MC环境下设计维度纵深式模块化产品族设计方法研究

从探讨定制产品生产过程的机理出发,提出了包含生产时间维、定制层次维的“定制产品山峰制造模型”,提出了“设计维度纵深式模块化产品族设计方法”,建立了产品系列矩阵、基型模块矩阵、基型模块n级子模块矩阵,结合数据库建立了该设计方法的设计流程,给出了相应算法,最后通过实例验证了该方法的可行性与有效性。     

基于制造定制的产品工时定额快速计算方法

为了保证制造定制生产方式下能够有效、快速、精确的确定产品的工时定额,提出了一种快速制定工时定额的方法.该方法通过成组技术和模块化思想对定制产品进行模块划分;并综合考虑了实际操作和知识经验对制定时间定额的因影响作用,在时间模块的基础上建立作业模块和补偿模块;采用不同的技术手段制定其时间定额,再构建时间模块数学模型;通过时间模块的快速组合计算产品的工时定额.最后,通过实例证明了其可行性及有效性.该方法可以拓展到设计定制、装配定制等多种定制形式下的工时定额的高效制定,具有较强的通用性.     

加工时间离散可控作业车间调度问题分解方法

给出了加工时间离散可控的作业车间调度问题（job-shop scheduling problem with discretely controllable processing times,JSP-DCPT）基于析取图的模型及其数学描述.提出一种三步分解方法,使得JSP-DCPT可以通过求解一个作业车间调度问题（job-shop scheduling problem,JSP）以及一系列离散时间-成本权衡问题得到解决.为简化分解方法,引入时间-成本相平面描述离散时间-成本权衡问题的权衡点,并详细阐释了一种基于极限模式的集合支配理论以确定用于消减JSP-DCPT分解所得离散时间-成本权衡问题的一个上界.随后提出一种基于极限模式的集合支配分解算法EMSDDA（extreme mode-based set dominant decomposition algorithm）.基于JSP标准算例FT10构造了测试算例JSPDCPT_FT10,实验仿真验证了提出的理论及分解方法的有效性.     

离子束修正光学镜面误差中拼接加工方法研究

由于离子束加工机床工作运动空间的限制, 为了加工大型光学镜面, 本文提出了一种全新光学镜面加工方法——拼接加工方法. 论文首先从理论上分析解决了拼接加工工艺的系列关键技术问题, 如： 面形控制模型、拼接加工驻留时间解算算法、加工定位参数辨识与补偿等. 基于CCOS成形原理, 通过分析拼接加工面形控制机制, 建立了光学镜面拼接加工有限域叠加的非线性面形控制模型; 依据拼接加工有限域非线性问题特征, 提出了基于Bayesian原理的改进型SRL迭代法较好地解决了拼接加工的驻留时间求解问题; 通过分析拼接加工中对刀误差和材料去除率对加工精度和加工面形的影响分析, 提出了一种光学镜面离子束定位误差、去除率等工艺参数辨识算法. 通过上述研究, 首次建立了光学镜面离子束拼接加工基本加工理论、方法和工艺流程. 拼接加工工艺实验表明： 误差补偿后的加工收敛率可达10. 本文提出的理论和方法通过有效地解决拼接加工的关键技术问题使拼接加工与全口径加工一样, 能够实现对镜面的精确修形. 与此同时大大节约了加工系统制造和加工成本.     

一种基于加工匹配度的车间作业调度方法研究

单件小批量生产形式下的作业车间调度是生产调度中的重要问题.在综合考虑订单优先级、加工时间等诸多因素的基础上,提出了加工匹配度的概念来研究作业调度问题,建立了相应的调度模型,设计了一种启发式作业调度算法.在某制动阀生产企业数控车间的实际应用结果表明,该方法能够较好的辅助车间调度人员进行车间作业调度.     

细水雾作用下固体池火熄灭时间的实验研究

利用三维激光粒子动态分析仪测量距离细水雾喷嘴不同位置的雾场特性(雾滴速度、雾滴粒径), 选择东北红松和PMMA作为固体燃料研究了细水雾与固体池火的相互作用过程, 重点考察燃料特性、细水雾施加流量以及喷嘴距离燃料试样表面的高度对熄灭时间的影响. 结果表明, 随着细水雾流量的增加, 灭火时间均下降, 在同一个流量条件下PMMA的灭火时间比松木长. 在小流量的情况下, 喷嘴离燃烧试样表面越远, 灭火时间越短. 固体池火的熄灭主要由于燃料表面的冷却.     

人工协调场及其在动态不确定环境下机器人运动规划中的应用

针对动态不确定环境下移动机器人运动规划问题, 提出了人工协调场的方法. 建立了协调力, 并设计了协调场能随机器人和环境的状态发生变化, 以克服传统人工势场法在动态不确定环境下所存在的诸如局部极小、抖动等问题. 和传统人工势场相比, 协调场具有二维正交力矢量. 基于协调因子的实时决策模型, 方便并简化了机器人的行为设计, 有效地克服了局部极小. 重点分析了人工协调场的可控性、自适应性、安全性和可达性等性质及其设计, 并实现了移动机器人在动态不确定环境下基于人工协调场的运动规划. 理论分析和仿真实验结果证明了所提方法的有效性.     

厂商在市场需求及预测不确定下的多期定价研究

建立多阶段线性需求-价格模型,运用动态规划思想,针对耐用品市场需求量不确定及耐用品生产厂商市场信息预测不准的问题展开探讨.得到了如果消费者对耐用品价格的预期与耐用品生产厂商对市场的预期不一致,耐用品需求量的波动及厂商掌握信息量的多少将对耐用品生产厂商的最优定价具有影响等结论.并根据现实市场状况,对信息不对称下的耐用品定价模型的经济含艾给予分析,结果表明模型对耐用品生产厂商的市场决策具有理论指导意义.     

企业在市场需求及预测不确定下的多期定价研究

建立多阶段线性需求一价格模型,运用动态规划思想,针对耐用品市场需求量不确定及耐用品生产厂商市场信息预测不准的问题展开探讨。得到了如果消费者对耐用品价格的预期与耐用品生产厂商对市场的预期不一致,耐用品需求量的波动及厂商掌握信息量的多少将对耐用品生产厂商的最优定价具有影响等结论。并根据现实市场状况,对信息不对称下的耐用品定价模型的经济含义给予分析,结果表明模型对耐用品生产厂商的市场决策具有理论指导意义。     

基于DSM的产品协同开发流程优化方法研究

针对传统的按职能分工部门之间的壁垒造成产品开发过程中的信息传输迟滞,以及流程活动冗余造成的流程运行时间长的问题,从时间的维度进行产品开发活动的协同管理,建立流程活动间信息交互模型,以实现流程活动间信息传递的量化和无量纲化。根据流程活动间信息依赖关系,建立基于设计结构矩阵（Design Structure Matrix,DSM）的产品协同开发流程结构矩阵,用模糊聚类方法对流程进行优化,并建立了流程优化算法,通过实例验证该方法的可行性。     

Backward‐in‐Time Selection of the Order of Dynamic Regression Prediction Model

We investigate the optimal structure of dynamic regression models used in multivariate time series prediction and propose a scheme to form the lagged variable structure called Backward‐in‐Time Selection (BTS), which takes into account feedback and multicollinearity, often present in multivariate time series. We compare BTS to other known methods, also in conjunction with regularization techniques used for the estimation of model parameters, namely principal components, partial least squares and ridge regression estimation. The predictive efficiency of the different models is assessed by means of Monte Carlo simulations for different settings of feedback and multicollinearity. The results show that BTS has consistently good prediction performance, while other popular methods have varying and often inferior performance. The prediction performance of BTS was also found the best when tested on human electroencephalograms of an epileptic seizure, and for the prediction of returns of indices of world financial markets.Copyright © 2013 John Wiley & Sons, Ltd.     

The Effects of Disaggregation on Forecasting Nonstationary Time Series

This paper focuses on the effects of disaggregation on forecast accuracy for nonstationary time series using dynamic factor models. We compare the forecasts obtained directly from the aggregated series based on its univariate model with the aggregation of the forecasts obtained for each component of the aggregate. Within this framework (first obtain the forecasts for the component series and then aggregate the forecasts), we try two different approaches: (i) generate forecasts from the multivariate dynamic factor model and (ii) generate the forecasts from univariate models for each component of the aggregate. In this regard, we provide analytical conditions for the equality of forecasts. The results are applied to quarterly gross domestic product (GDP) data of several European countries of the euro area and to their aggregated GDP. This will be compared to the prediction obtained directly from modeling and forecasting the aggregate GDP of these European countries. In particular, we would like to check whether long‐run relationships between the levels of the components are useful for improving the forecasting accuracy of the aggregate growth rate. We will make forecasts at the country level and then pool them to obtain the forecast of the aggregate. The empirical analysis suggests that forecasts built by aggregating the country‐specific models are more accurate than forecasts constructed using the aggregated data. Copyright © 2014 John Wiley & Sons, Ltd.     

Real‐Time Forecasts of Inflation: The Role of Financial Variables

We present a mixed‐frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed‐frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Copyright © 2012 John Wiley & Sons, Ltd.     

Detecting and Predicting Economic Accelerations,Recessions, and Normal Growth Periods in Real‐Time

The dichotomous characterization of the business cycle in recessions and expansions has been central in the literature over the last 50 years. However, there are various reasons to question the adequacy of this dichotomous, recession/expansion approach for our understanding of the business cycle dynamics, as well as for the prediction of future business cycle developments. In this context, the contribution of this paper to the literature is twofold. First, since a positive rate of growth at the level of economic activity can be considered as the normal scenario in modern economies due to both population and technological growth, it proposes a new non‐parametric algorithm for the detection and dating of economic acceleration periods, trend or normal growth periods, and economic recessions. Second, it uses an ordered probit framework for the estimation and forecasting of these three business cycle phases, applying an automatized model selection approach using monthly macroeconomic and financial data on the German economy. The empirical results show that this approach has superior out‐of‐sample properties under real‐time conditions compared to alternative probit models specified individually for the prediction of recessions and/or economic accelerations. Copyright © 2016 John Wiley & Sons, Ltd.     

基于小波的钦州水稻产量多时间尺度分析与预测

利用小波诊断技术对广西钦州1950～2005年水稻产量和播种面积进行了多时间尺度分析,并利用基于小波的ARIMA模型进行了预测。分析结果表明：56年来,钦州水稻总产量和单位产量波动具有明显的3a、7a和25a特征时间尺度,播种面积7a特征时间尺度主要受农村土地制度改革影响。基于小波的ARIMA模型在水稻产量、播种面积预测方面精度很高,预测误差与气象灾害和土地政策变化有关。利用基于小波的水稻产量多时间尺度分析与预测方法,可以辅助水稻产量增减周期的分析以及对未来趋势的判断,对于结合供求关系合理调整种植面积,促进农业可持续发展提供帮助。     

Introducing the Euro Area‐wide Leading Indicator (ALI): Real‐Time Signals of Turning Points in the Growth Cycle from 2007 to 2011

This paper introduces a new monthly euro Area‐wide Leading Indicator (ALI) for the euro area growth cycle which is composed of nine leading series and derived from a one‐sided bandpass filter. The main findings are that (i) the GDP growth cycle in the euro area can be well tracked, in a timely manner and at monthly frequency, by a reference growth cycle indicator (GCI) derived from industrial production excluding construction, (ii) the ALI reliably leads turning points in the GCI by 5 months and (iii) longer leading components of the ALI are good predictors of the GCI up to 9 months ahead. A real‐time case study on the ALI's capabilities for signalling turning points in the euro area growth cycle from 2007 to 2011 confirms these findings. Copyright © 2013 John Wiley & Sons, Ltd.