Climate change and optimal energy technology R&D policy

Public policy response to global climate change presents a classic problem of decision making under uncertainty. Theoretical work has shown that explicitly accounting for uncertainty and learning in climate change can have a large impact on optimal policy, especially technology policy. However, theory also shows that the specific impacts of uncertainty are ambiguous. In this paper, we provide a framework that combines economics and decision analysis to implement probabilistic data on energy technology research and development (R&D) policy in response to global climate change. We find that, given a budget constraint, the composition of the optimal R&D portfolio is highly diversified and robust to risk in climate damages. The overall optimal investment into technical change, however, does depend (in a non-monotonic way) on the risk in climate damages. Finally, we show that in order to properly value R&D, abatement must be included as a recourse decision.     

Generalized Markov Models of Infectious Disease Spread: A Novel Framework for Developing Dynamic Health Policies

We propose a class of mathematical models for the transmission of infectious diseases in large populations. This class of models, which generalizes the existing discrete-time Markov chain models of infectious diseases, is compatible with efficient dynamic optimization techniques to assist real-time selection and modification of public health interventions in response to evolving epidemiological situations and changing availability of information and medical resources. While retaining the strength of existing classes of mathematical models in their ability to represent the within-host natural history of disease and between-host transmission dynamics, the proposed models possess two advantages over previous models: (1) these models can be used to generate optimal dynamic health policies for controlling spreads of infectious diseases, and (2) these models are able to approximate the spread of the disease in relatively large populations with a limited state space size and computation time.     

口腔医院系统发展对策生成与对策效应定量仿真评价

医院系统是一个复杂系统.对此,采用对策生成与对策实施未来效应仿真评价法对南昌大学口腔医院发展进行研究.首先,对南昌大学口腔医院发展进行了深入调查与数据分析,然后,建立7个增长正反馈环和4个制约负反馈环构成的增长制约反馈基模,揭示现行系统发展的优势和存在的问题,又围绕7个增长正反馈环和4个制约负反馈环的内涵,依据消除制约的原理,提出了消除负反馈环制约的两项管理对策.接着,对管理对策未来实施效应进行仿真评价,基于评价指标建立流位流率系,基于调查数据值及比较分析建立七棵入树的仿真方程,建立仿真模型,分别对南昌大学口腔医院系统发展两项对策实施状况进行仿真评价,定量论证了所制定的管理对策的科学性及实施的必要性,为南昌大学口腔医院十二五规划编制与发展提供了决策依据.     

需求率为斜坡型的一类变质性物品最优库存策略

本文在考虑需求率服从斜坡型分布的情况下,研究了允许缺货且缺货完全回补、变质率服从威布尔分布、补货率为无穷、有限计划期内的库存模型,证明了最优补货策略的存在性,并给出了求解最优补货策略的算法.     

第三方负责回收的Dowrside-Risk闭环供应链协调性研究

以风险中性制造商、第三方物流服务商和具有下行风险特性的销售商组成的闭环供应链系统为背景,证明了在Downside-Risk约束下收益费用共享契约不能使闭环供应链协调.通过将补偿策略附加到该契约,设计了风险共享契约,既能满足下行风险约束,又保证供应链参与方利润均有增量,实现了Downside-Risk约束下闭环供应链的协调.最后通过应用算例说明了风险共享契约的有效性和可行性.     

延期支付条件下零售商的最优售价和订货策略

在供应商给予零售商延期支付和现金折扣的优惠政策下,进一步假设产品的年需求量依赖于零售商产品售价的基础上,建立了由一个零售商和一个供应商所构成的库存决策模型,扩展了经典的经济生产批量(EPQ)模型。通过模型的分析求解,可以得出零售商在上述情况下的最优订货周期、最优售价及最优付款时间的简单判定方法。最后,通过算例,验证了模型的可行性,得出了与实际相符的结论。     

终端安全准入控制技术的比较研究

为保障信息化系统的安全和针对各种网络安全威胁,人们开发应用了防火墙、IPS、VPN、杀毒软件等多种软硬件技术,但各种网络安全威胁仍未得到有效解决。为解决这方面的网络安全问题,有安全专家认为安全应该回归终端,以终端安全为核心来解决信息系统的安全问题。     

不透明销售的顾客退货政策选择

本文探讨从事不透明销售的零售商对顾客退货政策的选择问题。分别针对零售商垄断和竞争两种市场情况,建立不透明零售商与其它供应链成员(制造商或普通零售商)之间的博弈模型,获得唯一均衡解;对均衡结果进行结构化分析,给出不透明销售方式下采用全额退款政策的判别条件;针对均衡结果,分析零售商垄断情况下产品不透明参数的最优设计,以及零售商竞争情况下的市场分化情况;鉴于净残值参数在退货政策选择中的决定性作用,本文进一步探讨了净残值为正时全额退款政策对各参与方利润及产品需求和价格的影响,分析了净残值在其中的作用机理。本研究能够为不透明零售商制定退货政策和价格以及其它供应链成员制定相关决策提供支持。     

基于SD-VF理论的城市限行政策效应研究

随着我国城市化进程不断加快,城市交通供需矛盾日益突出,交通拥堵问题愈发严重,我国各地纷纷实行各种政策措施来缓解交通拥堵。本文以北京市为例,针对城市交通拥堵的现象,从系统的角度出发,运用系统动力学与灰色Verhulst预测相结合的方法(SD-VF),建立城市交通治堵模型,并采用Vensim软件对模型进行动态仿真分析,从经济、社会、环境等方面探究政策措施对城市环境生态承载力、道路生态承载力等的影响。研究结果表明,限行限号政策虽然在一定程度上提高道路生态承载力,但也可能引发“悖论”效应;修路政策并没有根本上解决城市交通拥堵问题,仅仅是把拥堵状况向后推迟;而相对于单一政策,将发展公共交通与限行政策相结合可以有效缓解交通拥堵,改善空气质量,提升环境承载力,是更加科学合理的政策措施。     

A periodical replacement model based on cumulative repair‐cost limit

This paper considers a periodical replacement model based on a cumulative repair‐cost limit, whose concept uses the information of all repair costs to decide whether the system is repaired or replaced. The failures of the system can be divided into two types. One is minor failure that is assumed to be corrected by minimal repair, while the other is serious failure where the system is damaged completely. When a minor failure occurs, the corresponding repair cost is evaluated and minimal repair is then executed if this accumulated repair cost is less than a pre‐determined limit L, otherwise, the system is replaced by a new one. The system is also replaced at scheduled time T or at serious failure. Long‐run expected cost per unit time is formulated and the optimal period T* minimizing that cost is also verified to be finite and unique under some specific conditions. Copyright © 2007 John Wiley & Sons, Ltd.