一类马氏过程在作战分析中的应用

本文应用随机过程的理论和方法定义了一类特殊的作战过程——纯灭战斗过程,研究并导出了该战斗过程的作战实力转移特征等．     

一类马氏过程在作战分析中的应用

本应用随机过程的理论和方法定义了一类特殊的作战过程——纯灭战斗过程,研究并导出了该战斗过程的作战实力转移特征等。     

基于灰色神经网络的航空装备作战携行量预测

当前局势多变且节奏迅速的现代高新技术战争使得航空装备需求量与损耗率激增,航空装备保障人员也面临着战时保障情况复杂、决策难度增加的问题.在BP神经网络的基础上使用灰色理论对其进行了优化,将得到的灰色BP神经网络对航空装备作战携行数量进行了预测并与一般BP神经网络和GM(1,1)模型预测结果对比.结果表明:灰色BP神经网络预测精度高、收敛速度快、所需样本数据少,对航空装备作战携行数量预测具有重要价值.     

模糊综合评判在步枪作战效能评估中的应用

从20世纪80年代至90年代开始,世界各主要军事大国均研制了本国的新一代步兵战斗用枪.本文应用模糊综合评判方法对两代(共10种)冲锋枪和自动步枪作战效能的评估和排序进行了讨论,并对其作战效能和技术水平进行了判断.所得结论对了解我军装备的先进程度,适应新时期军事科学和战备具有特别重要的意义.     

"兰氏"平方律作战毁伤过程兵力损耗率系数战例求解理论

如何求得著名的“兰氏”平方律战斗动力学方程中双方兵力损耗率系数，这是作战模拟应用研究领域中一个久攻未克的难题。本文提出了以作战结果来逆向研究作战过程中双方兵力损耗率系数的思想。阐述了对于不变的作战双方在相同（相近）的作战环境与作战条件下相继进行的作战序列里，双方各自的兵力损耗率系数不变（波动不大）的公理，论证了揭示作战序列内部规律的两条定理。据此，建立了兵力损耗率系数的战例求解理论与方法。运用这一理论与方法，据以往发生的作战过程其数值特征可以求得未来相似或相同作战过程中双方兵力损耗率系数的具体取值，首次解决了作战模拟研究领域中兵力损耗率系数的具体取值这一难题。     

单兵种对双兵种作战模型

本文讨论了单兵种对双兵种作战的 Lanchester模型 ,分析研究了战斗进程中各方兵力的动态变化 ,给出了单兵种方的最优兵力分配原则 .     

作战时耗指派矩阵取胜指派矩阵兵力耗损指派矩阵的一体构造

指派矩阵构造是指派问题应用研究的难点,在作战应用领域展开指派矩阵构造专题研究.文中回望了1914年Lanchester关于"兰氏"平方律作战过程取胜条件与剩余兵力的分析结果,以及1996年本文第一作者提出的关于"兰氏"平方律作战过程存在胜负的情况下其作战持续时间计算的数学模型,提出了关于"兰氏"平方律作战过程在作战双方势均力敌的情况下作战持续时间的数学模型.综合运用上述的已有理论与新建理论,建立了取胜矩阵、时耗矩阵、兵力耗损矩阵的一体构造模型.该一体构造模型从作战系统的4类可知数据出发,对于具体的多部队参战的作战过程均能构造出具体的取胜、时耗、兵力耗损数值矩阵.最后给出了取胜、时耗、兵力耗损矩阵的一个一体构造实例,并运用(n×m)-k缺省指派问题理论对该实例求得了其最多K胜条件下的最短时限最少耗费缺省指派最优解.     

面向多阶段任务的武器系统备件优化配置建模

资源优化配置是作战单元维修保障的关键因素.当作战单元执行单阶段任务时,讨论了部件结构为串联系统和k/n系统的任务成功概率建模问题,在此基础上,建立了k/n结构动态变化的多阶段任务系统的任务成功概率模型.在满足系统任务成功概率约束条件下,给出了防空作战单元备件携行量优化模型,并运用边际分析法进行了求解,通过示例表明了模型的正确性.     

模糊综合评判地空导弹武器系统作战效能的一种方法

为科学评估地空导弹武器系统的作战效能,对地空导弹武器系统进行了全面分析,建立了地空导弹武器系统作战效能综合评价的指标体系.并利用模糊数学方法,确立了该系统的综合评价模型,从而为地空导弹武器系统的总体作战效能的综合评价提供了一种方法,具有一定的参考价值.     

一种基于复杂性理论的装备作战仿真方法

采用什么样的计算机仿真方法来隐喻真实的作战系统是装备作战仿真研究的关键问题.从复杂性科学的研究角度,引入了复杂适应系统(CAS)理论及其技术体系,提炼了基于Agents/space的建模与仿真框架,说明了框架实现的关键技术——可计算模型、复杂性解决方案和仿真实现平台.进而利用该方法进行了典型装备作战仿真问题研究,包括:利用神经网络、三维连续空间可计算模型,并选用Mason平台实现了装甲装备战损规律仿真;利用三层元胞自动机、产生式系统可计算模型,并选用Repast平台实现了装备群对抗仿真.为基于复杂性理论开展装备作战仿真或具有类似特征问题的研究提供了一种新的试验途径.     

考虑外部效应与风险因素的多项目优化模型及应用

多项目的组合优化与资源分配优化,实质上是经济学中资源配置与利用问题.运用经济学与管理学的相关理论和定量研究方法,建立了外部效应估算模型、项目评价模型、风险估算模型、多项目组合优化模型和关于资源分配优化的两层决策模型.相关模型的应用实例研究,说明了该模型的有效性.     

Project portfolio designing using data envelopment analysis and De Novo optimisation

In a rapidly evolving economic world, projects become tools to support organization goals. Project portfolio is set of all projects that are implemented in the organisation at a time. Possible projects are characterized by sets of inputs and outputs, where inputs are resources for project realisation and outputs measure multiple goals of the organisation. The data envelopment analysis (DEA) is an appropriate approach to select efficient projects. The organisation has its total resources in limited quantities. Designing a portfolio of efficient projects not exceeding the limited resources does not always lead to the most efficient portfolio. De Novo optimisation is an approach for designing optimal systems by reshaping the feasible set. The paper proposes a new approach for project portfolio designing based on a systemic combination of DEA model and De Novo optimisation approach. A total available budget is a restriction on project portfolio. The proposed concept provides designing of optimal project portfolio with the minimal budget. Performance measures of the designed project portfolio are the efficiency of the portfolio and the effectiveness of outputs. Possible extensions of the concept are formulated and discussed.     

基于交互效应的项目组合风险度量及选择优化

研究了具有不对称交互效应的项目组合风险测度和选择问题。从资源、收益和技术三方面描述了项目组合交互效应的产生,提出了基于生态位和生态位重叠理论的具有交互效应的项目组合风险度量方法,建立了以收益最大化和风险最小化的组合选择优化模型,并针对该模型给出了一种实用的混合遗传算法和应用实例。研究表明交互效应对组合风险度量和项目选择具有显著影响,这也为项目组合研究提供了新的视角和思路。     

心理账户、损失厌恶与行为资产配置实证研究

基于前景理论和三参照点理论,建立了单心理账户和三心理账户下的线性损失厌恶行为投资组合模型,并利用中证基金指数数据构建了不同市场状态下的行为投资组合,实证研究不同损失厌恶系数、不同参照点、不同心理账户资金配置条件下模型的最优资产配置策略和投资组合绩效,研究发现线性损失厌恶模型更关注下侧损失,损失厌恶系数影响资产配置,注重安全性的投资者偏好低风险资产,而寻求实现抱负水平的投资者更偏好高收益资产。     

Uncertain portfolio optimization problem under a minimax risk measure

Portfolio optimization problem is concerned with choosing an optimal portfolio strategy that can strike a balance between maximizing investment return and minimizing investment risk. In many cases, the return rate of risky asset is neither a random variable nor a fuzzy variable. Then, it can be described as an uncertain variable. But, the existing works on uncertain portfolio optimization problem fail to find an analytic solution of optimal portfolio strategy. In this paper, we define a new uncertain risk measure for the modeling of investment risk. Then, an uncertain portfolio optimization model is formulated. By introducing a new variable, we transform it into an equivalent bi-criteria optimization model. Then, we derive a method for the construction of the set of analytic Pareto optimal solutions. Finally, a numerical simulation is carried out to show the applicability of the proposed model and the convenience of finding the analytic solution.     

An a posteriori decision support methodology for solving the multi-criteria supplier selection problem

This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.     

Optimal payoff under the generalized dual theory of choice

We consider portfolio optimization under a preference model in a single-period, complete market. This preference model includes Yaari’s dual theory of choice and quantile maximization as special cases. We characterize when the optimal solution exists and derive the optimal solution in closed form when it exists. The optimal portfolio yields an in-the-money payoff when the market is good and zero payoff otherwise. Finally, we extend our portfolio optimization problem by imposing a dependence structure with a given benchmark payoff.     

Robust portfolio optimization with a hybrid heuristic algorithm

Estimation errors in both the expected returns and the covariance matrix hamper the construction of reliable portfolios within the Markowitz framework. Robust techniques that incorporate the uncertainty about the unknown parameters are suggested in the literature. We propose a modification as well as an extension of such a technique and compare both with another robust approach. In order to eliminate oversimplifications of Markowitz’ portfolio theory, we generalize the optimization framework to better emulate a more realistic investment environment. Because the adjusted optimization problem is no longer solvable with standard algorithms, we employ a hybrid heuristic to tackle this problem. Our empirical analysis is conducted with a moving time window for returns of the German stock index DAX100. The results of all three robust approaches yield more stable portfolio compositions than those of the original Markowitz framework. Moreover, the out-of-sample risk of the robust approaches is lower and less volatile while their returns are not necessarily smaller.     

Single-Period Markowitz Portfolio Selection,Performance Gauging,and Duality: A Variation on the Luenberger Shortage Function

The Markowitz portfolio theory (Ref. 1) has stimulated research into the efficiency of portfolio management. This paper studies existing nonparametric efficiency measurement approaches for single-period portfolio selection from a theoretical perspective and generalizes currently used efficiency measures into the full mean-variance space. We introduce the efficiency improvement possibility function (a variation on the shortage function), study its axiomatic properties in the context of the Markowitz efficient frontier, and establish a link to the indirect mean-variance utility function. This framework allows distinguishing between portfolio efficiency and allocative efficiency; furthermore, it permits retrieving information about the revealed risk aversion of investors. The efficiency improvement possibility function provides a more general framework for gauging the efficiency of portfolio management using nonparametric frontier envelopment methods based on quadratic optimization.     

基于EEMD与模糊回归的投资组合选择模型及实证

随着模糊理论的不断发展与其在证券市场的广泛应用,越来越多的学者关注到参数模糊化对投资组合优化具有重要作用。本文利用集合经验模态分解(EEMD)和模糊线性回归相结合的预测方法,构建了基于对称三角模糊数的投资组合模型。并将提出的模型与集合经验模态分解和普通最小二乘结合的方法、单一模糊线性回归方法进行了对比分析,结果表明基于集合经验模态分解和模糊线性回归建立的投资组合模型最优,这对构建最优投资组合具有参考意义。