配比试验在灰铸铁熔铸渗硼中的应用

本文用配比试验法研究了灰铸铁在铸型中进行碳化硼涂料渗硼工艺，证明了渗硼层有高的耐锌液腐蚀性能．因此，带有渗硼层的灰铸铁锌锅使用寿命长，可取得显著的经济效益．     

配比试验在灰铸铁铸渗硼中的应用

本文用配比试验法研究了灰铸铁在铸型中进行碳化硼涂料渗硼工艺，证明了渗硼层有高的耐锌液腐蚀性能，因此，带有渗硼层的灰铸铁锌锅使用寿命长，可取得显著的经济效益。     

连通度与强正则图的2—可扩性

本文证明了所有具有偶顶点数的强正则图是1—可扩的,如果强正则图G具有偶顶点数和参数(v,k,α,β),并且G的圈边连通度至少为3k—3测G是2—可扩的.     

装药驱动飞片引爆炸药性能影响参数分析

扩爆装药结构对爆轰波传播、飞片驱动过程以及对主炸药引爆性能有直接的影响。为分析装药结构对飞片威力参数的影响,针对装药直径、飞片厚度、飞片拱起高度等主要结构参数,利用正交实验原理设计了数值实验方案,并采用动力学有限差分程序建立了相应的数值模拟模型。通过对数值实验结果的对比和统计分析,获得了影响飞片速度、动量、比动能等引爆炸药威力指标的主要装药结构参数及其影响规律。其结果可为相关扩爆装药设计提供理论依据。     

竞争不可逆化学反应过程工艺特征数学模型的解析

数学模型可用来研究化工工艺的稳定性和多样性,入口工艺参数的不同组合及出口状态等工艺特征.探索获取指定选择率入口工艺参数的不同组合及范围并证明入口工艺参数组合的多种工艺出口状态(参数).通过数学模型图解的方法能得到出口状态全解,给出完整的工艺稳定体系特征图包括各种出口状态的转变,工艺过程的各种不稳定状态.用数学模型图解详细分析伴有竞争不可逆化学反应的反应精馏工艺多种分离状态并存的原因及其与工艺参数的依存关系,定性讨论分离状态稳定性情况,分析用分离状态极值边界数学模型作工艺设备费用和操作费用之间经济核算的可行性.     

高维仿射空间参数曲线的几个不变量

本文的目的是将仿射平面上有关参数曲线的几个不变量的研究扩广到高维仿射空间去,我们得到如下的 定理 m维仿射空间n(>m>2)次参数曲线一般具有m(n-m)-2个内在仿射不变量。 证 设曲线C_n的参数表示为     

基于无故障概率的多参数多工序工艺系统可靠性评估模型与方法

通过对工艺要素、工艺过程、工艺输出参数、性能参数和产品可靠性等的影响分析,建立了多参数多工序工艺系统可靠性影响关系模型;基于制造质量和生产效率两个维度,提出了多参数工艺系统故障判据与工作能力状态准则;分串联、并联和混联三种模式,建立了多工序工艺系统可靠性逻辑框图;考虑设置检验和不设置检验两种状态,研究了基于无故障概率的多参数多工序工艺系统可靠性评估方法、数学表述和算例.     

随机参数作用下参激双势阱Duffing系统的随机动力学行为分析

基于正交多项式逼近理论,研究了在不同随机参数作用下参激双势阱Duffing系统的随机动力学行为.首先,借助Poincaré(庞加莱)截面分析系统的复杂动力学行为;其次,分别针对系统非线性项系数和阻尼项系数为随机参数的情况,运用正交多项式逼近法,将随机参数Duffing系统转化为与之等价的确定性扩阶系统,并证明其有效性;最后,运用等价确定性扩阶系统的集合平均响应,揭示随机系统的动力学特性,以及随机变量强度变化对系统产生的影响.数值结果表明,对于多吸引子共存情形,参激双势阱Duffing系统在随机非线性项系数影响下,其动力学行为较为稳定,共存吸引子与确定性情形保持一致;而当阻尼系数为随机参数时,随着随机变量强度的增加,部分共存吸引子将发生分岔现象.     

水湿储层水驱油微观驱替特征的网络模拟研究

应用逾渗理论,基于计算机随机建模方法,建立了水湿储层三维网络模型.模型的孔隙喉道半径采用截断式威布尔分布随机产生,通过与实际岩心相渗曲线的拟合,验证了模型的有效性.计算了形状因子、孔隙度、导流率等参数,为模拟过程提供基础数据.应用建立的网络模型,模拟了饱和油和水驱油两个驱替过程,分析了孔隙特性参数对相对渗透率及驱替效率的影响.结果表明:随着孔喉比的降低、配位数的增加、形状因子的增加(在一定范围内),水相渗透率降低,油相渗透率升高,残余油饱和度降低,驱替效率增大.与其他理想模型相比,模型可以更真实地研究油水两相流动特征.     

利用回归分析对正交试验结果进行修正

本文以纱线上浆率工艺参数正交试验为实例 ,利用回归分析的方法加以验证和修正 ,使工艺参数更符合实际情况 ,说明两种方法有较好的互补性 .     

Modelling the independent insurance agent's product evaluation strategy: An application of conjoint analysis

A major property-casualty insurance company had streamlined underwriting procedures and hoped to design a new commercial insurance package which would appeal to its independent agents. They hoped to do this by improving service times, premium and/or commission, but making these improvements would require the agents to fill out a new underwriting form. The research used conjoint analysis to determine for management the optimum levels of each of the factors to be employed in the new programme and the possible negative impact of the new underwriting form. Also discussed are issues relating to the use of conjoint analysis; in particular, the handling of large factorial designs and the aggregation of individual results.     

应用联合分析和混合回归模型进行市场细分

联合分析是一种有效地反映消费者需求差异的方法,所以被广泛地应用到市场细分的研究中。但是,传统的方法存在着一定的不足。本研究提出了一个残差分布假设不同的混合回归模型,模型估计的效率比较高,而且模型系数也必较可靠。所以不失为一个比较理想的市场细分分析工具。本文应用该模型方法对一个笔记本电脑联合分析案例进行了实证分析。     

结合分析的原理和应用

沈浩，柯惠新．结合分析的原理和应用．数理统计与管理，１９９８，１７（４），３９～４５．结合分析用来分析消费者对产品／服务的偏好。本文通过对采用部分因子设计估计主效应的全轮廓法以及用一般最小二乘法（ＯＬＳ）回归计算属性水平的效用分析，介绍了这种结合分析法的概念、步骤和应用     

A web-based methodology for product design evaluation and optimisation

The authors have developed a methodology that takes advantages of the World Wide Web to analyse and develop optimal new product designs. This paper describes the methodology and illustrates its application to a case study involving the design of an actual Web site where music CDs are sold. The proposed methodology has the following features: (a) it is based on a design inspired by conjoint analysis; (b) it involves unobtrusive electronic measurement of the actual behavior of Web users who remain undisturbed by experimental factors; and (c) it utilises an integer programming approach to seek optimal Web site configurations. The methodology uses limited dependent variable methods to develop response models that provide the basis for the development of objective functions for an optimisation model. The optimisation model can consider either single or multiple objective functions by using a Pareto optimum approach.     

Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research

Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity. Many recent advances in this area have raised questions about the most appropriate form of conjoint analysis for this research. We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models.     

Real world performance of choice-based conjoint models

Conjoint analysis is one of the most important tools to support product development, pricing and positioning decisions in management practice. For this purpose various models have been developed. It is widely accepted that models that take consumer heterogeneity into account, outperform aggregate models in terms of hold-out tasks. The aim of our study is to investigate empirically whether predictions of choice-based conjoint models which incorporate heterogeneity can successfully be generalized to a whole market. To date no studies exist that examine the real world performance of choice-based conjoint models by use of aggregate scanner panel data. Our analysis is based on four commercial choice-based conjoint pricing studies including a total of 43 stock keeping units (SKU) and the corresponding weekly scanning data for approximately two years. An aggregate model serves as a benchmark for the performance of two models that take heterogeneity into account, hierarchical Bayes (HB) and latent class (LC). Our empirical analysis demonstrates that, in contrast to the performance using hold-out tasks, the real world performance of HB and LC is similar to the performance of the aggregate model. Our results indicate that heterogeneity cannot be generalized to a whole market and suggest that aggregate models are sufficient to predict market shares (MSs).     

Following the traces:: An introduction to conjoint measurement without transitivity and additivity

This paper presents a self-contained introduction to a general conjoint measurement framework for the analysis of nontransitive and/or incomplete binary relations on product sets. It is based on the use of several kinds of marginal traces on coordinates induced by the binary relation.This framework leads to defining three general families of models depending on the kind of trace that they use. Contrary to most conjoint measurement models, these models do not involve an addition operation. This allows for a simple axiomatic analysis at the cost of very weak uniqueness results.     

Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines

Support vector machines (SVMs) have been successfully used to identify individuals’ preferences in conjoint analysis. One of the challenges of using SVMs in this context is to properly control for preference heterogeneity among individuals to construct robust partworths. In this work, we present a new technique that obtains all individual utility functions simultaneously in a single optimization problem based on three objectives: complexity reduction, model fit, and heterogeneity control. While complexity reduction and model fit are dealt using SVMs, heterogeneity is controlled by shrinking the individual-level partworths toward a population mean. The proposed approach is further extended to kernel-based machines, conferring flexibility to the model by allowing nonlinear utility functions. Experiments on simulated and real-world datasets show that the proposed approach in its linear form outperforms existing methods for choice-based conjoint analysis.     

A Strategic Business Decision with Multiple Criteria: The Bally Men's Shoe Problem

In 1976, Bally (U.K.) Ltd were considering how to counter the long term decline of their men's shoe business. It was first necessary to identify the set of potential strategies and the criteria by which they were to be evaluated. Simulation programmes were developed to model the policy options, and conjoint analysis experiments were designed to elicit multi-attributed preference structures. These were then applied to the simulation output to identify the optimal policy. It was found that decision makers were unwilling to participate in the conjoint measurement ranking exercises. Time preferences inconsistent with standard discounting were identified. The paper also describes the actual events and decisions subsequent to the analysis.     

Behavioral and procedural consequences of structural variation in value trees

Our experiment shows that the division of attributes in value trees can either increase or decrease the weight of an attribute. The structural variation of value trees may also change the rank of attributes. We propose that our new findings related to the splitting bias, some other phenomena appearing with attribute weighting in value trees, and the number-of-attribute-levels effect in conjoint analysis may have the same origins. One origin for these phenomena is that decision makers' responses mainly reflect the rank of attributes and not to the full extent the strength of their preferences as the value theory assumes. We call this the unadjustment phenomenon. A procedural source of biases is the normalization of attribute weights. One consequence of these two factors is that attribute weights change if attributes are divided in a value tree. We also discuss how the biases in attribute weighting could be avoided in practice.