Modeling the day-of-the-week shopping activity and travel patterns

This research develops models that extend existing multi-day analyses of shopping activity patterns. The main hypothesis is that shopping activity choice behavior varies, depending on the day of the week. Using a nested logit model structure, this hypothesis is tested by developing models of daily activity choice behavior for each day of the week. In doing so, household activity choice behavior is represented by three stages, with a shopping activity participation decision as the highest stage, the scheduling decision as the next lower stage, and the number of shopping stops as the lowest stage. Empirical results confirm the nested logit model structure hypothesized in this study. Furthermore, it is found that major distinctions in the daily utility functions take place between the weekday and weekend models. However, differences among weekday models are also observed. This indicates that there appear to be behavioral differences even among weekdays although households are exposed to the same set of temporal constraints.     

Choice Research: A Wealth of Perspectives

This article is a navigation guide to the twenty papers that emerged from the workshops conducted at the Sixth Invitational Choice Symposium (Estes Park, Colorado, June 2004). The papers are categorized into three broad sets that focus respectively on choice modeling issues, on providing interdisciplinary perspectives on choice, and on relating choice research to real world concerns. Within each category, we offer provide brief integrative overviews linking subsets of papers.     

韩国政府为实现环保型企业经营的政策及方案

文章论述了环境问题对人类的重要性及创造环保型的企业环境需要解决的问题,在此基础上,提 出了为实现环保型企业经营韩国政府所应该采用的政策及具体的实施方案。     

Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures

A feature transformation method based on domain knowledge for arti?cial neural networks (ANNs) is proposed. The method of feature transformation based on domain knowledge converts continuous values into discrete values in accordance with the knowledge of experts in speci?c application domains. This approach effectively ?lters data, trains the classi?er, and extracts the rules from the classi?er. In addition, it reduces the dimensionality of the feature space, which not only decreases the cost and time in the operation but also enhances the generalizability of the classi?er. The experimental results of the proposed approach will be compared and tested statistically with the results of the linear transformation method. The results show that the method of feature transformation based on domain knowledge outperforms the linear transformation in modelling of ANNs. Copyright © 2004 John Wiley & Sons, Ltd.     

物流配送实时监管系统设计

随着近年流通领域的发展,尤其是电子商务对现代物流的要求,物流配送需要现代化的信息技术已越来越成为人们的共识,如何对物流配送系统和配送中心进行现代化建设,已成为不少物流配送公司的重要课题。针对目前物流配送中“不可见部分”和过程无法控制的现状,论文设计了一个物流配送实时监管系统来满足物流配送企业和客户的需求。     

《管子》"人心管理"思想的科学人才观

管子的管理思想博大精深,"心的管理"是其管理思想的核心。从《管子》"人心管理"的思想出发,探讨其蕴涵的关于人和人才的深刻认识,这对现代企业科学管理仍然具有积极的现实意义。     

Preliminary test estimators of the parameters of simple linear model with measurement error

In this paper, we discuss the properties of preliminary test estimators (PTE) of the parameters of simple linear model with measurement error (ME model) when the slope of the linear model is suspected to be zero. Expressions of the bias, MSE and efficiencies are obtained under conditional as well as unconditional situations with known reliability coefficient. Conditional model results are compared to the standard model without measurement error. We also provide the unconditional model analysis in finite samples. Asymptotic theory under local alternatives is developed when the variance of measurement error or the ratio of the variance of the model error relative to the variance of the measurement error is known. Asymptotic expressions of bias and MSE of the estimators along with their efficiencies are obtained. In every case, it is shown that the measurement error tend to increase the variability of the estimators compared to the estimators without measurement error. Graphs and tables are provided to see these results and to determine optimum level of significance for minimum guaranteed efficiency. Received October 2001 RID="*" ID="*"  A. K. Md. E. Saleh is a Distinguished Research Professor and H. M. Kim is a Ph.D. candidate in the School of Mathematics and Statistics, Carleton University, Ottawa. Acknowledgment. The authors gratefully acknowledge the constructive suggestion of the referees to improve the paper. The research is supported by NSERC grant A3088.     

报废汽车回收体系结构模型研究

报废汽车的回收重用一方面是＂生产者责任延伸制＂在汽车行业贯彻实施的结果,另一方面来源于再制造环节中蕴藏的丰厚利润的诱导.目前我国回收利用报废汽车的方式比较落后,主要是对原材料进行重用.本文构建了一个由汽车制造商牵头纽建,以功能多样化的回收中心作为中心处理单元的报废汽车回收体系结构模型,以期实现更高层次的回收重用.     

Product attributes and models of multiple discreteness

Demand for product characteristics is examined within the context of models that allow for both corner and interior solutions corresponding to zero and non-zero demand. Product attribute information is associated with marginal utility and curvature (satiation) parameters of various utility functions. Empirical applications demonstrate the need for incorporating characteristics in a fairly general way. We also compare our approach to an ideal point and pure Lancasterian versions of our nonlinear utility model. The data support our model over either the ideal point or Lancasterian variants.     

The Measurement of Consumption Efficiency Considering the Discrete Choice of Consumers

In this study we propose a mathematical definition of the consumption efficiency of multi-attribute products in the price–quality space. A new model, the discrete Range Adjusted Measure (RAM) model, is suggested as an empirical tool to measure the level of consumption efficiency. We further discuss the effect of consumption efficiency on the innovation incentive. Empirical work is made for the mobile phone market. We expect that the consumption efficiency concept will contribute to the extension of the traditional framework of production efficiency analysis on the one hand and to the understanding of the nature of innovation in a technology-intensive market on the other hand.JEL Classification: C67, D11, D12, D21