人力资本与教育的随机扰动模型

在一个带有生产扰动和公共支出扰动的随机模型中,把教育的产出--人力资本引入效用函数和生产函数,利用随机最优化方法,确定了最优经济增长率和最优个体教育投资率.通过分析参数,得出了最优税率.     

公共资本投资的随机内生增长模型

本文假设公共资本是宏观经济的一部分,由此建立一个随机内生增长模型,把公共资本作为宏观经济变量并进入个人效用.分析了经济达均衡时税收,政府投资,个人投资对经济增长率和社会福利的影响.同时还求出子最优的增长率和个人资本与财富比,消费和财富比.     

人力资本投资的随机内生增长模型

本文假设人力资本是宏观经济的一部分 ,由此建立一个随机内生增长模型 ,把人力资本作为宏观经济变量并进入个人效用 .分析了经济达到均衡时个体偏好 ,不确定性对均衡增长率的影响 .同时分析了财政政策 ,税率对经济增长的影响 ,还求出了最优的增长率和人力资本投入与物质资本比 .     

A Stochastic Petri Net Approach for Inventory Rationing in Multi-Echelon Supply Chains

Manufacturing supply chains are considered as discrete event dynamical systems (DEDS) where coordination of material and information flows is essential to satisfy customer orders and to improve the bottomline of the constituent organizations. A critical problem that is often faced by distribution centres that hold finished good inventory is that of inventory rationing. Inventory rationing is a useful strategy to tackle the problem of conflicting objectives i.e., minimizing inventory costs (holding and backorder) on the one hand and achieving the desired customer service levels (CSLs) on the other. The focus of this paper is to formulate Generalized Stochastic Petri net models to address the inventory rationing problem in the context of multi-echelon make-to-stock distribution chains, where the goods flow through multiple echelons, typically from product manufacturers all the way up-to the retail outlets. The statistical inventory control (SIC) policies modeled by the GSPN are (R, s, S) and a variant that we propose, (R^∗, s, S). We compare the performance of the model under two rationing settings. The first setting considers a case without cooperation, where the individual local stockpoints maximize their own performance. The second setting considers a case with cooperation, where the local stockpoints cooperate with each other to maximize the overall system performance. We provide a methodology to approximately determine the optimal rational fractions with different weights assigned to expected backorder and holding cost components (b/h). We present some interesting results obtained after rigorous numerical experimentation on the model.     

A Stochastic Model for Bidding

Competitive bidding situations involve considering a multiplicity of factors. Organizations must be able to weigh the relative probability of potential projects based on resource usage, project duration and competitor actions to decide which of many possible bids to submit. A bidding strategy designed to maximize expected long run return is crucial, since an organization can usually submit only one bid per project.This paper presents a family of stochastic dynamic programming models considering different bidding situations. Several projects, each with several potential bids, are available for each situation. The objective is to determine what bidding strategy will maximize expected returns. Models are developed for two principle bidding situations: sequential, where projects are bid individually; and simultaneous, where several projects are bid at one time. Next, the effects of over- or under-commitment of resources are incorporated into the models. Finally, changes in project timing and the resultant effects on bidding strategy are included.A numerical example traces the changes in bidding strategy which occur as the models are expanded. The general formulation of bidding problems is also discussed, including changing the bid success probabilities due to competitor actions, the possibility of crashing projects and alternate methods of performing projects.     

A Stochastic Model for Auditing

Auditing is based on random sampling from the records of a company. This paper considers a Bayesian model for determining the sample sizes by finding a balance between the cost of sampling and the risk of leaving major faults undiscovered. It leads to a dynamic programming problem which involves substantial computations, but a slightly different approach in which discrete sampling is replaced by a continuous search for faults produces more explicit solutions.     

基于随机模型的区域产业资本漂移研究

资本是产业系统的"血液",是产业实现可持续发展的重要内因变量.在利益驱动下,产业资本会随机地发生漂移.采用Markov方法研究区域产业资本漂移这一随机过程,利用产业资本的转移概率得到了产业资本周转时间的均值和方差,也给出了资本支持某产业发展时间的均值和方差,进而推导出了产业资本的产出率和产出值的计算方法,并进行了实证分析.     

基于资本约束的资本机会成本定价研究

资本约束与信息不对称密切相关 ,当企业内部存在信息不对称时 ,企业实施内部资本约束 ,此时 ,由于外部资本市场能够产生正确的资本机会成本信号 ,净现值准则可以用于项目选择 .而当企业外部资本市场存在信息不对称时 ,资本提供者会实施外部资本约束 ,由于信息不对称歪曲了资本机会成本的市场信号 ,净现值准则失效 ,此时 ,机会成本的定价研究需要引进效用函数及主观资本回报率等工具 .     

药动学随机过程模型

用随机过程的方法 ,建立了一种用于分析药物在体内吸收、分布、消除的模型 ,推导了该模型的高阶转移概率矩阵 ,并对应用问题进行了讨论 .     

基于可靠性的随机拥挤收费模型

提出了一种需求不确定环境下的基于可靠性的拥挤收费模型.在需求不确定的环境下,出行时间的变动使得无论是出行者还是网络决策者都面临着风险.出行者需要提高出行的可靠性以避免迟到,决策者需要在缓解拥堵的同时提高网络整体的可靠性.以基于路段的平均超额出行时间作为出行者的路径选择准则,以平均超额系统总阻抗最小作为决策者拥挤收费的目标,建立了具有均衡约束的数学规划模型.提出了模型能分析出行者的路径选择行为和决策者的决策过程中的可靠性,同时充分考虑迟到等不可靠因素的影响,全面刻画不确定环境下路径出行时间和系统总阻抗的分布特征.数值算例表明,忽略不确定性的影响可能会得到对于网络整体性能过于乐观的评价,得到脱离实际的收费方案.网络的不确定性越高,对于网络性能和收费方案的影响就越大,从而考虑网络可靠性和出行者路径选择的可靠性的必要性就越大.此外,以平均超额系统总阻抗作为拥挤收费的目标可以进一步改善网络的可靠性.     

随机的改进Lotka-Volterra竞争模型

本文提出且讨论了一类两种群随机的改进Lotka Volterra竞争模型.白噪声及有色噪声都在本文中被考虑.我们得到了全局唯一正解、随机有界性、随机持久和随机灭绝等种群动力性质的充分条件.     

考虑环境污染的随机经济增长模型

本文主要考虑了环境污染对个体福利的负面效应,把环境污染水平纳入效用函数,由此建立了一个随机经济增长模型.分析了经济均衡时税收,政府环保投资,私人环保投资对经济增长率和社会福利的影响.同时还求出了最优的增长率和个人资本与财富比,消费和财富比.     

关系型信贷:一个基于信贷配给的模型

通过一个基于信贷配给的模型,揭示了为什么中小企业融资困难.而关系型信贷是解决"信贷配给"矛盾的一种方式.关系型借贷是市场交易的一种内生制度.这里的关系反映的是企业和一家基本银行(或少数几家银行)建立的长期封闭及规范化的契约关系,他的维系有助于银行收集关于企业发展前景和贷款偿还概率等方面的信息,进而为做出贷款决策提供便利.对于中小企业来说,也能够便利的获得所需的贷款.     

Stock Rationing in a Continuous Review Two-Echelon Inventory Model

In this paper we consider a 1-warehouse, N-retailer inventory system where demand occurs at all locations. We introduce an inventory model which allows us to set different service levels for retailers and direct customer demand at the warehouse. For each retailer a critical level is defined, such that a retailer replenishment order is delivered from warehouse stock if and only if the stock level exceeds this critical level. It is assumed that retailer replenishment orders, which are not satisfied from warehouse stock, are delivered directly from the outside supplier, instead of being backlogged. We present an analytical upper bound on the total cost of the system, and develop a heuristic method to optimize the policy parameters. Numerical experiments indicate that our technique provides a very close approximation of the exact cost. Also, we show that differentiating among the retailers and direct customer demand can yield significant cost reductions.     

随机捕食——被捕食模型

本文建立了一类二维非线性生灭捕食--被捕食模型.利用概率母函数, 我们得到其均值过程所满足的偏微分方程组,并证明了捕食者和被捕食者以概率1灭绝.     

A Stochastic Model of Sugar Cane Crop Rotation

An identifying model of sugar cane crop rotation has been developed to optimize return to a single sugar cane farm by scheduling harvesting times and implicitly defining the crop growth periods. A Markov decision process is used to provide a simple structure which retains the problem's dynamic nature. Stochastic influence on the system is incorporated within this structure by defining states representing crops at discrete quality levels with probabilistic transitions between states. Non-discounted and discounted versions of the Markov process are solved using a linear programming formulation. Problem formulation and interpretation of the solution is complicated by an extra constraint on the Markov process due to sugar industry restriction. This constraint is treated by two different means and a rule is presented to aid its interpretation.     

股票市场监管策略的一个随机模型

研究了股市在一段有可能给社会造成危害的上涨行情中，管理者监管股市的问题＋根据股市的运行规律，建立了一个随机最优化模型，讨论了参数对解的影响，并得出了一些对股市监管有意义的结论．     

研究胆结石形成的一个随机模型

根据研究胆结石成因的实验中所形成的层状结构，我们借鉴于随机伊辛模型的思想提出了胆结石形成的一个随机模型。