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。     

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.     

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.     

基于梯形模糊中智数的最短路径求解方法

最短路径的选择是图论中的经典问题之一.复杂环境中对象之间的关系通常具有模糊性、犹豫性、不确定性和不一致性,而中智集是元素的真实程度、不确定程度及谬误程度的集合,更有能力捕捉不完全信息.基于此,基于中智集理论和图理论的中智图最短路径选择成为一个关键问题.针对边长表述为梯形模糊中智数的中智图最短路径求解问题,提出一种扩展的动态规划求解方法.利用基于梯形模糊中智数的得分函数和精确函数来比较路径长度,并给出扩展的动态规划求解最短路径方法,从而得到最短路径和最短路径长度.最后,通过两个算例验证此方法的可行性,通过与Dijkstra算法对比分析说明所提出方法的合理性和有效性,并且分析了采用不同排序方法对中智图最短路径选择的影响.     

基于混合优化策略的智能集装箱预翻箱系统

讨论了一个智能集装箱预翻箱系统的设计与实现。在将集装箱预翻箱问题分解的基础上,采用混合优化策略来实现集装箱预翻箱方案。混合优化策略首先根据堆箱规则构造集装箱堆栈目标状态,然后运用改进广度优先算法搜索搬箱步骤。系统是运用分层设计思想来设计的,它是在生产调度领域应用人工智能理论和方法的有益尝试。     

基于时空网络的自动化集装箱码头自动化导引车路径规划

针对自动化集装箱码头水平搬运作业中自动化导引车路径冲突问题,提出一种基于时空网络的路径优化方法。对于单个运输需求,首先,将路网离散化为网格网络,设计依据时间可更新的时空网络;其次,以任务完工时间最短为目标,基于时空网络下可用路段集合来建立车辆路径优化模型;最后,在时空网络上运用最短路径算法求解得最短路径。对于多个运输需求,为避免路径冲突,根据当前运输需求的路径规划结果更新下一个运输需求的时空网络,并通过迭代最终获得满足规避碰撞和缓解拥堵条件的路径规划。计算实验中,与基本最短路径求解策略（求解算法P）相比,所提方法的碰撞次数降低为0并且最小相对距离始终大于安全距离;与停车等待求解策略（求解算法SP）相比,所提方法最多减少任务总延误时间24　s,且明显降低延误任务占比以及路网平均拥堵度,最大降低程度分别为2.25%和0.68%。实验结果表明,所提方法能够有效求解大规模冲突规避的路径规划问题,并显著提高自动化导引车的作业效率。     

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。     

GIS中最短路径搜索算法

文章讨论了一种在GIS环境下的最短路径规划算法,它根据用户给出的起始结点与目标结点以及必经结点序列和避开结点序列在建立的搜索图基础上分段查找最短路径,最后生成满足用户约束条件的最短路径。     

On the abstraction method for the container relocation problem

The container relocation problem or the blocks relocation problem is a classic combinatorial optimisation problem that occurs in day-to-day operations for facilities that use block stacking systems. A typical place where this problem arises is a container terminal where containers can be stacked vertically in order to utilise the scarce resource of yard surface, thus at times resulting in the unproductive reshuffling moves for containers stacked above the target container for retrieval. Due to the problem class being NP-hard, a number of studies on this topic propose heuristic approaches to solve this problem. There are a few exact methods (search-based algorithms or mathematical programming) proposed for this problem but the feasible problem size of such methods is quite restricted, limiting their practical significance. In this paper, we propose a new insight into reducing the search space of this problem by the abstraction method. Our main contribution to the existing literature is two-fold: the reduction in the search space by the abstraction method and the bidirectional search using the pattern database. Our computational results confirm that our approach enables instances of a near-practical size to be solved optimally within a reasonable computation time.     

Application of a mixed fuzzy decision making and optimization programming model to the empty container allocation

Containerization transportation has been growing fast in the past few decades. International trades have been growing fast since the globalization of world economies intensified in the early 1990s. However, these international trades are typically imbalanced in terms of the numbers of import and export containers. As a result, the relocation of empty containers has become one of the important problems faced by liner shipping companies. In this paper, we consider the empty container allocation problem where we need to determine the optimal volume of empty containers at a port and to reposition empty containers between ports to meet exporters’ demand over time. We formulate this empty container allocation problem as a two-stage model: in stage one, we propose a fuzzy backorder quantity inventory decision making model for determining the optimal quantity of empty container at a port; whereas in stage two, an optimization mathematical programming network model is proposed for determining the optimal number of empty containers to be allocated between ports. The parameters such as the cost of loading container, cost of unloading container, leasing cost of empty container, cost of storing container, supplies, demands and ship capacities for empty containers are considered in this model. By taking advantages of the fuzzy decision making and the network structure, we show how a mixed fuzzy decision making and optimization programming model can be applied to solve the empty container allocation problem. The utilization of the proposed model is demonstrated with a case of trans-Pacific liner route in the real world. Six major container ports on the trans-Pacific route are considered in the case study, including the Port of Kaohsiung, the Port of Hong Kong, the Port of Keelung, the Port of Kobe, the Port of Yokohama and the Port of Los Angles. The results show that the proposed mixed fuzzy decision making and optimization programming model can be used to solve the empty container allocation problem well.     

改进的基于元胞自动机扩展模型的图的最短路径算法

在基于元胞自动机单源点到单节点图的最短路算法的基础之上,通过改进控制演化的终止条件和记录演化过程中的路径信息,提出了单源点到多节点的元胞自动机扩展模型求解图的最短路算法模型，将该算法应用于城市道路交通网的实证研究之中,可以得到路段上任意两端点之间的最短路径及路权。     

基于优先队列的时变网络最短路径算法

提出了基于优先队列的时变网络最短路径算法,能克服传统最短路径算法难以对时变网络求解最短路径的缺陷。提出的时间窗选择策略能够在算法求解过程中为节点选择合适的时间窗以降低路径长度,从而求得精确解。进一步地,算法使用了优先队列组织节点集合以提高计算效率。在随机生成的网络数据以及美国道路数据上的实验表明,基于优先队列的时变网络最短路径算法与经典方法相比,不仅能够求得精确解,运算速度也有所提高。     

The shortest path problem with discrete fuzzy arc lengths

In the past, the fuzzy shortest path problem in a network has attracted attention from many researchers for its importance to various applications. In this paper, we propose a new algorithm to deal with the fuzzy shortest path problem. It is composed of fuzzy shortest path length procedure and similarity measure. The former is presented to determine the fuzzy shortest path length from source node to the destination node in the network, and the latter is used to measure the similarity degree between fuzzy length sets. This algorithm not only can yield shortest length but also can offer the actual shortest path to decision makers. An illustrative example is also included to demonstrate our proposed algorithm.     

应急模糊网络系统最大满意度路径的选取

讨论给定限制期条件下的应急系统模糊路径问题.当边的长度为对称三角模糊数 (Symmetric Triangular Fuzzy Number)时,由于模糊数的不可比性,网络中一般不存在绝对 最短的路.为此,引入了路径满意度函数的概念,从而问题就变成:寻找一条从起点到终点的 通路,应急车辆经过此路的时间不超过限制期t的满意度最大.这样的路径选取问题实际可 转化为一个比例路径问题,尽管许多比例路径问题已被证明是NP问题,完全可以针对问题 的具体特点,运用最短路方法的变权迭代实现对该问题的精确求解.     

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.     

过必经点集且具有额外硬约束的最短路径算法

求解过必经点集的最短路径问题已有多种算法,但其应用到在具有额外硬约束限定条件的场景时存在不足。针对此类问题,提出一种基于深度优先搜索发展的随机搜索算法,由使用者依据现场情况给出数学描述,建模抽象为无向带权图表示;依据路径规划要求定义相关变量,包括路径规划的起点、终点、必经点集以及额外硬约束条件,图信息和节点信息以邻接矩阵的形式保存;搜索过程中对路径的可行性加入额外硬约束条件进行实时判定,最终获得最短路径解。实验仿真和实测结果表明,该算法能有效规避额外硬约束条件下的中间路径,生成合理的最短路径,改善相关问题的可求解性。     

最短路径距离矩阵修正的多维标度定位算法

为了减小最短路径距离矩阵与欧氏距离矩阵之间的差异,提高MDS-MAP(C)算法的节点定位精度,提出一种改进的多维标度节点定位算法.该算法对MDS-MAP(C)算法进行了以下改进:采用启发式的搜索策略对最短路径距离矩阵进行修正,以减少最短路径距离矩阵与实际的欧氏距离矩阵之间的误差;利用smacof算法迭代误差函数代替SVD分解来求解节点的定位问题,以优化和改善节点定位的求解过程.实验结果表明,与MDS-MAP(C)算法相比,改进算法能够减少最短路径距离的误差,有效提高节点的定位精度,并且对不规则网络具有更好的适应性.