集装箱装入问题的研究

集装箱装载问题是一种有广泛应用背景的组合优化问题,它属于NP-hard问题。禁忌搜索算法(TS)是求解组合问题的一种主要方法,有很强的全局搜索能力。集装箱装入属于有多种约束的空间资源优化问题。约束条件多,求解困难。根据同类型货物一次性装载的思想,提出了一种新的基于空间划分的启发式算法。     

应用自适应遗传算法解决集装箱装载问题

集装箱配载是一个复杂的组合优化问题，其约束条件多，属于NP完全问题，并且求解难度大．在满足一定的约束条件下。提出一种3维集装箱装载的自适应遗传算法．算法中考虑了货物放置方向和装载容积等约束条件，给出了有效的解码算法．实例仿真结果表明了该算法的有效性和实用性．     

基于装箱树算法求解集装箱装载问题的研究

研究了把同种货物装入一个集装箱内，使箱子内的空间利用率为最大的集装箱装载问题．首先，运用启发式算法,充分考虑了箱子和货物的方位、剩余空间等问题．然后，通过主空间装填、空间分层、剩余空间优化等建立一个装箱树．最后，用Java程序完成装箱树算法，并实现集装箱装载问题的求解．用实例验证了算法的可行性，能够投入实际应用．     

蚁群算法求解复杂集装箱装载问题

针对复杂集装箱装载问题(CLP),应用启发式信息与蚁群算法求解了最优装载方案。首先,建立了复杂集装箱装载问题的数学模型,利用蚁群算法对解空间的强搜索能力、潜在并行性及可扩充性,结合三空间分解策略将布局空间依次分割;然后,装入满足约束条件的最优货物块,完成不同大小三维矩形货物的装载布局。在此基础上,设计了基于空间划分策略的蚁群算法。最后以700件货物装入40尺(12.025m)高柜箱进行计算,结果表明该方法能提高集装箱的空间利用率,同时兼顾了多个装载约束条件,可应用性好。     

混合二元蚁群算法求解集装箱装载问题

集装箱装载问题是一个具有复杂约束条件的组合优化问题,属于NP-hard问题。针对集装箱装载问题的特点,设计了空间三叉树,对可利用空间采用三叉树划分策略,利用二元蚁群算法结合启发式算法进行求解,即先利用二元蚁群算法确定预备装入货物集,再用启发式算法决定货物的装入优先级顺序,并给出了有效的装箱算法。实例结果表明该算法的有效性和实用性。     

集装箱装载的一种启发式算法

多约束条件下的三维装箱问题是一个复杂的组合优化问题,属于NP-HARD问题，其求解是很 困难的．所以在实际应用中,往往采用一些启发式算法来求解．本文在考虑一些实际应用中 的约束条件下，提出了一种三维集装箱装载的启发式算法．此算法采用了三空间分割、平均 高度装载、货物合并、空间合并等策略，考虑了方向、重量、优先顺序、货物的配置位置等 约束条件．通过逐步淘汰差的装载方案，最后达到满意的装载．实例仿真说明了该算法的有 效性和实用性，能够直接用于实际应用中．     

有卸货顺序约束的集装箱装载问题及算法研究

对有卸货顺序约束的三维集装箱问题进行了描述.基于禁忌规则,采用了求解该问题的模拟退火算法,设计了货物的摆放规则和序列生成方式.采用3种邻域,根据邻域的不同,构造了2种禁忌表.根据问题的特点,在模拟退火算法抽样过程中加入了禁忌规则.介绍了算法的原理,给出了具有代表性算例试验结果并且进行了分析.试验结果表明,提出的混合算法对有卸货顺序约束的集装箱三维装载问题的有效性.     

求解三维装箱问题的混合遗传模拟退火算法

集装箱装载是货物运输过程中重要的一步,其属于NP-hard问题。为了提高效率,降低成本,提出了以集装箱体积利用率最大化为目标建立三维装载模型,同时考虑体积约束、重量约束、重心约束、方向约束。利用混合遗传、模拟退火与三空间分割启发式装载算法求解模型,算法中融入局部最优解保存策略来避免局部较好解在后续的算法过程中出现适应度降低的情况。通过强异类算例与弱异类算例对算法进行性能测试,并结合具体的货物装载数据,得出三维装载图与目标函数值。结果表明,该算法应用于集装箱装载有着较好的效果。     

满足货物承载能力约束的集装箱装载问题研究

本文采用基于矩阵的货物空间约束表达形式和货物承载能力约束表达形式以及简单块生成策略,对基于Beam Search算法的集装箱装载算法进行了改进,以使其能够有效进行满足货物承载能力约束的集装箱装载问题的优化计算,实验结果表明了该算法的有效性。     

一种求解集装箱装载问题的启发式算法

所谓集装箱装载问题,就是将若干大小不同的长方体盒子装进一个大小已知的长方体容器,其目标是最大化容器的积裁率.对这一问题,国内外学者利用不同的哲学思想,提出了诸如遗传算法、模拟退火算法等求解算法.本文提出一种求解此问题的基于最大穴度优先原则的启发式算法.算法中使用了两个重要的策略:最大穴度原则和最小边度原则.用一些公开的算例对算法性能进行了实算测试,测试结果表明:算法所得结果的容器积载率高,是求解集装箱装载问题的有效算法.     

A tree search algorithm for the container loading problem

This paper presents a binary tree search algorithm for the three dimensional container loading problem (3D-CLP). The 3D-CLP is about how to load a subset of a given set of rectangular boxes into a rectangular container, such that the packing volume is maximized. In this algorithm, all the boxes are grouped into strips and layers while three constraints, i.e., full support constraint, orientation constraint and guillotine cutting constraint are satisfied. A binary tree is created where each tree node denotes a container loading plan. For a non-root each node, the layer set of its left (or right) child is obtained by inserting a directed layer into its layer set. A directed layer is parallel (or perpendicular) to the left side of the container. Each leaf node denotes a complete container loading plan. The solution is the layer set whose total volume of the boxes is the greatest among all tree nodes. The proposed algorithm achieves good results for the well-known 3D-CLP instances suggested by Bischoff and Ratcliff with reasonable computing time.     

一种高效的同尺寸长方体的装箱算法

针对应用广泛的同尺寸长方体货品的装箱问题,本文运用分层装载方案,根据货品的长宽高采用三种不同的层高,利用动态规划算法分别计算三种层的最大装载量,再通过背包算法对层进行组合,得出装箱的最优方案。该算法复杂度低,装载方案简单。     

求解三维装箱问题的多层树搜索算法

提出了一种求解三维装箱问题的多层树搜索算法, 该算法采用箱子–片–条–层–实体的顺序生成装载方案, 装载方案由实体表示. 该算法由3层搜索树构成. 第1层为三叉树, 每个树节点的3个分叉分别对应向实体中填入XY面平行层、XZ面平行层、YZ面平行层; 第2层为二叉树, 每个树节点的两个分叉分别对应向层内装载两个相互垂直的最优条; 第3层为四叉树, 用于将同种的多个箱子生成片. 在同时满足摆放方向约束和完全支撑约束的前提下, 该算法求解BR12~BR15得到的填充率高于现有装箱算法.     

A parallel multi-population biased random-key genetic algorithm for a container loading problem

This paper presents a multi-population biased random-key genetic algorithm (BRKGA) for the single container loading problem (3D-CLP) where several rectangular boxes of different sizes are loaded into a single rectangular container. The approach uses a maximal-space representation to manage the free spaces in the container. The proposed algorithm hybridizes a novel placement procedure with a multi-population genetic algorithm based on random keys. The BRKGA is used to evolve the order in which the box types are loaded into the container and the corresponding type of layer used in the placement procedure. A heuristic is used to determine the maximal space where each box is placed. A novel procedure is developed for joining free spaces in the case where full support from below is required. The approach is extensively tested on the complete set of test problem instances of Bischoff and Ratcliff [1] and Davies and Bischoff [2] and is compared with 13 other approaches. The test set consists of 1500 instances from weakly to strongly heterogeneous cargo. The computational experiments demonstrate that not only the approach performs very well in all types of instance classes but also it obtains the best overall results when compared with other approaches published in the literature.     

基于分片复用的多版本容器镜像加载方法

容器将应用和支持软件、库文件等封装为镜像，通过发布新版本镜像实现应用升级，导致不同版本之间存在大量相同数据.镜像加载消耗大量时间，使容器启动时间从毫秒级延迟为秒级甚至是分钟级.复用不同版本之间的相同数据，有利于减少容器加载时间.当前，容器镜像采用继承和分层加载机制，有效实现了支持软件、库文件等数据的复用，但对于应用内部数据还没有一种可靠的复用机制.提出一种基于分片复用的多版本容器镜像加载方法，通过复用不同版本镜像之间的相同数据，提升镜像加载效率.方法的核心思想是：利用边界匹配数据块切分方法将容器镜像切分为细粒度数据块，将数据块哈希值作为唯一标识指纹，借助B-树搜索重复指纹判断重复数据块，减少数据传输.实验结果表明，该方法可以提高5.8X以上容器镜像加载速度.     

串联式集装箱重心偏移自适应修正算法研究

针对串联式集装箱分箱计重中因货物配置不匀导致的重心随机偏移问题,提出了一种基于径向基函数神经网络的自适应修正算法。该算法利用K-Mean聚类算法来确定RBF神经网络隐含层的中心向量;并采用RLS算法来调整隐含层与输出层之间的连接权值。该算法模型能针对各种车型并在各种车速下实现串联式集装箱的重心动态计算,从而获得准确的分箱重量。通过RBF及BP算法的仿真对比实验表明：基于RBF的修正算法具有更高的环境适应性和更低的计重误差率,能真正达到串联式集装箱重心自适应偏移纠正的目的。     

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.     

An iterated construction approach with dynamic prioritization for solving the container loading problems

This paper addresses the single and multiple container loading problems, which forms the core engine of a warehouse management system we are contracted to implement for a Hong Kong logistics company. We propose to use dynamic prioritization to handle the awkward box types, whereas the box type with a higher priority is packed onto lower surfaces of the container for the single container case, or packed in earlier containers for the multiple container case. The solution found in one iteration of the algorithm is analyzed, and the priorities are updated and used in the next iteration. This approach provides very competitive results using standard benchmark data sets as compared with other methods. It helps to reduce the difficulty in system implementation and maintenance, because the algorithm is easy to understand for practitioners in the local industry, and it is applicable for both the single and multiple container loading problems at the same time. In addition, we find the existing test data for the multiple container loading problem to be deficient and supplement them by generating new test data consisting of 2800 test cases. Last but not least, our algorithm has been packaged into a software component with full graphical user interface and integrated into a warehouse management system for daily operations.