按单装配系统中的协同补货问题研究

多库存品的库存系统中各库存品具有需求相关性在现实中很常见,但许多关于库存的文献并没有考虑这一点.针对按单装配系统的特性,本文研究了不同部件需求具有相关性的情况下的库存控制问题,分析了不同部件独立补货和不同部件协同补货两种补货策略.数值分析表明,按单装配系统中部件协同补货策略比部件独立补货策略具有更低的总成本.     

动态联盟中伙伴选择问题的区间规划模型及其求解

伙伴选择和风险管理是动态联盟中的重要决策问题,当考虑失败风险时,失败概率无法给出精确值,因此,考虑采用不确定性规划描述此类问题.提出动态联盟中伙伴选择问题的区间规划模型,模型中用区间数表示联盟伙伴的失败概率.为了求解该模型,引入序关系,并利用Nakahara和Ishibuchi的定理,将区间规划模型转化为等价的清晰双目标模型.设计带自适应适值函数的遗传算法,求出问题的全部非劣解.经过对多个问题的仿真,证明了算法的有效性.     

面向网络营销企业的营销信号选择博弈分析

如何通过营销信号来降低电子市场逆向选择风险是影响网络营销企业业绩的重要因素.本文以两家企业垄断市场为例,建立了企业信号传递博弈模型.通过对分离均衡与混同均衡存在条件的分析,讨论了信号传递成本以及不同消费者群体所占比例对企业营销信号决策的影响.分析结果有助于网络营销企业在电子市场中选择营销信号.     

教育政策的利益分析——以择校问题为例

利益是公共政策分析的基本范畴,实现公共利益是教育政策制定最重要的目标。教育政策的利益分析包括利益主体分析、利益需求分析、利益实现方式分析以及利益结果分析四个方面。从利益分析的角度来看,择校问题实际上是由于优质教育资源供需矛盾而导致的,其中教育资源分配不均衡是根本原因。择校代表了一种个人的基本权利,体现的是个人利益。通过改革不合理的教育供给方式、增加教育服务供给,是治理择校问题、平衡公共利益和私人利益的有效途径。     

基于累积前景理论的多资产投资组合优化与实证研究

基于CPT视角的多风险资产投资组合模型,探讨投资者面对不同投资风险时的心理变化以及心理变化对其投资策略的影响。通过选取不同超额收益率及波动率水平的股票,测试投资策略对于风险态度指标的敏感度。研究表明,投资者面对不同风险具有明显地心理变化,并且其心理变化对投资策略具有显著的影响。具体表现在几个方面：投资者面对不确定收益时,表现出风险厌恶,面对不确定损失时,表现出风险偏好;投资者将无风险资产的投资收益作为心理参考点,所做的投资决策与相对于此参考点的相对财富水平的变化有关,而不是与传统理论中的绝对财富变化量相关。     

企业用户云服务技术选择行为

如何选择最佳云服务方案,是企业决定采纳云服务之后所面临的一个重要决策。为了解决云服务技术选择问题,从业务流程的视角出发,以模块化系统理论为基础,提出一个流程模块化、IT可拆解性及安全风险如何影响云服务技术选择的研究模型。采用问卷调查和计量经济学的普通最小二乘法OLS进行实证分析。研究结果表明:流程模块化和IT可拆解性对云服务技术选择的抽象程度有显著的正向影响,即流程模块化程度IT可拆解性越高的企业,越倾向于选择抽象程度高的云服务,如软件即服务SaaS;而流程模块化程度和IT可拆解性越低的企业,越倾向于选择抽象程度低的云服务,如基础设施即服务IaaS。同时,安全风险会正向调节流程模块化和IT可拆解性对云服务技术选择的抽象程度的影响。     

高新技术创新的文化路径及政策选择

相对一般的技术创新，高新技术创新有其独有的一些特点，这些特点决定了文化对高新技术创新具有重要作用。高新技术创新的文化路径包括观念文化路径、制度文化路径和行动文化路径三个方面。基于文化视角优化高新技术创新的政策选择，完善我国科技政策，有助于推动高新技术的进一步创新。     

A likelihood ratio test to discriminate exponential–Poisson and gamma distributions

The exponential–Poisson (EP) distribution with scale and shape parameters β>0 and λ∈?, respectively, is a lifetime distribution obtained by mixing exponential and zero-truncated Poisson models. The EP distribution has been a good alternative to the gamma distribution for modelling lifetime, reliability and time intervals of successive natural disasters. Both EP and gamma distributions have some similarities and properties in common, for example, their densities may be strictly decreasing or unimodal, and their hazard rate functions may be decreasing, increasing or constant depending on their shape parameters. On the other hand, the EP distribution has several interesting applications based on stochastic representations involving maximum and minimum of iid exponential variables (with random sample size) which make it of distinguishable scientific importance from the gamma distribution. Given the similarities and different scientific relevance between these models, one question of interest is how to discriminate them. With this in mind, we propose a likelihood ratio test based on Cox's statistic to discriminate the EP and gamma distributions. The asymptotic distribution of the normalized logarithm of the ratio of the maximized likelihoods under two null hypotheses – data come from EP or gamma distributions – is provided. With this, we obtain the probabilities of correct selection. Hence, we propose to choose the model that maximizes the probability of correct selection (PCS). We also determinate the minimum sample size required to discriminate the EP and gamma distributions when the PCS and a given tolerance level based on some distance are before stated. A simulation study to evaluate the accuracy of the asymptotic probabilities of correct selection is also presented. The paper is motivated by two applications to real data sets.     

Quadratic Approximation via the SCAD Penalty with a Diverging Number of Parameters

The high-dimensional data arises in diverse fields of sciences, engineering and humanities. Variable selection plays an important role in dealing with high dimensional statistical modelling. In this article, we study the variable selection of quadratic approximation via the smoothly clipped absolute deviation (SCAD) penalty with a diverging number of parameters. We provide a unified method to select variables and estimate parameters for various of high dimensional models. Under appropriate conditions and with a proper regularization parameter, we show that the estimator has consistency and sparsity, and the estimators of nonzero coefficients enjoy the asymptotic normality as they would have if the zero coefficients were known in advance. In addition, under some mild conditions, we can obtain the global solution of the penalized objective function with the SCAD penalty. Numerical studies and a real data analysis are carried out to confirm the performance of the proposed method.