整合框架下的产品属性比较机理研究综述

产品属性比较是消费者选择中的一个基本心理过程,学术界对产品属性比较过程的机理进行了诸多的研究,并提出了一系列的效应。基于此,对产品属性比较的过程进行了分解,从信息选择、对比参照和信息推断3个方面构建了产品属性比较的新框架,从而整合了产品属性比较中的效应机理。     

产品改进与增值性服务冲突下制造商的选择策略研究

针对产品改进与增值性服务可能存在冲突的情况,根据消费者效用理论构建了产品和增值性服务的需求函数,研究了制造商进行产品改进还是提供增值性服务的选择策略问题.研究表明,当增值性服务效用较高时,制造商同时进行产品改进和提供增值性服务.当增值性服务效用较低时,制造商有4种选择方式:在产品改进成本系数较低的情况下,制造商选择只进行产品改进;在产品改进成本系数较高并且冲突系数较低的情况下,进行产品改进并同时提供增值性服务;在两者都较高时,只向市场提供改进型产品;但在产品改进成本系数足够大的情况下,制造商会放弃产品改进,只提供增值性服务.     

混合渠道下2-2 可替代品供应链中交叉选择及均衡分析

在不同批发价格策略下探讨产品间可替代程度对供应链交叉选择的影响。选择由两个制造商和两个零售商构成的供应链为研究对象,分别探讨了制造商向零售商提供统一批发价格和实施批发价格歧视时混合渠道中的交叉选择及均衡结果。研究结果表明,当制造商向零售商提供统一批发价格,产品间可替代性低时,交叉选择将会发生。产品间可替代性高时,制造商倾向于采用单一的销售渠道,交叉选择将不会发生;当实施批发价格歧视时,产品间的替代性低时,双向交叉选择将成为均衡的结果;采用分散销售渠道的制造商不会仅选择集成销售渠道中的零售商。最后,用实例分析验证了研究结论。     

基于Internet的竞争厂商产品选择与市场决策模型

本文以差异化理论和博弈论为分析工具,通过建构两竞争厂商的产品选择决策模型来探讨线下和线上厂商之间最优的产品差异化策略和市场选择策略,并给出了厂商差异化产品策略时的跨市场经营诱因和条件。     

网络中选择环境对多样化寻求行为的影响研究

消费者在选择产品时通常会发生多样化寻求行为,而其对信息刺激的需要是消费者产生多样化寻求行为的原因之一。本文通过实验方法研究了在网络购物中,消费者是否会通过平衡从产品选择和选择环境中所获得的这两种刺激来达到其最佳刺激水平。研究结果显示,在网络环境下,通过改变选择环境中消费者同期购买的其他产品类别的构成,增加对受试者的刺激,会减少其在目标产品选择中的多样化寻求行为。     

易腐性产品运输设施选择博弈

易腐性产品的价值会随着时间的流逝而逐渐损失,多个客户可以联合使用某种运输设施时,如何对费用进行公平且稳定的分摊是合作能否进行的基础.把易腐性产品的损失价值和运输费用之和作为总费用,进而将易腐性产品的运输设施选择的费用分配问题构造成运输设施选择合作博弈,证明了在易腐性产品负指数价值损失的情况下,运输设施选择博弈的核心非空,且为凹博弈,并讨论了解的特征.论文还证明具有附加运输费用的运输设施选择博弈的核心非空,分析了核心与线性规划松弛的对偶最优解之间的关系.论文对有约束设施选择博弈进行了分析,并提出了进一步研究的方向.     

面向产品生命周期的部分柔性技术选择

本文研究随机需求下产品生命周期不同阶段的部分柔性技术选择与生产能力规划问题。部分柔性技术是相对于完全柔性技术而言的,一种生产技术的柔性程度定义为能生产一产品类中产品个数多少的能力。不同柔性强度的生产技术,其投资成本和运行成本不同。本文首先建立了以计划期上总成本最小为目标的技术选择和生产能力规划模型,然后根据产品在其生命周期不同阶段的特点与市场需求的特点,应用所建立的模型进行仿真并总结产品导入期和成熟期技术选择的特点。     

面向大规模定制的产品族功能性评价与选择方法

针对大规模定制生产模式下产品多样性和低复杂性的矛盾,系统地研究了一种大规模定制产品族的功能性评价与选择方法。分别以功能域的类功能需求(GFR)和物理域的类设计参数(GDP)对大规模定制产品族进行了描述,并基于公理设计理论建立了GFR和GDP之间的映射关系。提出了产品族功能的定制度的概念和计算方法,建立了基于定制度的定制功能的评价和选择方法。     

网络环境下主要营销渠道选择策略

本文从企业的角度出发,在现有关于渠道问题的研究成果上考虑“产品层次”的影响因素,建立由“产品——市场”三要素所构成的渠道选择决策模型进行渠道选择,为企业合理选择营销渠道提供了系统、实用的理论工具。文章的最后部分对图书市场进行实证分析．结果表明,产品、营销渠道的特点以及当前消费者渠道偏好决定了传统分销方式仍然是最主要的营销渠道,但在某些领域,电子商务渠道也发挥着重要的作用。针对不同层次的产品企业需要采取不同的渠道策略。     

基于三阶段决策视点的ERP软件供应商评价模型研究

从风险控制的角度出发,考虑选择软件供应商应与企业的各项目标需求和资源约束相匹配,提出从产品视点、服务视点和发展视点入手,结合ERP软件产品特点,运用实证分析构建了ERP软件供应商选择风险评价模型。     

A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.     

A linear formulation of the two-level DEA model

In efficiency measurement, the input and output factors of similar characteristics can be grouped into input and output categories, respectively, using a weighted-average approach under the data envelopment analysis (DEA) framework to form a two-level DEA model. The resulting two-level model is nonlinear. This note transforms the nonlinear model into a linear one using a variable substitution technique. Linear models are easier to solve than their nonlinear counterparts. The linear transformation is applicable to the two-level model built from both the primal and dual forms of the conventional one-level DEA model. The linear model transformed from the nonlinear model developed from the primal form has a dual which is equivalent to the nonlinear model developed from the dual form.     

Monotonic Bayesian Semiparametric Benchmark Dose Analysis

Quantitative risk assessment proceeds by first estimating a dose‐response model and then inverting this model to estimate the dose that corresponds to some prespecified level of response. The parametric form of the dose‐response model often plays a large role in determining this dose. Consequently, the choice of the proper model is a major source of uncertainty when estimating such endpoints. While methods exist that attempt to incorporate the uncertainty by forming an estimate based upon all models considered, such methods may fail when the true model is on the edge of the space of models considered and cannot be formed from a weighted sum of constituent models. We propose a semiparametric model for dose‐response data as well as deriving a dose estimate associated with a particular response. In this model formulation, the only restriction on the model form is that it is monotonic. We use this model to estimate the dose‐response curve from a long‐term cancer bioassay, as well as compare this to methods currently used to account for model uncertainty. A small simulation study is conducted showing that the method is superior to model averaging when estimating exposure that arises from a quantal‐linear dose‐response mechanism, and is similar to these methods when investigating nonlinear dose‐response patterns.     

Dose-Response Model for 13 Strains of Salmonella

Data from a human feeding trial with healthy men were used to develop a dose-response model for 13 strains of Salmonella and to determine the effects of strain variation on the shape of the dose-response curve. Dose-response data for individual strains were fit to a three-phase linear model to determine minimum, median, and maximum illness doses, which were used to define Pert distributions in a computer simulation model. Pert distributions for illness dose of individual strains were combined in an Excel spreadsheet using a discrete distribution to model strain prevalence. In addition, a discrete distribution was used to model dose groups and thus create a model that simulated human feeding trials. During simulation of the model with @Risk, an illness dose and a dose consumed were randomly assigned to each consumption event in the simulated feeding trial and if the illness dose was greater than the dose consumed then the model predicted no illness, otherwise the model predicted that an illness would occur. To verify the dose-response model predictions, the original feeding trial was simulated. The dose-response model predicted a median of 69 (range of 43-101) illnesses compared to 74 in the original trial. Thus, its predictions were in agreement with the data used to develop it. However, predictions of the model are only valid for eggnog, healthy men, and the strains and doses of Salmonella used to develop it. When multiple strains of Salmonella were simulated together, the predicted dose-response curves were irregular in shape. Thus, the sigmoid shape of dose-response curves in feeding trials with one strain of Salmonella may not accurately reflect dose response in naturally contaminated food where multiple strains may be present.     

Armitage-Doll Two-Stage Model: Implications and Extension

The objective of this paper is twofold: (1) to provide insight on the simplified MVK model of carcinogenesis (a model proposed by Moolgavkar and colleagues) by revealing the relationship between the Armitage-Doll two-stage model and the simplified MVK model; and (2) to extend the Armitage-Doll two-stage model to one with time-varying (piece-wise constant) parameters. It is shown that the simplified MVK model is a special case of the Armitage-Doll two-stage model, and many applications of the simplified MVK model involving intermittent exposures or exposures to different compounds in different time periods can be derived easily from the Armitage-Doll two-stage model with piece-wise constant parameters. The limitations of the Armitage-Doll two-stage model are discussed.     

Application of the Hyper‐Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes

The hyper‐Poisson distribution can handle both over‐ and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation‐specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper‐Poisson distribution in analyzing motor vehicle crash count data. The hyper‐Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway‐highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness‐of‐fit measures indicated that the hyper‐Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper‐Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway‐Maxwell‐Poisson model previously developed for the same data set. The advantages of the hyper‐Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper‐Poisson model can handle both over‐ and underdispersed crash data. Although not a major issue for the Conway‐Maxwell‐Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model.     

Single and Multiple Period Decision Models for Analysis of Quality and Quantity of Validation*

This paper investigates a single period model for the analysis of the impact of the quality of the validation effort. The single period model uses a Bayesian approach to find that validation is a critical point process. That model is then extended to allow for the uncertainty of the validation process to determine the quality of the underlying model. Some monotonicity results are developed for the model and investigated in light of the process being a critical point process. The model indicates that, consistent with comments from real world settings, the impact of the quality of the validation effort can be substantial. The paper also presents two multiperiod models of the impact of the quantity of the validation effort. In practice, the development of an expert system may follow a recurring multiperiod life cycle, where a prototype is built, the system is validated to determine how well it performs, and based on that performance, is either funded or not funded. The first multiple period model assumes that validation and funding occurs at each point in the PVF budget cycle. The model employs Bayesian revision of probabilities to update the prior probability of obtaining a model with an appropriate level of success. It is found that the critical point for multiperiod problems is different than that for single period problems. This model forms the basis of the second model. The second multiple period model extends the first by assuming that the quantity of validation can be varied. The more validation, the more likely that flaws in the model will be found. Thus, the more validation, the better the understanding of the level of performance of the model.     

An integrated model for slack-based measure of super-efficiency in additive DEA

As a non-radial approach, a super-efficiency model, Super SBM, was proposed by Tone [15] to rank efficient DMUs. Du et al. [7] extends the Super SBM model to the additive (slacks-based) DEA model. To obtain the super-efficiencies of the DMUs, one needs to identify the efficient DMUs first and then apply the additive super-efficiency model to those efficient DMUs. In this paper, we propose an integrated model so that the efficiencies of the inefficient DMUs and the super-efficiencies of the efficient DMUs can be obtained by a single model. The efficiency scores obtained by our integrated model are the same as those obtained by Du et al. [7] and the additive DEA model.     

Statistical Approach to Identify Threshold and Point of Departure in Dose–Response Data

The study presents an integrated, rigorous statistical approach to define the likelihood of a threshold and point of departure (POD) based on dose–response data using nested family of bent‐hyperbola models. The family includes four models: the full bent‐hyperbola model, which allows for transition between two linear regiments with various levels of smoothness; a bent‐hyperbola model reduced to a spline model, where the transition is fixed to a knot; a bent‐hyperbola model with a restricted negative asymptote slope of zero, named hockey‐stick with arc (HS‐Arc); and spline model reduced further to a hockey‐stick type model (HS), where the first linear segment has a slope of zero. A likelihood‐ratio test is used to discriminate between the models and determine if the more flexible versions of the model provide better or significantly better fit than a hockey‐stick type model. The full bent‐hyperbola model can accommodate both threshold and nonthreshold behavior, can take on concave up and concave down shapes with various levels of curvature, can approximate the biochemically relevant Michaelis–Menten model, and even be reduced to a straight line. Therefore, with the use of this model, the presence or absence of a threshold may even become irrelevant and the best fit of the full bent‐hyperbola model be used to characterize the dose–response behavior and risk levels, with no need for mode of action (MOA) information. Point of departure (POD), characterized by exposure level at which some predetermined response is reached, can be defined using the full model or one of the better fitting reduced models.     

公交系统随机平衡网络设计模型及求解算法

本文根据公交网络的具体特点,对公交网络进行了系统的描述,提出一个双层规划模型来描述随机平衡公交网络设计问题。在双层规划模型中,上层模型为一个标准的公交网络设计模型,下层模型是一个公交网络随机平衡配流模型。针对所提出的模型,设计了基于灵敏度分析的求解算法。最后,给出一个简单算例加以证明。