Unaddressed problems and research perspectives in scheduling blood collection from donors

Many optimisation problems arise in managing the Blood Donation (BD) supply chain. Most of them have been addressed in the literature, while other problems, e.g. donation scheduling, have not been sufficiently addressed so far. However, blood collection from donors may have a disruptive impact on the entire BD supply chain if not properly managed. For example, scheduling donation appointments based on some system metrics may improve the flow of blood units through the BD supply chain and increase the quality of service perceived by donors. In this short communication, we present the donation scheduling problem and highlight the lack of work in the literature that deals with this problem. Then, we analyse the impact of an unbalanced flow of blood units on the BD system and the benefits that may derive from a suitable appointment scheduling. Finally, we propose suggestions for future research in BD collection scheduling that may improve the BD process.     

基于消费者效用的生鲜农产品供应链生鲜度激励机制研究

生鲜供应链整体生鲜度的提升对减少浪费、提高供应链整体盈利水平及社会福利有重要意义。本文基于消费者效用理论研究了两供应商和单一零售商组成的供应链系统，构建了单周期下生鲜农产品生鲜度激励模型，模型以供应商为领导者，采用Stackelberg博弈方法求解得到了均衡状态下供应商、零售商的最优定价策略及供应商新鲜度努力选择。研究结果表明，供应商和零售商利润与消费者价格敏感系数呈反向变化，与新鲜度敏感系数呈同向变化。在价格竞争市场，供应商保鲜努力程度和利润与价格替代率呈反向变化；在生鲜度竞争市场，供应商保鲜努力程度和利润与新鲜度替代率呈同向变化。基于研究结论，厂商及政府应当联手引导消费者的消费观念，尽可能减少由于信息不对称引致的道德风险，在市场价格替代率不变甚至降低的情形下，提高生鲜度替代率，使得消费者的意愿支付价格上升，提高自身讨价还价能力，获取更多利润。     

核心企业主导下乳制品全产业链质量管控GERT网络模型

乳制品质量安全是关系消费者健康的重大问题，乳制品质量管控是一项涵盖多环节、多主体的复杂系统工程；而要把质量管控措施落到实处，乳制品产业链的核心企业是关键。针对我国乳制品产业质量管控现状，构建了基于质量价值流动的乳制品全产业链质量管控GERT网络模型，并对"产业链主体未加强质量管控"、"各主体独立加强质量管控"以及"核心企业主导下全产业链质量管控"三种情境进行对比分析。研究表明，核心企业主导下全产业链质量管控具有明显优势；强化乳制品行业质量管控，须从政府层面、企业层面以及消费者层面采取对策，尤其要突出核心企业在此过程中的主导地位。     

产品服务供应链的“双重收益共享”合作机制

产品与服务融合缓解了"供需错位"矛盾，使得产品服务供应链探索成为"供给侧结构性改革"下供需结构性匹配的重要路径。本文刻画产品服务视角下供应链组织结构的本质特征，构建产品服务供应链合作模型，探讨合作机制下"帕累托改进"区间存在性，揭示合作机制对产品服务供应链效率的作用机理。核心研究发现：供应链多元化组织结构加剧收益分配冲突，并导致服务规模与供应链效率"倒挂"现象，而合作机制下"双重收益共享"合作模型能提升产品服务供应链效率。     

Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model

Time-varying GARCH-M models are commonly employed in econometrics and financial economics. Yet the recursive nature of the conditional variance makes likelihood analysis of these models computationally infeasible. This article outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only O(T) computational operations, where T is the sample size. Furthermore, the theoretical dynamic properties of a time-varying-parameter EGARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.     

Bootstrapping Periodic State-Space Models

This article is concerned with how the bootstrap can be applied to study conditional forecast error distributions and construct prediction regions for future observations in periodic time-varying state-space models. We derive, first, an algorithm for assessing the precision of quasi-maximum likelihood estimates of the parameters. As a result, the derived algorithm is exploited for numerically evaluating the conditional forecast accuracy of a periodic time series model expressed in state space form. We propose a method which requires the backward, or reverse-time, representation of the model for assessing conditional forecast errors. Finally, the small sample properties of the proposed procedures will be investigated by some simulation studies. Furthermore, we illustrate the results by applying the proposed method to a real time series.     

Joint models for mixed categorical outcomes: a study of HIV risk perception and disease status in Mozambique

Two types of bivariate models for categorical response variables are introduced to deal with special categories such as ‘unsure’ or ‘unknown’ in combination with other ordinal categories, while taking additional hierarchical data structures into account. The latter is achieved by the use of different covariance structures for a trivariate random effect. The models are applied to data from the INSIDA survey, where interest goes to the effect of covariates on the association between HIV risk perception (quadrinomial with an ‘unknown risk’ category) and HIV infection status (binary). The final model combines continuation-ratio with cumulative link logits for the risk perception, together with partly correlated and partly shared trivariate random effects for the household level. The results indicate that only age has a significant effect on the association between HIV risk perception and infection status. The proposed models may be useful in various fields of application such as social and biomedical sciences, epidemiology and public health.     

Joint Modelling of Survival and Longitudinal Data with Informative Observation Times

In this paper, we consider the joint modelling of survival and longitudinal data with informative observation time points. The survival model and the longitudinal model are linked via random effects, for which no distribution assumption is required under our estimation approach. The estimator is shown to be consistent and asymptotically normal. The proposed estimator and its estimated covariance matrix can be easily calculated. Simulation studies and an application to a primary biliary cirrhosis study are also provided.     

Mixtures of regression models with incomplete and noisy data

The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.     

A class of partially linear transformation models for recurrent gap times

In this article, we propose a general class of partially linear transformation models for recurrent gap time data, which extends the linear transformation models by incorporating non linear covariate effects and includes the partially linear proportional hazards and the partially linear proportional odds models as special cases. Both global and local estimating equations are developed to estimate the parametric and non parametric covariate effects, and the asymptotic properties of the resulting estimators are established. The finite-sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease is provided.