基于部分集卡到达信息的码头进口箱翻箱优化

集装箱码头进口箱提箱作业过程中,由于外集卡到达时间的不确定性,存在外集卡提箱顺序与目标箱的堆存位置不一致,从而产生大量翻箱.为了解决这一问题,本文以期望翻箱量最小为落箱位置的选择原则,提出最小化二次翻箱启发式算法,利用算例验证算法的有效性,分析集卡到达的信息质量对进口箱提箱翻箱的影响,根据集卡到达信息质量的不同选择最佳翻箱规则.结果表明:提出的翻箱策略可以在获得部分集卡到达信息下有效地减少翻箱,针对不同的集卡到达信息质量,采取相应翻箱规则进行翻箱作业能有效减少翻箱,提高码头堆场的作业效率.     

基于HMM-SVR的船舶动力设备故障模式识别与状态预测研究

船舶动力设备因故障监测信号样本少、变化缓慢且数据特征呈非线性,使得设备故障模式的准确识别和状态预测比较难。鉴于此,文章研究了基于隐马尔科夫模型的故障模式识别方法,利用该模型将微弱变化的信号特征转换为变化较大的对数似然概率对故障模式实现有效识别。在此基础上进一步提出基于HMM-SVR的设备状态预测模型,将遗传算法用于支持向量回归模型参数寻优,并结合隐马尔科夫模型,实现对设备状态的预测。对船用柴油机进行仿真,结果表明上述模型具有较高的识别率,能准确预测船舶动力设备的当前状态。     

Liner shipping network design with emission control areas: A genetic algorithm-based approach

To curb emissions, containerized shipping lines face the traditional trade-off between cost and emissions (CO₂ and SO_x) reduction. This paper considers this element in the context of liner service design and proposes a mixed integer linear programming (MILP) model based on a multi-commodity pickup and delivery arc-flow formulation. The objective is to maximize the profit by selecting the ports to be visited, the sequence of port visit, the cargo flows between ports, as well as the number/operating speeds of vessels on each arc of the selected route. The problem also considers that Emission Control Areas (ECAs) exist in the liner network and accounts for the vessel carrying capacity. In addition to using the MILP solver of CPLEX, we develop in the paper a specific genetic algorithm (GA) based heuristic and show that it gives the possibility to reach an optimal solution when solving large size instances.     

The Storage Space Allocation Problem at a Container Terminal

Due to the asymmetry between the time required to retrieve a container to vessel and the time required to register a container at the gate, the storage space allocation problem solves the best allocation policy of these containers that minimizes the potential number of gantry movements during the assigning process and the future retrieval process. A decision rule-based heuristic is proposed, and three properties are discovered in this research. Comprehensive numerical experiments show that the proposed heuristic can solve the real-sized instances within a second, making it possible to account for uncertainty because the re-optimization effort is negligible. Moreover, when applying the proposed heuristic to a container yard at the Port of Kaohsiung in Taiwan and considering the extreme cases, the heuristic performs even better when the number of containers increases and still requires less than 1 s to solve these extreme cases. Finally, the proposed heuristic can be applied to different layouts of storage space and various material handling machines.     

Multi-criteria optimization of traffic signals: Mobility,safety, and environment

Two-dimensional multi-objective optimizations have been used for decades for the problems in traffic engineering although only few times so far in the optimization of signal timings. While the other engineering and science disciplines have utilized visualization of 3-dimensional Pareto fronts in the optimization studies, we have not seen many of those concepts applied to traffic signal optimization problems. To bridge the gap in the existing knowledge this study presents a methodology where 3-dimensional Pareto Fronts of signal timings, which are expressed through mobility, (surrogate) safety, and environmental factors, are optimized by use of an evolutionary algorithm. The study uses a segment of 5 signalized intersections in West Valley City, Utah, to test signal timings which provide a balance between mobility, safety and environment. In addition, a set of previous developed signal timing scenarios, including some of the Connected Vehicle technologies such as GLOSA, were conducted to evaluate the quality of the 3-dimensional Pareto front solutions. The results show success of 3-dimensinal Pareto fronts moving towards optimality. The resulting signal timing plans do not show large differences between themselves but all improve on the signal timings from the field, significantly. The commonly used optimization of standard single-objective functions shows robust solutions. The new set of Connected Vehicle technologies also shows promising benefits, especially in the area of reducing inter-vehicular friction. The resulting timing plans from two optimization sets (constrained and unconstrained) show that environmental and safe signal timings coincide but somewhat contradict mobility. Further research is needed to apply similar concepts on a variety of networks and traffic conditions before generalizing findings.     

W-SPSA in practice: Approximation of weight matrices and calibration of traffic simulation models

The development and calibration of complex traffic models demands parsimonious techniques, because such models often involve hundreds of thousands of unknown parameters. The Weighted Simultaneous Perturbation Stochastic Approximation (W-SPSA) algorithm has been proven more efficient than its predecessor SPSA (Spall, 1998), particularly in situations where the correlation structure of the variables is not homogeneous. This is crucial in traffic simulation models where effectively some variables (e.g. readings from certain sensors) are strongly correlated, both in time and space, with some other variables (e.g. certain OD flows). In situations with reasonably sized traffic networks, the difference is relevant considering computational constraints. However, W-SPSA relies on determining a proper weight matrix (W) that represents those correlations, and such a process has been so far an open problem, and only heuristic approaches to obtain it have been considered.This paper presents W-SPSA in a formally comprehensive way, where effectively SPSA becomes an instance of W-SPSA, and explores alternative approaches for determining the matrix W. We demonstrate that, relying on a few simplifications that marginally affect the final solution, we can obtain W matrices that considerably outperform SPSA. We analyse the performance of our proposed algorithm in two applications in motorway networks in Singapore and Portugal, using a dynamic traffic assignment model and a microscopic traffic simulator, respectively.     

Subjective-utility travel time budget modeling in the stochastic traffic network assignment

ABSTRACT

In this article, we propose a new model called subjective-utility travel time budget (SU-TTB) model to capture travelers' risk-averse route choices. In the travel time budget (TTB) and mean-excess travel time (METT) model, a predefined confidence level is needed to capture the risk-aversion in route choice. Due to the day-to-day route travel time variations, the exact confidence level is hard to be predicted. With the SU-TTB model, we assume travelers' confidence level belongs to an interval that they may comply with in the route choice. The two main components of SU-TTB are the utility function and the TTB model. We can show that the SU-TTB can be reduced to the TTB and METT model with proper utility function for the confidence levels. We can also prove its equivalence with our recently proposed nonlinear-expectation route travel time (NERTT) model in some cases and give some new interpretation on the NERTT with this equivalence. Finally, we formulate the SU-TTB model as a variational inequality (VI) problem to model the risk-averse user equilibrium (RAUE), termed as generalized RAUE (GRAUE). The GRAUE is solved via a heuristic gradient projection algorithm, and the model and solution algorithm are demonstrated with the Braess's traffic network and the Nguyen and Dupuis's traffic network.     

“船舶—堆场—列车”混堆堆场箱位分配模型

集装箱海铁联运港口堆场作业箱型复杂,为了提高港口作业能力和效率,基于进口、出口箱的混合堆存方式,对"船舶-堆场-列车"作业堆场箱位分配问题进行研究."船舶-堆场-列车"作业堆场负责堆存通过船舶、列车及外集卡进出港口的集装箱.本文在待分配箱的作业时间及箱区已知的前提下,以集装箱堆存产生的压箱数最小为目标,建立"船舶-堆场-列车"作业混堆堆场箱位分配模型,根据模型特点设计了启发式算法进行求解,并进行算例分析.结果表明,所建模型和求解算法能够有效减少堆场的压箱数,满足箱型堆存要求,提高港口作业效率.     

基于神经网络和遗传算法的系泊线长度参数优化

摘 要: 针对多成分系泊线三段长度如何取值的问题,采用一种基于神经网络和遗传算法对深海多成分锚泊系统长度进行优化。应用AQWA软件计算多点系泊FPSO,其时域结果直接用于训练BP神经网络。从而利用神经网络的非线性映射功能构建替代锚泊时域计算网络,大大缩短了优化所需的时间。以FPSO最小平面运动值为目标函数,锚链破断强度作为约束条件,采用遗传算法优化系泊长度。计算结果表明,与传统设计的锚泊长度相比,优化后FPSO在各个浪向下纵荡横荡值均能减少20%以上。 关键词:系泊;神经网络;遗传算法;优化     

基于稳性实时计算技术的船舶破损智能扶正系统研究

基于船舶稳性实时计算技术研究船舶实时状态三维模型数字化算法用于快速计算船舶的大倾角稳性、破舱稳性和平衡浮态。参考遗传算法研究船舶破损智能扶正方案生成算法来实现智能生成扶正方案功能。然后运用上述两种核心算法设计船舶破损智能扶正系统软件,为船舶在突发破损事故时提供一套包含压载水调拨措施与步骤建议的智能扶正方案,提高船舶破损事故的处理能力。