物流配送系统中的运筹学问题及方法研究

物流配送系统中包含诸多运筹学问题,如配送中心选址中的0-1规划问题,货物配载中的背包问题,配送路线优化中的动态规划问题等,通过建立数学模型,利用运筹学方法与信息技术的结合可对上述问题加以解决,为配送业务的进一步发展提供帮助.     

Simpler Probabilistic Population Forecasts: Making Scenarios Work

The traditional high-low-medium scenario approach to quantifying uncertainty in population forecasts has been criticized as lacking probabilistic meaning and consistency. This paper shows, under certain assumptions, how appropriately calibrated scenarios can be used to approximate the uncertainty intervals on future population size and age structure obtained with fully stochastic forecasts. As many forecasting organizations already produce scenarios and because dealing with them is familiar territory, the methods presented here offer an attractive intermediate position between probabilistically inconsistent scenario analysis and fully stochastic forecasts.     

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。     

Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models

Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings.     

Linear Programming Models for the Measurement of Environmental Performance of Firms—Concepts and Empirical Results

I use linear programming models to define standardised, aggregate environmental performance indicators for firms. The best practice frontier obtained corresponds to decision making units showing the best environmental behaviour. Results are obtained with data from U.S. fossil fuel-fired electric utilities, starting from four alternative models, among which are three linear programming models that differ in the way they account for undesirable outputs (pollutants) and resources used as inputs. The results indicate important discrepancies in the rankings obtained by the four models. Rather than contradictory, these results are interpreted as giving different, complementary kinds of information, that should all be taken into account by public decision-makers.     

灰色理论在城市总体规划中的应用研究

在编制城市总体规划时收集的资料和信息往往是不完全和不对称的，很多数据需要通过科学的理论去建立合理的模型来推算，本文采用灰色理论建模，以GDP预测为例详细说明了灰色预测模型GM（1，1）的建模过程。该模型对于预测城市总体规划中水文、人口、用地规模、城市化水平、交通运输发展、公共交通以及客货运量等具有普遍指导意义。     

低压供电线路电压损耗的简便校验

为了确保低压供电线路的供电质量,本文就低压配电线路未端,包括地埋、架空户内干线、支线的电压校验问题,提供简单易行的估算方法,对低压供电线路电压质量的校验通过简单四则运算,判别电压质量是否符合要求.     

Killing off cohorts: Forecasting mortality of non-extinct cohorts with the penalized composite link model

Mortality forecasting has crucial implications for insurance and pension policies. A large amount of literature has proposed models to forecast mortality using cross-sectional (period) data instead of longitudinal (cohort) data. As a consequence, decisions are generally based on period life tables and summary measures such as period life expectancy, which reflect hypothetical mortality rather than the mortality actually experienced by a cohort. This study introduces a novel method to forecast cohort mortality and the cohort life expectancy of non-extinct cohorts. The intent is to complete the mortality profile of cohorts born up to 1960. The proposed method is based on the penalized composite link model for ungrouping data. The performance of the method is investigated using cohort mortality data retrieved from the Human Mortality Database for England & Wales, Sweden, and Switzerland for male and female populations.     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

Threshold models for trended time series

This paper presents the theoretical development of a new threshold autoregressive model based on trended time series. The theoretical arguments underlying the model are outlined and a nonlinear economic model is used to derive the specification of the empirical econometric model. Estimation and testing issues are considered and analysed. Additionally we apply the model to the empirical investigation of U.S. GDP.This paper is the result of work carried out for the author's Ph.D. thesis. I would like to thank Hashem Pesaran for his help, encouragement and insights during the preparation of this paper. I also thank Gary Koop and Sean Holly for helpful comments. Financial assistance from the Economic and Social Research Council is gratefully acknowledged.