随机环境下技术开发和授权的最优策略

技术创新的价值实现需要后续的商业化投资,在随机不确定的市场环境下,创新企业面对着投资或等待、自身开发或技术授权的策略选择.利用实物期权方法建立了市场不确定时的决策模型,分析开发能力、不确定性等多种因素对企业授权的影响,在多种情况下得到企业的最优决策并对相关现象给出了解释.研究发现不确定性提高了企业投资的临界值,提高了企业的技术授权价格,高度的不确定性减少了技术授权的可能,较高的能力差异会促进技术交易.     

随机扰动下的库存投资决策模型

本文讨论的是库存投资的最优决策问题.不同于确定性q理论,对于引入了市场不确定性扰动的库存控制系统,文章建立了库存投资随机优化决策模型.从市场利率波动的角度对库存决策模型进行分析,得出的结论是:小的市场利率的扰动能够提高企业折现利润的预期,进而导致公司库存投资的上升.     

海外区域技术平台投资的时机选择模型及实证研究

基于后发企业海外区域技术平台(RTP)投资中“成熟技术产品推广”和“新技术产品开发”两个阶段,本文构建了两阶段实物期权模型。进一步,利用中国制造业对外直接投资(OFDI)的上市公司样本,考察了RTP投资时机选择的决定因素及影响效果。研究发现,第一阶段外生不确定性越低、第二阶段内生不确定性越高、新技术产品项目的增长期权越大,企业越倾向于尽早地RTP投资于海外市场;在两阶段内外生不确定性的不同条件下,RTP投资时机相应地对企业创新增长产生了显著的差异影响。     

奈特不确定条件下企业有成本可逆投资策略研究

风险是企业投资决策关键影响因素之一,采纳奈特不确定性来刻画风险,并在此基础上构建了模糊规避偏好和投资有成本可逆条件下企业投资决策模型.模型结果表明企业最优投资策略为双阈值策略:企业增加投资以避免资本边际收益大于上限阈值,削减资本存量以避免资本边际收益低于下限阈值,当资本边际收益处于上下限阈值之间时,企业既不增加投资也不削减资本.比较静态分析显示奈特不确定性增加会降低最优投资策略上下限阈值范围.     

基于空间滞后随机前沿模型技术效率的估计

为了刻画潜在的空间效应,本文提出了一类空间滞后随机前沿模型.在单边误差项分别服从半正态分布、指数分布及截尾正态分布的情况下,我们给出了模型的对数似然函数及迭代步骤.此外,应用JMLS方法给出了单边误差项的条件均值与条件众数,因而得到了相应的技术效率估计.     

“政策市”下的中国企业实物投资机理研究

在我国市场经济发展的过程中,某些具有不确定性和投资不可逆性的产业市场容易出现投资过热问题.这时政府往往频繁出台一系列宏观调控政策以规范市场,但效果不佳.为此从研究该类市场的投资主体—企业的投资决策机理的角度出发,通过引入实物期权的理论,给出了在完善和平稳发展的市场条件下及在政府政策冲击的市场条件下,价格分别服从单纯的几何布朗运动及混合的几何布朗运动/泊松跳跃过程的企业最佳投资规则及其临界价格,并进行了比较,结果表明:“政策市”下的企业投资更富“冒进性”.     

库存控制的随机优化(英文)

本文将随机扰动引入企业库存控制系统,并重点讨论了扰动引入后,库存投资与市场需求的相互作用,及对未来折现利润的相关影响.文章建立了关于库存控制管理的最优跨时决策模型.不同于其它文献中将市场需求及库存投资看作具有具体分布的随机变量,文章将两者设置为随机过程并分别服从不同的It型SDE.文章得出了三个命题及一些有意义的结果.     

面向付费类交通PPP项目的价格设计与补贴动态调整方案研究

针对付费类交通PPP项目,在通行需求服从跳跃扩散过程且调整成本不可忽略的实际背景下,研究价格和补偿的动态调整方法及其调整条件的预先设置问题.首先利用NPV确定特许期、价格等并分析交通通行需求的波动;然后提出动态调整方法,即在特定的时间周期内交通需求量持续突破上限或下限时,价格和补贴水平随之及时联动调整,使收益恢复到预期水平;以实际收益偏离预期最小为目标,建立调整条件决策模型,通过蒙特卡洛模拟并与遗传算法结合,寻找最优的调整条件,从而给出动态调整机制.最后通过一个实例验证了该方法的可行性和适用性,其结果显示,运营期中调整价格和补贴是保证私人部门合理收益的有效途径.     

不确定性量化法与不确定性决策

以不确定性信息的数学处理理论即不确定性数学理论为基础 ,阐述了专家意见的不确定性量化法 ,进而创立了不确定性决策模型 .最后通过举例 ,详细地演示了不确定性决策模型的建立过程 .     

新兴产业两阶段期权规划决策模型

新兴产业中处于横向竞争地位的初创企业,会呈现渐变和突变两种不同的演化状态。不同状态下的横向竞争企业,价值变化都具有高度的不确定性。为了降低投资决策的不确定性风险,获取稳定的投资收益,对处于渐变和突变状态下的初创企业,首先运用期权组合的方法,进行第一阶段的变量预估决策;然后依据期权投资的收益情况,再运用线性规划技术进行第二阶段的补偿优化决策。论文通过阿里巴巴、京东、苹果和诺基亚四家样本公司的数据,检验了两阶段期权规划决策模型的实际效果,结论显示该方法能降低新兴产业投资中的不确定性干扰,在获取稳定收益的同时,使风险处于可控状态。     

IMPACTS OF WATER SUPPLY UNCERTAINTY AND STORAGE ON EFFICIENT IRRIGATION TECHNOLOGY ADOPTION

In the framework of a stochastic dynamic programming model, the paper investigates the impact of water supply uncertainty and storage at farm level on adoption of efficient irrigation technologies under a flexible water price regime. We find that even a flexible water pricing cannot guarantee higher adoption of efficient irrigation technology in all cases. Results of the paper indicate that if a farmer invests in water storage capacity, then the value of efficient usage of water increases, and the rate of adoption of efficient irrigation technology will be higher. It establishes a complementarity relationship between investments in storage capacity and adoption of efficient irrigation technology. The relationship becomes stronger with increasing variance in water supply. In a situation without any option to store water at the farm level, we find a negative relationship between investment in efficient irrigation technology and water variability. However, numerical analysis results suggest that a risk averse farmer may invest more in efficient irrigation only if the variance in water supply is very high.     

技术不确定环境下新产品策略的设计应用

技术的动态发展和企业间的竞争对企业新产品策略有很大影响,直接决定新产品的引进周期。本文在产业技术动态变化的随机环境下构建随机动态规划模型,关注产业技术进步、投资成本和产品市场竞争等影响因素,探讨企业进行新产品引进的周期选择,对新产品引进的周期和质量决策进行方法设计和应用举例。利用随机动态规划模型得出新产品引进的最优时间周期,用算例分析技术进步和产品研发成本对企业引进周期策略的影响,采取策略迭代的方法进行求解,发现技术进步较快时企业的新产品引进步伐也较快,研发成本的提高使企业的新产品引入步伐降低。     

Robust planning of blending activities at refineries

Refinery operation planning is a complex task since refinery processes and inventories are tightly interconnected. We study refinery planning when ships are loaded with a blend of components and where arrival times of ships are uncertain. Any delay in ship arrival may result in overfull component tanks which results in less efficient blending alternatives, reduced process operations or even shut downs. We propose a planning approach where we use robust optimization as a decision tool. By using robust optimization uncertainty in arrival times is explicitly dealt with and the resulting plan and schedule will always be feasible. The approach includes a flexible way to describe and model uncertainties. To compare the robust approach with a traditional deterministic approach, we use a simulation process. Computational results from a case study and simulations show that the proposed methodology is substantially better than a deterministic approach.     

Supplier quality improvement: The value of information under uncertainty

We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty. We develop a Poisson–Gamma model within a Bayesian framework, representing both the epistemic and aleatory uncertainties in non-conformance rates. Estimates are obtained to value a supplier quality improvement activity and assess if it is worth gaining more information to reduce epistemic uncertainty. The theoretical properties of our model provide new insights about the relationship between the degree of epistemic uncertainty, the effectiveness of development programmes, and the levels of investment. We find that the optimal level of investment does not have a monotonic relationship with the rate of effectiveness. If investment is deferred until epistemic uncertainty is removed then the expected optimal investment monotonically decreases as prior variance increases but only if the prior mean is above a critical threshold. We develop methods to facilitate practical application of the model to industrial decisions by a) enabling use of the model with typical data available to major companies and b) developing computationally efficient approximations that can be implemented easily. Application to a real industry context illustrates the use of the model to support practical planning decisions to learn more about supplier quality and to invest in improving supplier capability.     

The impact of fat tailed returns on asset allocation

This paper analyzes the asset allocation problem of an investor who can invest in equity and cash when there is time variation in expected returns on the equity. The solution methodology is multistage stochastic asset allocation problem with decision rules. The uncertainty is modeled using economic scenarios with Gaussian and stable Paretian non-Gaussian innovations. The optimal allocations under these alternative hypothesis are compared. Our computational results suggest that asset allocation may be up to 20% different depending on the utility function and the risk aversion level of the investor. Certainty equivalent return can be increased up to .13% and utility can be improved up to .72% by switching to the stable Paretian model.     

现金押运路线问题——基于多类型的风险

以人民币现金押运为研究背景,考虑了一种基于多类型风险的现金押运路线问题,以在途风险成本、库存现金风险成本以及运输成本为优化目标,建立了混合整数线性规划模型,并提出了一种基于多样化策略和改进邻域搜索的混合遗传算法,其中遗传算法对押运路线进行选择,贪心算法用来求解各类风险指标。数值实验分别对问题特性和算法性能进行了分析。实验结果表明:1)混合遗传算法能求解更大规模的问题,得到较好的解,并很好地平衡了运行时间和求解质量;2)多类型风险影响了行驶路线;3)客户的期望需求影响了库存现金风险。     

Staffing call centers under arrival-rate uncertainty with Bayesian updates

We consider the problem of staffing service centers with quality-of-service constraints. We focus on the case where the arrival rates are uncertain. We introduce formulations that handle staffing decisions made over two decision periods, minimizing the staffing costs over the stages while satisfying a service quality constraint on the second stage operation. A Bayesian update is used to obtain the second-stage arrival-rate distribution based on the first stage prior arrival-rate distribution and the observations in the first stage.     

Technology choice under several uncertainty sources

We analyze a model of irreversible investment with two sources of uncertainty. A risk-neutral decision maker has the choice between two mutually exclusive projects under input price and output price uncertainty. We propose a complete study of the shape of the rational investment region and we prove that it is never optimal to invest when the alternative investments generate the same payoff independently of its size. A key feature of this bidimensional degree of uncertainty is thus that the payoff generated by each project is not a sufficient statistic to make a rational investment. In this context, our analysis provides a new motive for waiting to invest: the benefits associated with the dominance of one project over the other. As an illustration, we apply our methodology to power generation under uncertainty.