电子商务环境下物流配送中心选址及其启发式算法

电子商务环境下的物流配送中心选址是很复杂的问题，涉及许多因素。考虑电子商务环境下的物流配送特点，将物流配送中心选址模型从变动费用和时间约束的条件上进行修改，建立适用于电子商务环境下的物流配送中心选址模型。与原有模型相比，这些特点使得物流配送中心选址模型在模型的合理性上和实际应用上有了很大的改进。由于选址模型是NP问题，采用启发式算法进行求解。     

基于微粒群优化算法的物流配送中心选址问题研究

结合物流配送中心选址的特点，建立数学模型．在此基础上研究了基于微粒群算法的物流多配送中心选址问题，得到一种新的多配送中心选址方法。仿真结果证明此方法比传统选址方法更适合多配送中心选址和非线性问题的优化，并且具有传统算法所不具备的灵活性，适用多样的物流配送模型。     

地理信息系统技术与配送中心选址问题结合方法的研究

配送中心选址问题是物流配送行业的一个重要问题,由于其在经济上的重大价值,一直受到国内外学者的广泛关注。本文介绍了配送中心选址问题的研究现状和模型分类方法,然后对地理信息系统技术与配送中心选址问题的结合方法做出了探讨。     

基于重心法和优化离散模型的咸阳农产品配送中心选址研究

本文分析了农产品物流配送中心选址的意义,引入重心法和离散模型,并将此应用数值分析法应用到咸阳市农产品物流配送中心选址研究这一具体问题,给出了农产品物流配送中心选址问题的一个量化的处理方法。     

新华书店的区域配送中心选址模型

文章针对省新华书店配送中心选址问题,综合考虑了书籍运输成本与区域配送中心运营成本等成本因素。建立了以区域配送中心总成本最小化为目标的一个混合整数选址模型;在模型求解方法的选择上,根据混合整数模型的特点,提出了将模型的求解分成两个部分：线性部分采用运输规划法求解,非线性部分采用隐枚举法求解;最后通过实例论证了模型对求解配送中心选址问题的可行性并得到了比较满意的选址结果。     

农产品配送中心选址决策综合模型

农产品物流配送中心选址问题已经成为农产品产业链急需解决的重要问题,也是一个定性与定量相结合的问题.本文针对农产品物流配送中心选址的影响因素和特点进行阐述,并构建农产品配送中心选址的综合模型.     

带车辆路线安排的多配送中心选址问题的求解——基于SPSS和遗传算法

研究了带车辆路线安排的多配送中心选址问题。首先根据宏观的定量指标对需求点运用SPSS进行区域划分,将一个多配送中心选址问题转化为多个单配送中心选址问题,然后,在一辆配送车一次只能为一个需求点供货的情况下,建立并求解了单配送中心选址问题的整数线性规划模型;在一辆配送车一次可以为多个需求点供货的情况下,用遗传算法求出了最佳车辆配送路线和最佳选址位置。     

多分辨率多目标物流配送中心选址模型研究

依据多分辨率建模的基本理念,通过定性与定量选址相结合的方式,从不同维度对备选配送中心进行筛选,确定最佳选址方案,以寻求对现有配送中心选址模型的改进。多分辨率配送中心选址模型首先应用层次分析法从宏观角度对备选配送中心进行指标考量,确定初始选址方案。然后综合考虑选址成本、环境保护和客户满意度等选址影响因素,利用混合整数规划法建立选址模型,通过遗传算法求解模型,确定配送中心最佳选址方案。最后通过算例分析证明了模型可行性,说明多分辨率多目标配送中心选址模型可以兼顾选址的全面性和准确性,并提供综合可靠的配送中心选址方案。     

军需物资油料数据仓库构建研究

本文从分析军需物资油料信息化建设的现状入手，指出建设军需物资油料数据仓库的必要性；分析了军需物资油料数据仓库的数据来源，构建了系统总体框架；从确定主题域、逻辑模型设计、物理模型设计、ETL设计几个步骤详细阐述了该系统的设计过程。     

基于改进遗传算法的生鲜加工配送中心连续选址模型

建立了生鲜加工配送中心选址连续模型，在引入监控算子的基础上，运用改进的遗传算法对模型进行求解。该模型和改进的遗传算法可有效的解决生鲜加工配送中心的选址问题。     

A New Regime Switching Model with State–Varying Endogeneity

In this paper, we propose a state-varying endogenous regime switching model (the SERS model), which includes the endogenous regime switching model by Chang et al., the CCP model, as a special case. To estimate the unknown parameters in the SERS model, we propose a maximum likelihood estimation method. Monte Carlo simulation results show that in the absence of state-varying endogeneity, the SERS model and the CCP model perform similarly, while in the presence of state-varying endogeneity, the SERS model performs much better than the CCP model. Finally, we use the SERS model to analyze Chinese stock market returns, and our empirical results show that there exists strongly state-varying endogeneity in volatility switching for the Shanghai Composite Index returns. Moreover, the SERS model can indeed produce a much more realistic assessment for the regime switching process than the one obtained by the CCP model.     

Linear prediction of temporal aggregates under model misspecification

This paper aims to demonstrate a possible aggregation gain in predicting future aggregates under a practical assumption of model misspecification. Empirical analysis of a number of economic time series suggests that the use of the disaggregate model is not always preferred over the aggregate model in predicting future aggregates, in terms of an out-of-sample prediction root-mean-square error criterion. One possible justification of this interesting phenomena is model misspecification. In particular, if the model fitted to the disaggregate series is misspecified (i.e., not the true data generating mechanism), then the forecast made by a misspecified model is not always the most efficient. This opens up an opportunity for the aggregate model to perform better. It will be of interest to find out when the aggregate model helps. In this paper, we study a framework where the underlying disaggregate series has a periodic structure. We derive and compare the efficiency loss in linear prediction of future aggregates using the adapted disaggregate model and aggregate model. Some scenarios for aggregation gain to occur are identified. Numerical results show that the aggregate model helps over a fairly large region in the parameter space of the periodic model that we studied.     

A coupled Markov chain approach to credit risk modeling

We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heuristic global optimization techniques.We benchmark the model against a GLMM model in the context of bond portfolio risk management. The proposed model yields stronger dependencies and higher risks than the GLMM model. As a result, the risk optimal portfolios are more conservative than the decisions resulting from the benchmark model.     

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.     

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.     

基于业务事件的网络财务报告披露模式研究

本文研究了网络财务报告披露模式问题。文章首先回顾和评价现有的关于网络财务报告披露模式的观点即经济事项模式、数据库报告模式、交换式按需报告模式以及多层面报告模式,在此基础上提出了业务事件驱动报告模式的基本原理。文章最后比较了业务事件驱动模式与经济事项模式的异同。     

Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts

We construct a DSGE-VAR model for competing head to head with the long history of published forecasts of the Reserve Bank of New Zealand. We also construct a Bayesian VAR model with a Minnesota prior for forecast comparison. The DSGE-VAR model combines a structural DSGE model with a statistical VAR model based on the in-sample fit over the majority of New Zealand’s inflation-targeting period. We evaluate the real-time out-of-sample forecasting performance of the DSGE-VAR model, and show that the forecasts from the DSGE-VAR are competitive with the Reserve Bank of New Zealand’s published, judgmentally-adjusted forecasts. The Bayesian VAR model with a Minnesota prior also provides a competitive forecasting performance, and generally, with a few exceptions, out-performs both the DSGE-VAR and the Reserve Bank’s own forecasts.     

基于CVaR与平均离差波动限制的投资组合模型

提出了一种基于CVaR的投资组合模型,对组合资产收益率不做正态分布假设,用MAD模型作为一个约束条件,实现波动性度量限制,用上凸效用函数作为一个约束条件,表示风险资产交易费用。实验结果表明,该模型满足实际投资要求,符合实际投资规律,与M-V模型和原始CVaR模型相比具有波动性和风险价值最小化的优势。     

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.     

Semiparametric estimation of a hedonic price function

Previous work on the preferred specification of hedonic price models usually recommended a Box-Cox model. In this paper we note that any parametric model involves implicit restrictions and they can be reduced by using a semiparametric model. We estimate a benchmark parametric model which passes several common specification tests, before showing that a semiparametric model outperforms it significantly. In addition to estimating the model, we compare the predictions of the models by deriving the distribution of the predicted log(price) and then calculating the associated prediction intervals. Our data show that the semiparametric model provides more accurate mean predictions than the benchmark parametric model.