实验，选取不同模式下接收订单到提取空箱装货的时间、路运段空车行驶时间比例、订单损失率、作漏装的集装箱比例这四项指标对比分析。实验结果显示，与传统模式下相比，基于区块链的集装箱共享模式下，发货人提箱时间缩短，集卡空车行驶比例减小5.28%，不再出现因船期与订单时间窗不匹配而损失的订单，作漏装的集装箱比例减小6.99%。通过仿真实验结果可知，基于区块链原理的集装箱共享模式不仅能够弥补集装箱存量共享与增量共享中存在的不足，而且对集装箱运输流程起到优化作用，是集装箱运输行业实现降本增效的一种有效途径。     

A job grouping approach for planning container transfers at automated seaport container terminals

This paper proposes a practical job grouping approach, which aims to enhance the time related performance metrics of container transfers in the Patrick AutoStrad container terminal, located in Brisbane, Australia. It first formulates a mathematical model of the automated container transfers in a relatively complex environment. Apart from the consideration on collision avoidance of a fleet of large vehicles in a confined area, it also deals with many other difficult practical challenges such as the presence of multiple levels of container stacking and sequencing, variable container orientations, and vehicular dynamics that require finite acceleration and deceleration times. The proposed job grouping approach aims to improve the makespan of the schedule for yard jobs, while reducing straddle carrier waiting time by grouping jobs using a guiding function. The performance of the current sequential job allocation method and the proposed job grouping approach are evaluated and compared statistically using a pooled t-test for 30 randomly generated yard configurations. The experimental results show that the job grouping approach can effectively improve the schedule makespan and reduce the total straddle carrier waiting time.     

基于.NET平台的容器管理持久化

介绍了一种基于.NET平台的容器管理持久化技术,参照EJB规范并结合.NET平台的特点,使容器管理持久化技术不再是J2EE架构中独有的技术。为用.NET开发一个易于维护、升级、增强功能的大型电子商务应用,构造一个优秀的持久化容器。     

A fully verified container library

The comprehensive functionality and nontrivial design of realistic general-purpose container libraries pose challenges to formal verification that go beyond those of individual benchmark problems mainly targeted by the state of the art. We present our experience verifying the full functional correctness of EiffelBase2: a container library offering all the features customary in modern language frameworks, such as external iterators, and hash tables with generic mutable keys and load balancing. Verification uses the automated deductive verifier AutoProof, which we extended as part of the present work. Our results indicate that verification of a realistic container library (135 public methods, 8400 LOC) is possible with moderate annotation overhead (1.4 lines of specification per LOC) and good performance (0.2 s per method on average).     

Optimal control of container cranes

A dynamical model of container cranes is derived by using Lagrange's equation. When a ship is loaded or unloaded with containers, the total motion of the container load is divided into five fundamental sections. For each fundamental type of motion, the optimal control is calculated such that the corresponding trajectory satisfies the specified boundary conditions and that the swing of the container load during the transfer is minimized. A new algorithm which is employed for computing the optimal control is explained in detail. Some results of numerical computation are also shown.     

Second-order sliding-mode control of container cranes

Moving a suspended load along a pre-specified path is not an easy task when strict specifications on the swing angle and on the transfer time need to be satisfied. Intuitively, minimizing the cycle time and the load swing are conflicting requirements, and their satisfaction requires proper control actions, especially if some uncertainties in the system dynamics are present. In this paper we propose a simple control scheme, based on second-order sliding modes, which guarantees a fast and precise load transfer and the swing suppression during the load movement, despite of model uncertainties and unmodeled dynamic actuators. Such controller has been tested on a laboratory-size model of the crane, and some experimental results are reported.     

KCSS: Kubernetes container scheduling strategy

The Journal of Supercomputing - The Kubernetes framework is a well-known open-source container-orchestration system widely used in industrial and academic fields. In this paper, we introduce a new...     

Heuristic algorithms for container pre-marshalling problems

A container pre-marshalling problem is to find a sequence of container movements to reach final container layout satisfying certain conditions. Two container pre-marshalling problems that are denoted as problem Type-A and Type-B are defined in this paper. Two labelling algorithms, which denote as Heuristic-A and Heuristic-B, are proposed to solve these two container pre-marshalling problems, respectively. Experiments retrieved from past literature and generated by computer program are used to verify the performance of the two algorithms. According to the output results, these proposed algorithms are able to yield a competitive solution in comparison with other methods. Computational results and model variations are discussed.     

Workload-based yard-planning system in container terminals

Traditionally, the yard-planning problem has been considered to be the assignment of yard spaces to arriving vessels in practices of container terminals. This study proposes an integrated decision-making framework for the yard-planning that simultaneously considers various resources such as storage space, yard cranes, and traffic area in container terminals for planning. The decision-making framework in this study is based on a mathematical model, which supports intelligent yard-planning activities considering work-loads on various related resources. Further, it is shown that the decision-making framework for the yard-planning can be extended to that for simultaneous decisions on yard plans and handling capacity of yard cranes per block. A heuristic algorithm is also proposed in order to reduce the computational time for planning. Numerical experiments are conducted to validate the models and algorithms proposed in this study.