取送货混排的车辆路径问题的模糊动态研究

通过增加车辆预留能力系数,保证在避免货物重新排列的情况下完成取送货.充分考虑物流运输行业的现状,重点研究相关参数模糊且各取货节点动态随机出现的问题.在求解方法上,首先为车辆路线中每一个节点、车辆以及相邻节点间的路径建立标签,并建立初始路径;其次,通过标签插入方法求解模糊动态问题;最后给出了方案应用失败后的处理方法.仿真实例验证了所提方法的有效性.     

城乡结合部道路交叉口多目标信号配时优化模型

随着我国城镇化进程的加快,城乡结合部道路交通问题日益显著。为提高城乡结合部道路交叉口的交通运行效率、环境效益和交通安全,提出信号配时多目标优化方法,综合考虑延误、通行能力、停车次数、车辆尾气排放等因素构建模型,运用遗传算法进行求解。以某典型交叉口为例进行分析,给出改进设计方案并进行交通仿真评价。研究结果表明,通过多目标信号配时优化可以缓解城乡结合部道路交叉口交通拥堵和污染物排放的问题,具有较好的交通信号控制效果。     

基于改进PID神经网络算法的AUV垂直面控制

针对一类小型低速自主水下航行器(AUV)的垂直面运动控制问题,设计了一种改进的PID神经网络控制器,实现对水下航行器在垂直面内深度和俯仰角的全局控制。利用REMUS水下航行器模型搭建了Simulink下AUV垂直面仿真控制系统,仿真结果表明,改进的控制方法克服了原方法中饱和区过大的问题,具有良好的动态性能同时能够适应不同的学习速率和网络初始权重,对水下航行器的工程实际应用具有一定参考价值。     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

竞争环境下新能源汽车嵌入技术服务的定价策略

构建上游技术服务商和下游新能源制造商合作创新的两级供应链,考虑两个制造商的竞争关系和权力结构,研究不同竞争模式对产品定价、产品利润的影响,为技术服务商和新能源汽车制造商的决策提供参考。结果显示,技术服务商在同等条件下会提供技术给具有竞争关系的制造商以优化利润;制造商之间的权力结构会影响技术服务商的利润,并且在“权力对等”竞争模式(FLC)下的利润最大。从制造商角度,竞争强度和技术产出贡献度均会影响产品价格。在技术产出贡献度较小的情形下,垄断模式(M)下的产品定价最高,“制造商1领导”竞争模式(SLC)和“制造商2领导”竞争模式(TLC)下的定价较高,FLC模式下的定价最低;反之亦然。算例分析进一步指出,当技术产出贡献度较小时,M模式下的产品利润大于竞争模式(C)下的产品利润;反之亦然。此外,制造商之间的权力结构会影响C模式下的产品利润,因此,制造商在C模式的占优策略是通过FLC模式实施定价或在SLC和TLC模式下取得定价领导权。     

佛山市禅城区机动车排放污染分担率研究

在深入调查佛山市禅城区固定源污染状况以及机动车保有量、道路建设状况的基础上,对2002年机动车污染物排放总量分担率、不同车型污染物排放量分担率和道路污染物浓度分担率进行了全面研究,分析量化机动车尾气排放对大气环境污染的影响。     

The research of controller area network on hybrid electrical vehicle

It is of increasing importance to design and implement vehicle networks for transferring information between electrical control units on Hybrid Electrical Vehicle （HEV）. This paper presents a scheme of using Controller Area Network （CAN） technology to realize communication and datasharing between the electrical units on the HEV. The principle and communication protocol of Electrical Control Units （ECU） CAN node are introduced. By considering different sensitivity of the devices to the latency of data transportation, a new design procedure is proposed for the purpose of simplifying network codes and wiring harness, reducing assembly space and weight, improving assembly efficiency, and enhancing fault-diagnose in auto networks.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Making the abstract concrete: The role of norms and values in experimental modeling

Experimental modeling is the construction of theoretical models hand in hand with experimental activity. As explained in Section 1, experimental modeling starts with claims about phenomena that use abstract concepts, concepts whose conditions of realization are not yet specified; and it ends with a concrete model of the phenomenon, a model that can be tested against data. This paper argues that this process from abstract concepts to concrete models involves judgments of relevance, which are irreducibly normative. In Section 2, we show, on the basis of several case studies, how these judgments contribute to the determination of the conditions of realization of the abstract concepts and, at the same time, of the quantities that characterize the phenomenon under study. Then, in Section 3, we compare this view on modeling with other approaches that also have acknowledged the role of relevance judgments in science. To conclude, in Section 4, we discuss the possibility of a plurality of relevance judgments and introduce a distinction between locally and generally relevant factors.     

Exploring Survey‐Based Inflation Forecasts

This paper first shows that survey‐based expectations (SBE) outperform standard time series models in US quarterly inflation out‐of‐sample prediction and that the term structure of survey‐based inflation forecasts has predictive power over the path of future inflation changes. It then proposes some empirical explanations for the forecasting success of survey‐based inflation expectations. We show that SBE pool a large amount of heterogeneous information on inflation expectations and react more flexibly and accurately to macro conditions both contemporaneously and dynamically. We illustrate the flexibility of SBE forecasts in the context of the 2008 financial crisis. Copyright © 2011 John Wiley & Sons, Ltd.