On Model Selection Curves

Many popular methods of model selection involve minimizing a penalized function of the data (such as the maximized log-likelihood or the residual sum of squares) over a set of models. The penalty in the criterion function is controlled by a penalty multiplier λ which determines the properties of the procedure. In this paper, we first review model selection criteria of the simple form “Loss + Penalty” and then propose studying such model selection criteria as functions of the penalty multiplier. This approach can be interpreted as exploring the stability of model selection criteria through what we call model selection curves. It leads to new insights into model selection and new proposals on how to select models. We use the bootstrap to enhance the basic model selection curve and develop convenient numerical and graphical summaries of the results. The methodology is illustrated on two data sets and supported by a small simulation. We show that the new methodology can outperform methods such as AIC and BIC which correspond to single points on a model selection curve.     

European response to issues in recycling car plastics

This paper discusses the issue of recycling of plastics in the automobile industry which has gained importance due to the proposed European Commission regulation on End-of-Life Vehicles (ELVs) where the EC sets targets on the percent recyclablity or reusability of a car by the year 2015. This proposed regulation puts pressure on the car manufacturers to increase the recyclable and/or reusable components of their product. Plastic poses a critical challenge as on one hand it is necessary to meet the customer demands related to esthetics, light weight and the technological advantages, while on the other hand it is a hurdle in achieving a higher percent recyclability of the ELVs.A closer look on this issue from Europe is necessary as it is expected to set the trend in car recycling regulations all over the world. However, there are many related economic issues that have to be kept in mind while thinking of recycling of plastics (or other components) from ELVs. Tough regulations may not have the solution to the environmental question as the issue has ramifications outside the automotive industry and outside Europe.The significance of plastics in the automotive industry, the proposed ELV directive from the EC and the economic effects of the same, along with the future concerns is discussed here. Further, the paper takes a brief look at the environment in the Indian sub-continent which is considered an emerging market and is flooded with car manufacturers from all over the world, and where issues like recycling are still to attract the attention of the government and the local population.     

基于混合微粒群算法的带时间窗的车辆路径问题研究

本文在基本微粒群算法（PSO）的位置更新中引入了模拟退火算法思想，并改进了模拟退火算法（SA）中的降温操作该算法结合了基本PSO的快速寻优能力和SA的慨率突跳性，避免了基本PSO易于陷入局部最优的缺点，提高了进化后1期算法的收敛精度.把该算法用于解决有时间窗的车辆路径问题（VRHTW），它可以有效地求得有时间窗车辆路径问题的优化解。     

基于灰色综合评价模型的供应商评估策略研究

针对有分层、具有定性指标、信息不完全且没有符合典型的概率分布的指标，基于层次分析法和灰色关联分析，提出了进行供应商评价的灰色综合模型。最后进行了实证分析，表明该模型是可行的，为解决供应商评价工作提供了一种全新的思路。     

Efficient estimation and computation for the generalised additive models with unknown link function

The generalised additive models (GAM) are widely used in data analysis. In the application of the GAM, the link function involved is usually assumed to be a commonly used one without justification. Motivated by a real data example with binary response where the commonly used link function does not work, we propose a generalised additive models with unknown link function (GAMUL) for various types of data, including binary, continuous and ordinal. The proposed estimators are proved to be consistent and asymptotically normal. Semiparametric efficiency of the estimators is demonstrated in terms of their linear functionals. In addition, an iterative algorithm, where all estimators can be expressed explicitly as a linear function of $Y$, is proposed to overcome the computational hurdle for the GAM type model. Extensive simulation studies conducted in this paper show the proposed estimation procedure works very well. The proposed GAMUL are finally used to analyze a real dataset about loan repayment in China, which leads to some interesting findings.     

Factors affecting post-implementation success of enterprise resource planning systems: a perspective of business process performance

ABSTRACT

The study investigates critical factors which are important to evaluate enterprise resource planning (ERP) in the post-implementation stage. A conceptual framework is proposed with a set of relevant hypotheses and a structural equation modeling is used to analyze the survey data using Smart-PLS package program. The results illustrate that post-implementation success factors are significant for assessing an overall impact of ERP post-implementation. Likewise, the possibility of business process performance is higher in a condition of when the systems are employed in a coordinated way. The findings may assist ERP professionals and developers in other countries for ERP implementation in future.     

Enterprise resource planning (ERP) implementation using the value engineering methodology and Six Sigma tools

Enterprise resource planning (ERP) is a software solution that integrates the operational processes of the business functions of an enterprise. However, implementing ERP systems is a complex process. In addition to the technical issues, companies must address problems associated with business process re-engineering, time and budget control, and organisational change. Numerous industrial studies have shown that the failure rate of ERP implementation is high, even for well-designed systems. Thus, ERP projects typically require a clear methodology to support the project execution and effectiveness. In this study, we propose a theoretical model for ERP implementation. The value engineering (VE) method forms the basis of the proposed framework, which integrates Six Sigma tools. The proposed framework encompasses five phases: knowledge generation, analysis, creation, development and execution. In the VE method, potential ERP problems related to software, hardware, consultation and organisation are analysed in a group-decision manner and in relation to value, and Six Sigma tools are applied to avoid any project defects. We validate the feasibility of the proposed model by applying it to an international manufacturing enterprise in Taiwan. The results show improvements in customer response time and operational efficiency in terms of work-in-process and turnover of materials. Based on the evidence from the case study, the theoretical framework is discussed together with the study’s limitations and suggestions for future research.     

关系型会计信息系统——投资者关系管理视角下的会计信息系统

投资者关系是资本市场上资金的供给方(投资者)与需求方(企业)之间的关系。会计信息系统是资金需求方在其内部构建的,为价值管理、价值创造提供决策信息的系统。在经济一体化、信息技术飞速发展的今天,在资金流动并增值的整个过程中,投资者关系管理(IRM)与会计信息系统(AIS)密不可分又各有分工,共同完成价值管理与价值创造。将以客户关系管理为主的关系型企业的模式应用于资金流方面,关系型AIS用全新的视角看待投资者关系,并充分地利用现代信息技术进行资金流上的关系管理,以发现、对话、制度为管理框架,实现价值创造的最终目标。     

Cloud-based fleet management for prefabrication transportation

Prefabricated construction heavily relies on the effective transportation of precast components, and efficient prefabrication transportation can benefit the whole construction process from many aspects, such as saving cost, time, and spaces. However, current practices of prefabrication transportation are inefficient due to various involved stakeholders, outdated decision support tools, scarce real-time data, and ineffective information sharing mechanisms. To tackle these problems, and facilitate the management and operations of prefabrication transportation, this paper proposes a cloud-based fleet management platform through integrating the advantages of Internet of Things (iot) and cloud technology. A Transportation Management Service Sharing (TMSS) mechanism is proposed, which enables the platform to be easily and economically used by various transportation service providers, and could benefit all the other stakeholders involved. Besides, relation-based data extraction approach is proposed to extract sufficient transportation data during the whole process, and the transportation data synchronization mechanism is also worked out to ensure the data consistency. Finally, based on a real-life prefabricated construction project for public housing production in Hong Kong, a case study is conducted to verify the effectiveness of the proposed platform.     

A bias in estimating urban population density functions

This paper demonstrates that because of the rules used to delineate census tracts, unweighted estimation of an urban population density function using census tract observations leads to a severe upward bias in the estimated function. A weighted estimation procedure which leads to an unbiased estimate is proposed. The paper also points out that if one computes the integral of an unbiased estimate of a density function over the area of a city, that integral is not necessarily an unbiased estimate of total population. The paper thus explains the “disturbing” empirical results concerning density functions reported by McDonald and Bowman.     

Unbiased estimators of mean, variance and sensitivity level for quantitative characteristics in finite population sampling

This paper considers the problem of procuring reliable information on sensitive quantitative characteristics without exposing respondents’ identities. A generalized optional randomized response procedure is proposed, which enables us to estimate some unknown population parameters unbiasedly. In particular, conditions for the assurance of unbiased estimations of mean, variance and sensitivity level are studied. Efficiency comparisons are also carried out to study the performance of the proposed procedure.     

A supportive architecture for CFD-based design optimisation

Multi-disciplinary design optimisation (MDO) is one of critical methodologies to the implementation of enterprise systems (ES). MDO requiring the analysis of fluid dynamics raises a special challenge due to its extremely intensive computation. The rapid development of computational fluid dynamic (CFD) technique has caused a rise of its applications in various fields. Especially for the exterior designs of vehicles, CFD has become one of the three main design tools comparable to analytical approaches and wind tunnel experiments. CFD-based design optimisation is an effective way to achieve the desired performance under the given constraints. However, due to the complexity of CFD, integrating with CFD analysis in an intelligent optimisation algorithm is not straightforward. It is a challenge to solve a CFD-based design problem, which is usually with high dimensions, and multiple objectives and constraints. It is desirable to have an integrated architecture for CFD-based design optimisation. However, our review on existing works has found that very few researchers have studied on the assistive tools to facilitate CFD-based design optimisation. In the paper, a multi-layer architecture and a general procedure are proposed to integrate different CFD toolsets with intelligent optimisation algorithms, parallel computing technique and other techniques for efficient computation. In the proposed architecture, the integration is performed either at the code level or data level to fully utilise the capabilities of different assistive tools. Two intelligent algorithms are developed and embedded with parallel computing. These algorithms, together with the supportive architecture, lay a solid foundation for various applications of CFD-based design optimisation. To illustrate the effectiveness of the proposed architecture and algorithms, the case studies on aerodynamic shape design of a hypersonic cruising vehicle are provided, and the result has shown that the proposed architecture and developed algorithms have performed successfully and efficiently in dealing with the design optimisation with over 200 design variables.     

Test of Bernoulli success probability in inverse sampling for nearer alternatives using adaptive allocation

This paper provides a test procedure for the problem of testing Bernoulli success probability in the case of costly trials when an inverse sampling is carried out. The proposed test is based on a two population adaptive sampling scheme used in clinical trials. Some exact and asymptotic results related to the test are studied. The proposed procedure is applicable where the alternatives are not too far from the null hypothetical value.     

Forecasting exchange rates using feedforward and recurrent neural networks

In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample mean squared prediction error relative to the random walk model.     

GIS-based fuzzy multi-criteria approach for optimal site selection of fire stations in Istanbul,Turkey

Fire stations play a central role in protection and response activities as part of emergency management services in cases of fire incidences. With the rising urban populations and city expansions, the demand for more fire services resultantly increases. It then becomes critical to effectively plan the location of emergency facilities to adequately service the population and ensure the protection of lives and infrastructure. This study, therefore proposes the use of the fuzzy extension of the Multi-Criteria Decision-Making (MCDM) method of Analytical Hierarchical Process (AHP), hence called fuzzy AHP, integrated with Geographic Information Systems (GIS) approach to optimally site new fire stations for the case of Istanbul region. This proposed fuzzy approach simulates the subjective expert judgements for the preferences of the six criteria assessed for fire station suitability mapping and thereby accounted for the uncertainty of crisp comparison values via triangular fuzzy numbers (TFNs). The criteria weights evaluated from this procedure were used in a weighted overlay analysis of the reclassified criteria map layers in ArcGIS to generate a fire station suitability map. These resultant fuzzy AHP criteria weights were validated using another MCDM technique, called Best-Worst Method (BWM) and found to be comparable and consistent. The criteria that had the strongest influence on the selection of sites for fire stations were identified to be: the density of hazardous material facilities (DHM), a high population density (HPD) and proximity to main roads (PMR) with associated weights of 33.3%, 24.4% and 15.2%, respectively. Based on a thorough assessment within the areas represented by class values ranging from 3 to 5 on the suitability map, a total of 34 new fire station sites were selected complementing the existing 121 fire stations. Further, a prioritization analysis from low, medium to high, was performed to plan the phases for the construction of new fire stations in view of competing budgetary needs and resource constraints. The methodology to achieve this was proposed and modelled for enhancing the decision-making process in urban fire station site selection studies.     

Applying fuzzy quality function deployment (QFD) to identify solutions of service delivery system for port of Kaohsiung

The main purpose of this paper is to apply fuzzy quality function deployment (QFD) model to identify solutions of service delivery system (SDS) for port of Kaohsiung from the viewpoints of customers. At first, to facilitate the main issue of the QFD problem, however, the ‘what’ question of customer needs and ‘how’ problem of the services have to be made, which are two major components and be emphasized on the house of quality (HOQ) matrices. In conjunction with fuzzy sets theory, hence, the systematic procedures using fuzzy QFD were proposed in this paper. Subsequently, a case study for port of Kaohsiung demonstrated the systematic appraisal process for identifying solutions of SDS. The results of empirical study show that (1) 10 key factors are deemed as to have priority to improve the quality of SDS for Kaohsiung port; and (2) eight feasible solutions for improving service quality performance are identified. Moreover, it is suggested that port Authority of Kaohsiung should listen attentively the voice of customers and emphasize on exploiting these customer requirements effectively. And then develop the ‘how’ issues of profiles of solutions, which should continuously strengthen the perspectives of customer, internal business process, and learning and growth, respectively.     

基于价值工程的复合理论评标方法探讨

招投标的关键是评标,采用什么样的评标方法又成为了关键中的关键。本文以价值工程理论为基础,结合层次分析法和模糊综合评价法,提出一种复合理论评标方法,建立了该方法的评标模型,并应用于案例。案例分析表明,基于价值工程复合理论评标方法能使工程评标兼顾工程技术和报价,能全面反映投标商的综合实力,有助于工程建设项目成本降低、功能提高。     

Asymptotic second-order efficiency of three-stage procedure with a warranted confidence level

This paper considers a three-stage procedure which was proposed by Holm (1995) to yield a fixed-width confidence interval of the normal mean with a precise confidence level. The procedure is shown to be asymptotically second-order efficient. The procedure is also applied to such problems as bounded risk and hypothesis testing     

基于粗糙集和支持向量机的商业银行信用风险评估模型

结合粗糙集理论的属性约简和支持向量机(SVM)的分类机理,提出一种数据分类的混合算法;建立了基于此算法的商业银行信用风险评估模型。模型以粗糙集属性约简作为预处理器,删除冗余属性和冲突对象,但不损失有效信息;然后基于SVM进行分类建模和预测。实证表明,创建的模型分类性能良好,降低SVM分类过程的复杂度,一定程度上避免了训练模型的过拟合现象。通过与SVM和神经网络模型的比较,证实该方法用于信用风险评估的有效性。     

“实践”观点与企业审美文化价值本质探析

本文具体分析了关于价值本质的几种学说,包括需要说、属性说、效用说等对于企业审美文化价值本质这一问题的适用性。然而,对于企业审美文化这样一个具体而复杂的客体而言,其价值本质问题不能直接引用任何一种现有说法。从实践、实践结果出发,从企业审美文化的存在、功能和发展出发来理解企业审美文化价值,才能深刻探寻到其本质。