SPSM and its application in cylindrical shells

In naval architectures, the structure of prismatic shell is used widely. But there is no suitable method to analyze this kind of structure. Stiffened prismatic shell method (SPSM) presented in this paper, is one of the harmonic semi-analytic methods. Theoretically, strong stiffened structure can be analyzed economically and accurately. SPSM is based on the analytical solution of the governing differential equations for orthotropic cylindrical shells. In these differential equations, the torsional stiffness, bending stiffness and the exact position of each stiffener are taken into account with the Heaviside singular function. An algorithm is introduced, in which the actions of stiffeners on shells are replaced by external loads at each stiffener position. Stiffened shells can be computed as non-stiffened shells. Eventually, the displacement solution of the equations is acquired by the introduction of Green function. The stresses in a corrugated transverse bulkhead without pier base of an oil tanker are computed by using SPSM.     

基于改进的BP人工神经网络的物流需求规模预测

为了对物流需求规模进行准确预测,探讨了用于物流需求规模预测的经济指标和物流需求规模的度量指标,再应用粗糙集理论、适应度函数和BP人工神经网络理论建立了用于物流需求规模预测的模型,即改进的BP人工神经网络模型。该模型首先应用粗糙集对BP人工神经网络的输入层进行指标知识约简,以减少BP人工神经网络的复杂度,再在BP人工神经网络中引入适应度函数,以克服传统BP人工神经网络算法易陷入局部最优、训练速度较慢等缺陷,最后,将该模型应用在案例分析中。结果表明,该模型使预测精度得到很大提高;该方法为以后物流需求规模的预测提供了一种新的思路和方法。     

基于异方差的递增时间序列自回归预测的改善方法

依据"利用反双曲正弦函数变换提高数据列光滑程度"的结论,给出了两种异方差条件下递增时间序列自回归预测的改善方法.     

随机扰动项呈离散分布的库存路径和定价问题的模型与算法

针对产品需求价格函数随机扰动项呈离散分布的库存路径和定价问题(Inventory Routing and Pricing Problem,IRPP),利用随机扰动项的离散分布率和零售商库存服务水平要求,以供货商期望收益最大化为目标,构建IRPP优化模型,将禁忌搜索算法嵌入改进的粒子群算法中求解模型.3组不同规模的算例分...     

图的符号k-控制

设G（V,E）为一个图,k为任意的正整数且k不超过｜G｜,若有一个函数f：V｜1,－1｜满足：V中至少有k个点满足f[v]≥1,则称f为图G的一个符号k-控制函数,图G的符号k-控制数定义为γks^-11（G）=min{f（V）｜f为图G的一个符号k-控制}．给出了图的符号k-控制数的下界的一个改进的结论,并确定了轮图的符号k-控制数、     

模糊综合评价法在公司效能评估中的应用

企业效能的评估是任何一个经济体系中的核心问题,因为在企业的发展中,企业效能评估系统起着一种导向性的作用。在研究确定疏浚公司效能的各级评估指标体系的基础上,给出隶属度的确定方法,建立用模糊评价法评估疏浚公司综合效能的可行方法。疏浚公司综合效能评估既有定量指标也有定性指标,模糊评价法对定性指标的评价具有独特优势。     

农业机械化技术在农业发展中的作用及推广对策

近几年我国社会技术水平不断发展与进步,农业机械化技术也逐渐提升并逐步被运用到农业的发展中。但现今我国农业机械化技术还需要在农业发展中进行推广,促使农业经营人员对农业机械化技术的作用充分了解,并将其优势发挥出来,进而提高农业生产效率与质量,提高农业经济收益。以下内容重点对农业机械化技术在农业发展中的作用及推广对策开展探究,进而为农业发展奠定良好的基础。     

An application of the directional distance function with the number of accidents as an undesirable output to measure the technical efficiency of state road transport in India

By using the directional distance function (DDF) of data envelopment analysis (DEA), this study measures the technical efficiency of 37 Indian state road transport undertakings (SRTUs) for the year 2012–13. We employ the DDF as a tool for analyzing a joint production function with both desirable and undesirable outputs (i.e., the number of accidents). A comparison between the results with and without accidents shows that several SRTUs have experienced significant changes in their efficiency scores as well as in their rankings after accounting for the undesirable output. This indicates the importance of including the number of accidents – a safety standard – as representative of the undesirable output in computing the efficiency scores of SRTUs. The results of the Tobit model indicate that SRTUs with greater vehicle productivity are more efficient under both conventional DEA and DDF approaches. We also employed zero-truncated negative binomial model to assess the factors influencing the number of road accident experienced by the Indian SRTUs and found that the accident count was significantly influenced by fleet utilization and vehicle productivity.     

Dynamic pricing for managed lanes with multiple entrances and exits

Priced managed lanes are increasingly being used to better utilize the existing capacity of the roadway to relieve congestion and offer reliable travel time to road users. In this paper, we investigate the optimization problem for pricing managed lanes with multiple entrances and exits which seeks to maximize the revenue and minimize the total system travel time (TSTT) over a finite horizon. We propose a lane choice model where travelers make online decisions at each diverge point considering all routes on a managed lane network. We formulate the problem as a deterministic Markov decision process and solve it using the value function approximation (VFA) method for different initializations. We compare the performance of the toll policies predicted by the VFA method against the myopic revenue policy which maximizes the revenue only at the current timestep and two heuristic policies based on the measured densities on the managed and general purpose lanes (GPLs). We test the results on four different test networks. The primary findings from our research suggest the usefulness of the VFA method for determining dynamic tolls. The best-found objective value from the method at its termination is better than other heuristics for all test networks with average improvements in the objective ranging between 10% and 90% for revenue maximization and 0–27% for TSTT minimization. Certain VFA initializations obtain best-found toll profiles within first 5–50 iterations which warrants computational time savings. Our findings also indicate that the revenue-maximizing optimal policies follow the “jam-and-harvest” behavior where the GPLs are pushed towards congestion in the earlier time steps to generate higher revenue in the later time steps, a characteristic not observed for the policies minimizing TSTT.     

Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks

Reliable and accurate short-term subway passenger flow prediction is important for passengers, transit operators, and public agencies. Traditional studies focus on regular demand forecasting and have inherent disadvantages in predicting passenger flows under special events scenarios. These special events may have a disruptive impact on public transportation systems, and should thus be given more attention for proactive management and timely information dissemination. This study proposes a novel multiscale radial basis function (MSRBF) network for forecasting the irregular fluctuation of subway passenger flows. This model is simplified using a matching pursuit orthogonal least squares algorithm through the selection of significant model terms to produce a parsimonious MSRBF model. Combined with transit smart card data, this approach not only exhibits superior predictive performance over prevailing computational intelligence methods for non-regular demand forecasting at least 30 min prior, but also leverages network knowledge to enhance prediction capability and pinpoint vulnerable subway stations for crowd control measures. Three empirical studies with special events in Beijing demonstrate that the proposed algorithm can effectively predict the emergence of passenger flow bursts.