存在借贷资金的随机资金预算问题研究

讨论了企业运用自有资金及银行贷款进行投资时的资金预算问题,与以往的研究不同,本文假设投资支出、年投资收益以及银行贷款都为随机变量,而且,文章的研究并不要求待选的投资项目具有相同的投资期或具有相同的寿命周期.给出了随机环境下净现值收益的期望值模型及期望值目标规划模型,并设计了基于随机模拟的遗传算法,给出了模型的一般解决方法,此外,还提供了两个数值例子,用以说明建模思想,并例证算法的有效性.     

企业技术投资决策模型及其模拟

何时投资购买技术是企业技术战略研究的一个关键问题.传统的技术投资的决策准则是使用净现值(NPV)方法,这种方法会导致企业过早的投资于一项技术,也不能解释企业技术投资时间滞后的现象.企业技术投资时间的滞后可以通过技术本身的不确定性来解释,技术的不确定性主要表现为在技术演化过程中新技术出现的速度和新技术技术效率改进程度的不确定.本文针对企业技术投资中的技术效率决策问题,假设技术出现服从泊松分布、技术改进程度服从均匀分布,构建了技术投资决策模型,并对模型进行模拟,结论表明决策模型比NPV方法能更好的解释企业技术投资决策的行为.     

技术创新与增长期权定价

本采用了净现值(NPV)和实物期权定价方法对一个实际的MMDS的发射放大器项目进行了定价，并对两种方法定价的结果进行了分析和对比。由于实物期权方法定价的结果包含项目中的增长期权和放弃期权的价值，因而实物期权方法定价的结果比NPV方法定价的结果更合理和更高。     

内部收益率准则的再考虑

净现值(NPV)和内部收益率(IRR)准则是投资项目评价中最常用的两个准则.本文在文献[1]用一组公理将 NPV 准则特性化(公理化)的基础上,通过引进两个新的公理,也将 IRR 准则加以特性化.这样,就从本质上明确了 NPV 及 IRR 准则的差别,解释了 NPV 及 IRR 准则导致不同项目排序的原因.本文还对项目的 IRR 指标进行了严格的重新定义,即 MIRR(有意义的 IRR),使其对某特定项目集 N,其中的任一项目都存在唯一的 MIRR.     

资源约束下的投资优化模型

资源约束下的投资问题在决策中很常见.本文提出运用边际净现值比较的思想来解决资源约束下多项目多地区投资决策问题的观点,并且尝试用"区域影响力系数"来衡量区域经济规模对投资项目的影响.在此基础上建立了资源约束下的投资优化模型,并给出解决此类问题的方法.     

投资项目的期权评价与最优投资规则

本文介绍了不确定环境下的投资项目的期权评价方法和最优投资规则，研究了单期项目和连续投资项目的投资决策问题，探讨了实物期权评价方法与传统的净现值评价方法中最优投资规则的差异，并对影响最优投资规则的差异因素进行了敏感性分析，得出了直观而有实用价值的结论。     

净现金流量的一种经验估计方法

研究净现值计算公式的拓展问题.净现值计算中关于项目未来的净现金流量的估计问题至今未能得到很好的解决.本文就此问题,从经验的而非理论论证的角度,利用统计分组的思想,给出了一种估算未来净现金流量的方法,这种方法要比直接估计所得到的未来净现金流量更为接近实际,而此方法操作方便且便于应用,文中给出了算例.     

具有惩罚因子的模糊集约生产计划

研究基于模糊环境下的集约生产计划问题,并设计了带有惩罚因子的模糊优化模型,以实现生产费用和惩罚费用之和最小.通过模糊变量和模糊等式定义的描述,简化了模型,并给出机会约束规划方法进行模型求解的整体步骤.通过仿真结果和灵敏度分析,表明模型和方法的有效性,并为决策者在模糊环境下的决策提供支持.     

Knight不确定及机制转换环境下带通胀的最优投资问题研究

本文研究了投资者在Knight不确定及机制转换环境下带通胀的最优投资决策问题.利用Ito公式、α-最大最小预期效用偏好模型、随机分析等方法,得出了机制转换环境下利润流的动力学方程,Knight不确定及机制转换条件下考虑通胀因素的投资预期价值公式,利润流临界现值及不同参数对投资的影响.     

带有资源柔性约束的max-NPV分布式多项目调度问题

现有的分布式资源约束多项目调度问题研究中,假定全局资源限量在多项目工期内不可突破且多以工期为优化目标。针对此问题,考虑全局资源可从外部获取,以净现值为目标,构建带有全局资源柔性约束的分布式多项目调度问题的整数规划模型并设计有效的求解算法。首先,界定问题并确定项目现金流的计算方法;然后,针对求解问题的NP-hard属性,设计了遗传-模拟退火混合算法(GA_SA)求解此模型。最后,通过多组数值实验,设计不同算法与GA_SA算法进行比较,并分析了关键参数对多项目净现值的影响。结果表明,GA_SA算法具有较好的求解效果;与传统的全局资源刚性约束条件相比,全局资源柔性使用状态可以显著改善分布式多项目的收益绩效。     

Chance constrained integer programming models for capital budgeting in fuzzy environments

This paper attempts to model capital budgeting problems by chance constrained integer programming in a fuzzy environment (rather than a stochastic environment). Some examples are also provided to illustrate the potential applications of new models. Finally, a fuzzy simulation based genetic algorithm is designed for solving chance constrained integer programming models with fuzzy parameters.     

Chance-constrained programming models for capital budgeting with NPV as fuzzy parameters

In an uncertain economic environment, experts’ knowledge about outlays and cash inflows of available projects consists of much vagueness instead of randomness. Investment outlays and annual net cash flows of a project are usually predicted by using experts’ knowledge. Fuzzy variables can overcome the difficulties in predicting these parameters. In this paper, capital budgeting problem with fuzzy investment outlays and fuzzy annual net cash flows is studied based on credibility measure. Net present value (NPV) method is employed, and two fuzzy chance-constrained programming models for capital budgeting problem are provided. A fuzzy simulation-based genetic algorithm is provided for solving the proposed model problems. Two numerical examples are also presented to illustrate the modelling idea and the effectiveness of the proposed algorithm.     

随机环境下资金预算问题的机会约束整数规划模型

本文基于最新的机会约束规划理论,提出了两类随机环境下资金预算问题的整数规划模型,并且设计了一种基于随机模拟的遗传算法来计算给出的模型．为了例证算法的有效性,本文给出了两类模型的数值例子,并且对其中一个例子给出了不同的参数,测试遗传算法的有效性,数值例子及测试结果均显示,本文所设计的基于随机模拟的遗传算法对于解决本文提出的两类模型是有效的．     

A Stochastic Capital Rationing Model

This paper addresses the issue of optimal project selection for capital expenditures assuming uncertain budgetary allocations. A critical review of the historical development of capital budgeting models indicates two major deficiencies: (i) deterministic models ignore the uncertain nature of capital budgeting problems; and (ii) those models which do incorporate the concept of uncertainty have serious computational problems when applied to larger problems. A stochastic capital rationing model (SCRM) is proposed which makes use of recent developments in stochastic programmes with recourse. This model remains computationally tractable despite the explicit incorporation of uncertainty and the application of theoretically sound penalties for constraint violations. Two problems are introduced which illustrate the model's superiority over comparable deterministic formulations. By varying both the probability distributions of the stochastic constraints and the borrowing rates, it was possible to identify the impact these factors have on project selections.     

A mean-risk index model for uncertain capital budgeting

This paper discusses the capital budgeting problem of projects using annual cash inflows, cash outflows and initial investment outlays given by experts’ evaluations when no historical data are available. Uncertain variables are used to describe the projects’ parameters. A profit risk index and a capital risk index are proposed, and a mean-risk index model is developed for optimal project selection. In addition, the deterministic forms of the model are given and a solution algorithm is provided. For the sake of illustration, a numerical example is also presented. The results of the example show that both profit risk index and capital risk index are important in investment risk control. However, when the profit risk control requirement is strong, the selected project portfolio may be insensitive to the capital risk constraint; when the profit risk control requirement is moderate, the capital risk constraint plays an important role. The results also show the tendency that when either the tolerable profit risk level or the tolerable capital risk level becomes higher, the obtained expected net present value of the project portfolio becomes larger, which is in agreement with the investment rule that the higher the risk, the higher the return.     

Logistics distribution centers location problem and algorithm under fuzzy environment

Distribution centers location problem is concerned with how to select distribution centers from the potential set so that the total relevant cost is minimized. This paper mainly investigates this problem under fuzzy environment. Consequentially, chance-constrained programming model for the problem is designed and some properties of the model are investigated. Tabu search algorithm, genetic algorithm and fuzzy simulation algorithm are integrated to seek the approximate best solution of the model. A numerical example is also given to show the application of the algorithm.     

Portfolio selection based on fuzzy cross-entropy

In this paper, the Kapur cross-entropy minimization model for portfolio selection problem is discussed under fuzzy environment, which minimizes the divergence of the fuzzy investment return from a priori one. First, three mathematical models are proposed by defining divergence as cross-entropy, average return as expected value and risk as variance, semivariance and chance of bad outcome, respectively. In order to solve these models under fuzzy environment, a hybrid intelligent algorithm is designed by integrating numerical integration, fuzzy simulation and genetic algorithm. Finally, several numerical examples are given to illustrate the modeling idea and the effectiveness of the proposed algorithm.     

基于熵的投资组合模糊优化模型

本文对基于信息熵的证券投资组合模型，根据模糊决策理论，在模糊环境下对模型进行求解，将投资者的主观意见反映在模糊情况的组合投资模型中，并通过实例，验证了该模型解法的可行性和有效性。