电子商务网站竞争模型分析及其策略

研究电子商务网站竞争模型,分析其平衡点的稳定性,给出数值模拟图,建立一系列电子商务网站获胜的新策略.     

双时滞网站竞争模型的稳定性及Hopf分析

考虑了一类同时具有自反馈时滞和竞争时滞且取值不相同的网站竞争模型.将时滞作为分支参数,利用系统正平衡点处的特征方程分析了系统局部渐近稳定的充分条件以及Hopf分支的存在性问题.最后,通过数值模拟验证了结论的正确性.     

一类带有竞争和自反时滞的网站竞争系统的Hopf分支

不同于以往研究网站竞争系统时,仅考虑带有自反时滞或竞争时滞的情况,本文研究了一类同时带有竞争时滞和自反时滞的网站竞争系统,并以时滞作为分支参数,通过分析正平衡点处的特征方程,研究了正平衡点的稳定性,证明了Hopf分支的存在性,得到了发生Hopf分支时的临界的时滞值,最后通过数值模拟进一步验证了所得结论.     

基于拼团模式的网络团购定价策略

随着移动互联网的发展,拼团模式受到越来越多零售商和消费者的追捧.以传统商业团购模型为基础,引入团购尺寸、发起者精力成本、需求扩散效应和拼团成功率,建立了零售商同时采取线上拼团和线下传统渠道策略的两阶段利润优化模型.运用Stackelberg博弈模型求解零售商和团购网站之间的利润分配问题,得出了零售商和团购网站的最优定价策略.通过算例分析了各因素对零售商和团购网站最优决策及利润的影响,对比分析了传统商业团购与拼团模式,据此给出了相关各方决策建议.     

资源优化配置下的供应链竞争模型研究

本文在供应链竞争的经济背景下,考虑由多条结构异质的供应链构成的竞争模型.供应链在市场需求的推动下以自身利润最大化为决策标准,整合生产工序、分配企业内部资源.本文首先通过市场链的定义分析了供应链间的竞争方式;然后,利用博弈理论以及变分不等式方法,构建了供应链间竞争的均衡模型;进一步,给出了模型解的存在性和唯一性.最后,利用数值算例说明了模型的有效性.     

北京市区县政府网站绩效评估方法研究

政府网站是政府面向公众的窗口,也是政府为人民服务的一种新形式,研究政府网站评估与提升策略对完善政府网站建设有积极的推动作用.分析了2016年政府网站绩效评估指标体系和评价结果.在大数据背景下,以北京市平谷区为例,使用起伏型时间序列模型和加权移动平均法对评估结果进行预测,在已有的数据基础之上,预测了2017年的评估得分.研究可以对北京市各区县未来网站建设的方向有一定的促进意义,同时也对其他地方政府网站的预测提供了具有借鉴价值的方法.     

基于生态模型的企业竞争稳定性研究

从生态学视角来研究企业竞争的动态演化.针对现有研究文献中只讨论企业自身线性制约的三维系统的不足,构造了企业自身非线性制约的三维竞争模型,运用微分方程稳定性理论分析其稳定性,并由此揭示出企业间竞争的动力学机制.数值仿真结果表明,该三维模型能有效地模拟企业间竞争的动态演化规律.     

竞争力研究的新视角:动力学理论分析框架

突破基于指标体系评估的静态分析模式,提出了竞争力研究的新视角,建立动力学理论分析框架.提出了竞争力场模型,包括竞争强度、竞争势、要素密度、要素极化等定义及其表达方式,并创建竞争场能、竞争场信息熵的概念和数学模型.给出了竞争场的高斯定理与环路定理,并证明了竞争场信息熵的凸性定理及其极大熵原理.最后指出了竞争力的动力学理论研究的进一步发展方向.     

考虑等待成本和需求扩散效应的网络团购定价策略

网络团购越来越受到零售商和消费者的追捧.零售商往往会在传统销售渠道的基础上,与网络平台合作适时推出团购策略.对于到达预定时间并达到一定团购尺寸的团购问题,不但考虑了传统渠道和团购渠道的相互影响,还引入了等待成本和团购产生的需求扩散效应,分别构建了零售商和团购网站的两阶段利润最优化模型,利用Stackelberg博弈理论分析了零售商和团购网站之间的利润分配.通过算例分析了团购网站的单位佣金、团购时间以及需求扩散效应对零售商和团购网站最优决策及利润的影响,据此给出相关各方决策建议.     

创新生态系统中两斑块种群关系研究——以江苏、浙江的工业企业为例

基于生态学原理,构建两斑块种群竞争模型.以江苏省和浙江省两斑块内工业企业为研究对象,分析工业企业种群竞争关系.浙江省工业企业种群内部互动关系显著,且呈现明显的协同关系.江苏省工业企业种群内部互动关系不显著.logistic模型和竞争度模型测算结果均表明江苏省和浙江省的工业企业竞争关系不强.测算两斑块工业企业种群投入产出指标的Malmquist指数,得到全要素生产率、技术进步和效率的变化趋势.发现工业企业协同效应促进了效率的提高,协同效应没有促进技术进步.     

Effect of toxic substance on delayed competitive allelopathic phytoplankton system with varying parameters through stability and bifurcation analysis

We have studied the combined effect of toxicant and fluctuation of the biological parameters on the dynamical behaviors of a delayed two-species competitive system with imprecise biological parameters. Due to the global increase of harmful phytoplankton blooms, the study of dynamic interactions between two competing phytoplankton species in the presence of toxic substances is an active field of research now days. The ordinary mathematical formulation of models for two competing phytoplankton species, when one or both the species liberate toxic substances, is unable to capture the oscillatory and highly variable growth of phytoplankton populations. The deterministic model never predicts the sudden localized behavior of certain species. These obstacles of mathematical modeling can be overcomed if we include interval variability of biological parameters in our modeling approach. In this investigation, we construct imprecise models of allelopathic interactions between two competing phytoplankton species as a parametric differential equation model. We incorporate the effect of toxicant on the species in two different cases known as toxic inhibition and toxic stimulatory system. We have discussed the existence of various equilibrium points and stability of the system at these equilibrium points. In case of toxic stimulatory system, the delay model exhibits a stable limit cycle oscillation. Analytical findings are supported through exhaustive numerical simulations.     

On Modelling Advertisement in Cournot Duopoly

A system of two firms is studied following Puus model of Cournot duopoly. Their competing advertisements are modelled. Chaos is observed if the advertisements parameters of both firms are close to each other. Chaos control formulae are given to control the chaos due to advertisements.     

A two species competition model under the simultaneous effect of toxicant and disease

A mathematical model is proposed to study the simultaneous effects of toxicants and infectious diseases on a competing species system. It is assumed that the competing populations are adversely affected by the toxicant and one of them is vulnerable to an infectious disease. In this paper, two models are studied separately. The first model is developed to study the effect of only infectious diseases on the existence of a two competing species system in the absence of a toxicant, whereas in the second model the presence of a toxicant is also taken into account. In both the models, conditions for the existence of interior equilibria are derived. The models are analyzed using stability theory, and conditions for the nonlinear stability of the interior equilibria are obtained using Lyapunov’s direct method. Further, the models are studied numerically by taking two sets of numerical values for each model and the results are compared.     

一类竞争扩散系统的二重固有值分岐

本文讨论一类反应扩散方程组,它刻画了栖息在同一有界区域内的两个种群的群体密度的变化状况.我们以种群的出生率α和b作分歧参数,讨论了在二重固有值处的分歧并得到了存在性及稳定性的结果.     

竞争风险数据和协变量相依权重下可加可乘的子分布风险率模型

本文在竞争风险数据下提出一种灵活的含变系数的可加可乘的子分布风险率模型.通过对删失时间的风险函数建立Cox比例风险模型,得到调整后的与协变量相依的权重,在新权重下建立估计方程来估计模型参数,并获得了估计的大样本性质,同时提出了模型中协变量的时变效应的检验方法.通过数值模拟验证了所提方法的有限样本性质,结果表明所提方法可以大大降低估计偏差.最后,分析了一组淋巴滤泡细胞的竞争风险数据集来展示所提方法的实际应用效果.     

Reliability evaluation based on a dependent two-stage failure process with competing failures

This paper evaluates system reliability performance based on a dependent two-stage failure process with competing failures. The failure process of the system can be divided into two stages, i.e., the defect initialization stage, and the defect development stage. Dependence between these two stages is reflected in the fact that they share the same shock process modeled by a nonhomogeneous Poisson process. The impact of shock damage on system failure behavior is characterized by random hazard rate increments of the two stages. Based on practical failure behavior of industrial systems, we consider two typical and competing failure modes, defect-based failure and duration-based failure. Defect-based failure occurs when a defect reaches the damage threshold and duration-based failure is triggered when the duration in defective state is larger than a time threshold. We derive some results on system reliability and show that, with different parameter settings, our model reduces to several classic competing risk models. Finally, a detailed illustrative example of an oil pipeline system is given to demonstrate the applicability of the proposed model.     

Multiple attractors in a Leslie–Gower competition system with Allee effects

We investigate asymptotic dynamics of the classical Leslie–Gower competition model when both competing populations are subject to Allee effects. The system may possess four interior steady states. It is proved that for certain parameter regimes both competing populations may either go extinct, coexist or one population drives the other population to extinction depending on initial conditions.     

Piecewise Linear Approximations for Cure Rate Models and Associated Inferential Issues

Cure rate models offer a convenient way to model time-to-event data by allowing a proportion of individuals in the population to be completely cured so that they never face the event of interest (say, death). The most studied cure rate models can be defined through a competing cause scenario in which the random variables corresponding to the time-to-event for each competing causes are conditionally independent and identically distributed while the actual number of competing causes is a latent discrete random variable. The main interest is then in the estimation of the cured proportion as well as in developing inference about failure times of the susceptibles. The existing literature consists of parametric and non/semi-parametric approaches, while the expectation maximization (EM) algorithm offers an efficient tool for the estimation of the model parameters due to the presence of right censoring in the data. In this paper, we study the cases wherein the number of competing causes is either a binary or Poisson random variable and a piecewise linear function is used for modeling the hazard function of the time-to-event. Exact likelihood inference is then developed based on the EM algorithm and the inverse of the observed information matrix is used for developing asymptotic confidence intervals. The Monte Carlo simulation study demonstrates the accuracy of the proposed non-parametric approach compared to the results attained from the true correct parametric model. The proposed model and the inferential method is finally illustrated with a data set on cutaneous melanoma.     

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In this paper, we investigate a competing risks model based on exponentiated Weibull distribution under Type-I progressively hybrid censoring scheme. To estimate the unknown parameters and reliability function, the maximum likelihood estimators and asymptotic confidence intervals are derived. Since Bayesian posterior density functions cannot be given in closed forms, we adopt Markov chain Monte Carlo method to calculate approximate Bayes estimators and highest posterior density credible intervals. To illustrate the estimation methods, a simulation study is carried out with numerical results. It is concluded that the maximum likelihood estimation and Bayesian estimation can be used for statistical inference in competing risks model under Type-I progressively hybrid censoring scheme.     

The signal model: A model for competing risks of opportunistic maintenance

This paper presents a competing risks reliability model for a system that releases signals each time its condition deteriorates. The released signals are used to inform opportunistic maintenance. The model provides a framework for the determination of the underlying system lifetime from right-censored data, without requiring explicit assumptions about the type of censoring to be made. The parameters of the model are estimated from observational data by using maximum likelihood estimation. We illustrate the estimation process through a simulation study. The proposed signal model can be used to support decision-making in optimising preventive maintenance: at a component level, estimates of the underlying failure distribution can be used to identify the critical signal that would trigger maintenance of the individual component; at a multi-component system level, accurate estimates of the component underlying lifetimes are important when making general maintenance decisions. The benefit of good estimation from censored data, when adequate knowledge about the dependence structure is not available, may justify the additional data collection cost in cases where full signal data is not available.