橡胶配合剂─—炭黑装运取样步骤ISO1124：1988（E）

橡胶配合剂─—炭黑装运取样步骤ＩＳＯ１１２４：１９８８（Ｅ）１适用范围本标准规定橡胶工业用炭黑散装、半散装或包装装运取样步骤。２定义本国际标准采用的定义如下：２．１散装：集装箱内装运的炭黑不小于２０００公斤。２．２半散装：集装箱内装运的炭黑大于５０公...     

集装箱空箱调运优化问题的研究

在分析集装箱空箱流转过程的基础上,提出建立基于海陆运整体成本最优的集装箱空箱调运随机性优化模型,并将粒子群算法与随机模拟技术相结合用于求解机会约束规划。最后通过对测试算例进行求解,验证了该模型和算法的合理性和有效性。     

集装箱空箱调运优化问题的研究

在分析集装箱空箱流转过程的基础上,提出建立基于海陆运整体成本最优的集装箱空箱调运随机性优化模璎,并将粒子群算法与随机模拟技术相结合用于求解机会约束规划.最后通过对测试算例进行求解,验证了该模型和算法的合理性和有效性.     

集装箱滚装托盘系统在弹药配送中的应用研究

介绍了美军近年来发展的一种新型弹药配送系统,即集装箱滚装托盘装运系统。对其系统组成、配送力量和配送流程进行了详细阐述,在此基础上提出了对建设我军弹药配送体系的几点启示。     

谈海运集装箱危险货物安全运输中货代工作的重要性

国际海上集装箱危险货物的运输离不开国际货运代理。危险货物的托运、申报、装箱等工作通常都是由国际货运代理来完成的,货代工作直接影响危险货物的安全出运和顺利到达。主要阐述货代在集装箱装运危险货物的具体工作,并分析货代工作对海运集装箱危险货物安全运输的影响。     

基于OOPN的港口集装箱堆场作业流程仿真优化研究

在阐述集装箱港口平面布局、装卸系统以及集装箱堆场的基本职能、堆存的作业流程之后,利用面向对象Petfi网（OOPN）的建模方法对堆场作业进行建模并做深入分析,并利用计算机模拟仿真技术（ExtendSim仿真软件）,结合所建立的集装箱堆场系统的OOPN模型,对集装箱港口的整个生产作业系统进行数字仿真并实现了模拟,通过对模型输出的仿真结果进行综合分析,剖析堆场的物流活动流程,对其进行集最优化处理,以达到提高堆场的资源利用率、使资源配置成本最小的目的。     

基于OOPN的港口集装箱堆场作业流程仿真优化研究

在阐述集装箱港口平面布局、装卸系统以及集装箱堆场的基本职能、堆存的作业流程之后,利用面向对象Petri网(OOPN)的建模方法对堆场作业进行建模并做深入分析,并利用计算机模拟仿真技术(ExtendSim仿真软件),结合所建立的集装箱堆场系统的OOPN模型,对集装箱港口的整个生产作业系统进行数字仿真并实现了模拟,通过对模型输出的仿真结果进行综合分析,剖析堆场的物流活动流程,对其进行集最优化处理,以达到提高堆场的资源利用率、使资源配置成本最小的目的.     

面向支线港腹地的集装箱卡车运输网络成本模型研究——直接装运策略下

本文研究了由一个集装箱支线港码头和若干收货人组成的面向港口腹地的集装箱卡车运输网络。着眼于运输网络的总成本,该网络涉及到两种类型的决策者:集装箱码头货运站经营人和收货人,前者负责提供运输服务,而收货人负责租赁集装箱并与货运站签订运输合同。本文建立直接装运运输策略下,面向支线港腹地的集装箱卡车运输网络的总成本模型。     

面向支线港腹地的集装箱卡车运输网络G_1的成本模型研究——巡回装运策略下

本文研究了由一个集装箱支线港码头和偶数个收货人组成的面向港口腹地的集装箱卡车运输网络G_1。着眼于运输网络的总成本,该网络涉及到两种类型的决策者:集装箱码头货运站经营人和收货人,前者负责提供运输服务,而收货人负责租赁集装箱并与货运站签订运输合同。本文建立巡回装运运输策略下,面向支线港腹地的集装箱卡车运输网络G_1的总成本模型。     

基于蚁群算法的码头集装箱卡车路径优化研究

分析了集装箱码头作业流程及其传统作业工艺,提出了使用蚁群算法,以集装箱卡车到达目的地的距离最短为目标．建立集装箱卡车路径优化模型,为码头集装箱卡车寻求出发点与目的地之间的最短路径。实验表明,该方法能有效解决集卡最短路径搜索问题。     

一种INSS动态反馈负载均衡算法

负载均衡策略是智能网络存储系统(INSS)的关键性技术之一。本文介绍了智能网络存储系统的存储结构,提出了一种新的负载均衡算法。该算法采用周期动态反馈原理,以节点的最小化负载方差为目标,动态调整节点分配权值,最后引入随机概率的转发模式。在实验室搭建的系统平台上,对该算法以及WRR和WLC两种负载均衡算法进行了对比测试,试验结果表明该算法性能更优。     

Elman网络在短期负荷预测中的应用

文章介绍的Elman神经网络的短期负荷预测模型,具有模型简单、运算效率高的特点,并具有较好的全局最优性能,从而很好地克服了传统BP算法容易陷入局部极小点的缺陷。文章还比较了Elman网络和BP网络结构的建模效果。通过仿真实验证明了Elman神经网络具有动态特性好、逼近速度快、精度高等特点,是一种实用、可靠的负荷预测方法。     

Probabilistic forecasting of industrial electricity load with regime switching behavior

This paper suggests a novel inhomogeneous Markov switching approach for the probabilistic forecasting of industrial companies’ electricity loads, for which the load switches at random times between production and standby regimes. The model that we propose describes the transitions between the regimes using a hidden Markov chain with time-varying transition probabilities that depend on calendar variables. We model the demand during the production regime using an autoregressive moving-average (ARMA) process with seasonal patterns, whereas we use a much simpler model for the standby regime in order to reduce the complexity. The maximum likelihood estimation of the parameters is implemented using a differential evolution algorithm. Using the continuous ranked probability score (CRPS) to evaluate the goodness-of-fit of our model for probabilistic forecasting, it is shown that this model often outperforms classical additive time series models, as well as homogeneous Markov switching models. We also propose a simple procedure for classifying load profiles into those with and without regime-switching behaviors.     

MPLS网络负载均衡的聚类算法研究

在分析了常用的几种QoS保障的算法基础上,给出了负载均衡模型,提出应用数据挖掘中的聚类算法求解MPLS网络负载均衡问题。该算法融入权重信息有效地提高了搜索效率。仿真结果证明算法是有效的,显著改善了传统MPLS网络负载均衡路由算法易于导致的网络流量分布不均衡的状况。     

供应链网络均衡模型研究

供应链是一个典型的动态系统,如何协调系统中各成员之间的利益和关系,寻找供应链网络的最终均衡状态,是供应链管理中的一个重要问题,文章首先以变分不等式为工具,介绍了确定性和不确定性需求条件下的供应链网络均衡模型,然后将该类模型转化为非线性互补问题,运用评价函数（merit function）将其转化为无约束最优化问题。最后通过拟牛顿算法求解该类模型,有效解决了该类模型的求解问题。     

The advantage of avoiding the Armington assumption in multi-region models

Most computable general equilibrium models that describe interactions between multiple regions employ the so-called Armington assumption that goods that are produced in different regions are imperfect substitutes for each other. The paper describes an alternative algorithm based on the assumption of perfect substitutability that accommodates the observation that each region produces all traded goods. For models with many regions and less-that-perfectly mobile factors, the alternative algorithm greatly reduces convergence time. The algorithm is illustrated by comparing two static multi-region models of the 50 U.S. states and the District of Columbia, with and without the Armington assumption. The model without the Armington assumption is then extended to a dynamic 51-region model with agents that have perfect foresight over 6 periods.     

军用集装箱三维装载的优化方法

以军用集装箱装载为研究对象，在考虑实际应用中的一些约束条件下，构造了军用集装箱的装载优化模型，提出了一种军用集装箱三维装载的启发式算法。该算法采用了整体装填、空间合并等策略，考虑了装载方向、优先顺序、重心等约束条件。最后对一个案例进行了计算与分析，验证了该模型的有效性，并且利用3ds max得到了装载方案的三维图。     

交通路网中最优路径算法的道路权重选择

在交通路网中,寻找任意两点间最优路径是出行导航的基本功能。除了最优路径算法自身性能外,道路权重的选择也直接决定了寻径结果的优劣。现有最优路径算法通常以通行能力为道路权重,其可能导致不合理的寻径结果,同时也不具有全局负载均衡的能力。因此本文以Dijkstra算法为例,引入可达性概念作为道路权重,从而弥补以通行能力为道路权重的缺陷。     

An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models

It is shown how to implement an EM algorithm for maximum likelihood estimation of hierarchical nonlinear models for data sets consisting of more than two levels of nesting. This upward–downward algorithm makes use of the conditional independence assumptions implied by the hierarchical model. It cannot only be used for the estimation of models with a parametric specification of the random effects, but also to extend the two-level nonparametric approach – sometimes referred to as latent class regression – to three or more levels. The proposed approach is illustrated with an empirical application.     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.