供应链二级分销网络的一种成本优化模型

提出了一种二级分销网络的成本优化的模型。模型研究了单个工厂的制造商和多个分销商组成的二级分销网络成本优化问题,把保证不发生缺货情况作为约束条件。综合考虑了订货成本、库存持有成本、运输成本和建立分销点和分销中心的成本,并给出了基于遗传算法的模型求解步骤。     

供应链分销系统成本最小化模型

研究由多个制造商、一个中间仓库和多个分销商组成的供应链分销系统,建立综合考虑库存成本、订货成本和运输成本的分销网络成本最小化模型,并在此基础上进行简单的实例分析。     

信息共享下的供应链分销成本博弈分配

供应链的持续创新能力是保证整条供应链竞争力的关键,从降低分销成本的角度,应用博弈理论,讨论了降低分销成本所引起的费用在制造商和分销商之间的博弈分配,并得出了一个可以使双方满意的博弈结果.     

基于赠送式捆绑销售的分销系统优化模型研究

以实现分销系统合作伙伴双赢为目标，在市场需求不确定情况下，运用二层非线性混合整数规划模型，研究了一个处于上层的分销商。在采取赠送式捆绑销售策略时的最佳捆绑方式及一个作为下层的零售商的最佳订货批量问题。设计一种基于遗传算法的求解方法，并针对仿真算例求解，验证了模型的有效性。结果表明，由分销商提出的捆绑销售策略有利于提高分销商和零售商利润。     

供应链物流成本变化的经济效应分析

指出生产成本和销售成本是影响整个供应链各环节的重要因素,在生产商与分销商稳定合作的条件下,建立了生产商利润、销售商利润与生产成本、销售成本之间的函数关系,分析了成本变化对供应链的经济效应.     

中小企业担保贷款的违约风险中性定价与担保费率厘定

基于风险中性原理,研究银行的贷款定价模型和担保机构的费率厘定方法,最终给出贷款利率与担保费率简单实用的表达式。基于风性中性的违约风险定价模型与担保费率厘定具有科学的理论依据,能够避免结构化模型的复杂性与高成本,排除简化模型中回收率设定不合理所带来的定价偏差。本文的研究为担保贷款利率的确定与担保费率的厘定提供了实务上的参考,有助于银行和担保机构动态调整利率和费率,节约管理成本。     

产品分销成本与渠道模式

据统计,主要生产商的最终用户成本中,大约有40％是在分销渠道中产生的。因此对分销成本的研究日趋重要。本文从企业财务的角度对产品分销成本与渠道模式之间关系进行了研究。研究表明:渠道模式的选定与变革在很大程度上受到产品分销成本的影响,并得出成本最低的结构就是最适当的渠道结构和模式的结论。     

绿色分销商的选择研究

分销商是制造企业与客户之间的纽带,分销商的选择是企业的分销渠道设计的重要环节之一.随着可持续发展深入人心,绿色因素也会逐渐纳入分销商选择的评价因素之中.文章基于灰色系统理论,利用灰色层次关联分析建立分销商选择模型,为决策者进行绿色分销商选择提供依据.     

一类供应链分销网络的双层多目标规划模型

以多生产商、多分销中心的供应链分销网络为基础,建立了多目标双层机会约束规划模型。基于协同供应链管理思想,充分考虑了多生产厂商相互制约以及决策信息的不完整性、市场需求不确定性,探讨如何实现整体成本的最小化。最后,结合一种算法给出了该模型的求解方法,并结合一个实例证明算法的有效性。     

基于信息共享的供应链牛鞭效应仿真研究

本文建立了包括供应商、生产商、分销商、零售商的多级库存系统动力学模型,从理论上看,如果具备一定的条件,就可以有效避免信息扭曲放大的现象,缓解牛鞭效应。     

The Cox,Ross and Rubinstein tree model which includes counterparty credit risk and funding costs

The binomial asset pricing model of Cox, Ross and Rubinstein (CRR) is extensively used for the valuation of options. The CRR model is a discrete analog of the Black–Scholes–Merton (BSM) model. The 2008 credit crisis exposed the shortcomings of the oversimplified assumptions of the BSM model. Burgard and Kjaer extended the BSM model to include adjustments such as a credit value adjustment (CVA), a debit value adjustment (DVA) and a funding value adjustment (FVA). The aim of this paper is to extend the CRR model to include CVA, DVA and FVA and to prove that this extended CRR model coincides with the model that results from discretising the Burgard and Kjaer model. Our results are numerically implemented and we also show that as the number of time-steps increase in the derived tree structure model, the model converges to the model developed by Burgard and Kjaer.     

An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores

The analysis of sports data, in particular football match outcomes, has always produced an immense interest among the statisticians. In this paper, we adopt the generalized Poisson difference distribution (GPDD) to model the goal difference of football matches. We discuss the advantages of the proposed model over the Poisson difference (PD) model, which was also used for the same purpose. The GPDD model, like the PD model, is based on the goal difference in each game that allows us to account for the correlation without explicitly modelling it. The main advantage of the GPDD model is its flexibility in the tails by considering shorter as well as longer tails than the PD distribution. We carry out the analysis in a Bayesian framework in order to incorporate external information, such as historical knowledge or data, through the prior distributions. We model both the mean and the variance of the goal difference and show that such a model performs considerably better than a model with a fixed variance. Finally, the proposed model is fitted to the 2012–2013 Italian Serie A football data, and various model diagnostics are carried out to evaluate the performance of the model.     

Forecasting correlated time series with exponential smoothing models

This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters’ model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection criterion is introduced into the forecasting scheme for selecting the most adequate multivariate model for describing the behaviour of the time series under study. The forecasting performance of this procedure is tested using some real examples.     

Nowcasting German GDP: Foreign factors,financial markets,and model averaging

This paper develops a nowcasting model for the German economy. The model outperforms a number of alternatives and produces forecasts not only for GDP but also for other key variables. We show that the inclusion of a foreign factor improves the model’s performance, while financial variables do not. Additionally, a comprehensive model averaging exercise reveals that factor extraction in a single model delivers slightly better results than averaging across models. Finally, we estimate a “news” index for the German economy in order to assess the overall performance of the model beyond forecast errors in GDP. The index is constructed as a weighted average of the nowcast errors related to each variable included in the model.     

Forecasting returns in the VIX futures market

This paper introduces a new forecasting model for VIX futures returns. The model is structural in nature and parsimonious, and contains parameters that are relatively easy to estimate. The forecasts of next day VIX futures returns based on this model are superior to those produced by a linear forecasting model that uses the same set of predictors. Moreover, the profits to a market-timing model based on the proposed forecasts are statistically and economically significant, and are robust to both the method used for adjusting for risk and transaction costs (up to around 15 basis points). In contrast, the forecasts generated by the linear forecasting model are not.     

危险品运输风险综合评价模型

利用近似算法,综合TR和CR两个模型形成了危险品运输风险综合评价模型。并且通过数字实例和现实危险品运输网络的运行,证明该综合模型具有良好表现。     

季节性库存条件下混合仓储空间的决策模型研究

研究并建立了混合仓储空间决策模型，并对模型进行了求解，从而使企业能够便捷迅速地为其季节性库存制定出最优的自有仓库库容。     

Queensland State Impact and Projection Model: The Household Sector

The Queensland Impact and Projection model is an integrated input–output econometric model of the Queensland economy. Its purpose is to complement a conventional input–output model for analyzing economic impacts at the state level. This paper provides an overview of some of the methods used to model the household sector in an input–output framework, before describing the approach taken in the Queensland model. Some results which support the empirical performance of the model are also provided. It is demonstrated that the integrated model is a viable alternative and improvement on the conventional input–output model. The results are consistent with the static input–output model and conform to expectations about how the economy responds in real impact situations.     

Regression density estimation using smooth adaptive Gaussian mixtures

We model a regression density flexibly so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends the existing models in two important ways. First, the components are allowed to be heteroscedastic regressions as the standard model with homoscedastic regressions can give a poor fit to heteroscedastic data, especially when the number of covariates is large. Furthermore, we typically need fewer components, which makes it easier to interpret the model and speeds up the computation. The second main extension is to introduce a novel variable selection prior into all the components of the model. The variable selection prior acts as a self-adjusting mechanism that prevents overfitting and makes it feasible to fit flexible high-dimensional surfaces. We use Bayesian inference and Markov Chain Monte Carlo methods to estimate the model. Simulated and real examples are used to show that the full generality of our model is required to fit a large class of densities, but also that special cases of the general model are interesting models for economic data.     

Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts

We propose a family of regression models to adjust for nonrandom dropouts in the analysis of longitudinal outcomes with fully observed covariates. The approach conceptually focuses on generalized linear models with random effects. A novel formulation of a shared random effects model is presented and shown to provide a dropout selection parameter with a meaningful interpretation. The proposed semiparametric and parametric models are made part of a sensitivity analysis to delineate the range of inferences consistent with observed data. Concerns about model identifiability are addressed by fixing some model parameters to construct functional estimators that are used as the basis of a global sensitivity test for parameter contrasts. Our simulation studies demonstrate a large reduction of bias for the semiparametric model relatively to the parametric model at times where the dropout rate is high or the dropout model is misspecified. The methodology's practical utility is illustrated in a data analysis.