基于可信性分布的投资组合问题的一种混合智能方法

研究了模糊环境下基于效用函数的有效资产投资组合的收益率模型,模型建立在可信性分布的基础上,而不是概率分布或可能性分布基础上.给出模糊环境下基于可信性分布的n种资产的最优投资组合问题的混合智能算法以寻找某种效用函数意义下的最优组合.并以实例仿真说明该方法的有效性.     

模糊环境下批量制造业提前/拖期交货的生产计划

描述了基于客户需求为模糊量的批量生产提前/拖期交货的生产计划,并建立了模糊环境下的三个模型.为了有效求解优化模型,我们将模糊模拟和遗传算法相结合给出了混合智能算法.最后通过数值例子说明算法的有效性.     

模糊随机环境下带个人偏好的投资组合决策模型

在现实的证券市场中,存在许多混合不确定性因素对证券的收益率产生影响.本文的目的是建立糊随机环境下带个人偏好的投资组合决策模型.在模糊随机环境下将证券的收益率视为模糊随机变量,同时考虑到投资者的偏好,提出入均值和投资者的风险曲线的概念,他们分别反映投资组合的收益和风险.文中给出新的入均值有效投资组合和入均值有效前沿的概念,探讨新的投资组合的收益率与偏好参数入的关系;最后本文采用混合智能算法进行实例分析,结果表明本文所提模型是可行性的.     

基于模糊收益率的投资组合模型构建及应用

投资组合理论是现代化投资管理活动的理论支持,广泛应用于证券投资领域.将模糊集理论与投资组合理论相结合,建立基于可能性理论和机会测度的投资组合模型,并用混合智能算法对模型求解.选取上证50指数成分股近两年的交易数据对模型进行实证分析.结果显示,模型构建的投资组合收益率优于经典模型收益率和上证50指数同期收益率,模型显著有效.     

证券组合的模糊规划模型

通过结构元方法定义了一种模糊数排序准则,利用模糊约束将Markowitz投资组舍模型转化为模糊线性规划模型,并利用模糊数来描述证券的期望收益率和风险损失率,建立模糊数模糊证券投资组合模型.最后,利用定义的模糊数排序准则把模糊数规划问题转化为经典的线性规划问题,然后再对该模型进行求解,并通过算例阐述了该方法的有效性.     

模糊投资组合选择问题的改进帝企鹅优化算法

模糊投资组合选择问题是在基本投资组合模型中引入模糊集理论,使所建立的模型与实际市场更加吻合,但同时也增加了模型求解难度.因此,本文针对两种不同的模糊投资组合模型,提出一种改进帝企鹅优化算法.算法首先引入可行性准则,处理模糊投资组合模型中的约束.其次,算法中加入变异机制,平衡算法的开发和探索能力,引导种群向最优个体收敛.通过对CEC 2006中的13个标准测试问题及两个模糊投资组合问题实例进行数值实验,并与其他群智能优化算法进行结果比较,发现本文所提出的算法具有较好的优化性能,并且对于求解模糊投资组合选择问题是有效的.     

模糊环境下动态供应商选择的混合整数非线性规划模型

基于供应商选择问题的动态性和模糊性,考虑在每个周期内生产商的需求能力及供应商的供应能力为模糊变量,本文将一个多阶段多商品多渠道的供应商选择问题视为一个0-1混合整数模糊动态非线性规划问题,目标函数为总成本最小化。然后建立了0-1混合整数模糊动态非线性规划模型。为了求解该模型,通过可信性理论把模型中模糊机会约束清晰化,将该模型转化为一个确定型的0-1混合整数动态非线性规划模型。最后给出了一个数值算例验证了模型的可行性。     

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。     

应急物资预置问题的可信性优化模型

基于可信性理论,研究了多受灾点、多出救点、多物资的应急设备选址和物资预置问题.考虑到运输费用、出救点的供应量、受灾点的需求量和道路容量的不确定性,用模糊变量来刻画,建立了模糊环境下应急物资预置的可信性优化模型以最小化期望总费用.当模型中的模糊变量相互独立且服从三角分布时,推导了总费用目标及服务质量和弧容量约束的解析表达式,从而将原模型转化为等价的确定模型.鉴于等价模型是一个混合整数规划,可采用Lingo软件编程求解.最后,数值算例演示所提建模思想.实验结果说明了所建模型的有效性.     

基于最小交易量和交易费用的多阶段模糊投资组合

考虑了在摩擦市场下的多阶段模糊投资组合模型,基于半绝对方差风险函数,建立了带有最小交易量和交易费用限制的收益最大化多阶段模糊投资组合模型.利用绝对值函数的性质,将模型转化为混合整数线性规划形式,并通过实例验证了模型的可行性,最后对模型与基于可能性均值和可能性方差的多阶段模糊投资组合模型进行了对比,分析了模型的优越性,并验证了模型的可行性.     

A review of credibilistic portfolio selection

This paper reviews the credibilistic portfolio selection approaches which deal with fuzzy portfolio selection problem based on credibility measure. The reason for choosing credibility measure is given. Several mathematical definitions of risk of an investment in the portfolio are introduced. Some credibilistic portfolio selection models are presented, including mean-risk model, mean-variance model, mean-semivariance model, credibility maximization model, α-return maximization model, entropy optimization model and game models. A hybrid intelligent algorithm for solving the optimization models is documented. In addition, as extensions of credibilistic portfolio selection approaches, the paper also gives a brief review of some hybrid portfolio selection models.     

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean–variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.     

Dynamic pricing model and algorithm for perishable products with fuzzy demand

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, α‐optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real‐world example is presented to highlight the effectiveness of the developed model and algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

Fuzzy turnover rate chance constraints portfolio model

One concern of many investors is to own the assets which can be liquidated easily. Thus, in this paper, we incorporate portfolio liquidity in our proposed model. Liquidity is measured by an index called turnover rate. Since the return of an asset is uncertain, we present it as a trapezoidal fuzzy number and its turnover rate is measured by fuzzy credibility theory. The desired portfolio turnover rate is controlled through a fuzzy chance constraint. Furthermore, to manage the portfolios with asymmetric investment return, other than mean and variance, we also utilize the third central moment, the skewness of portfolio return. In fact, we propose a fuzzy portfolio mean–variance–skewness model with cardinality constraint which combines assets limitations with liquidity requirement. To solve the model, we also develop a hybrid algorithm which is the combination of cardinality constraint, genetic algorithm, and fuzzy simulation, called FCTPM.     

A hybrid differential evolution algorithm to vehicle routing problem with fuzzy demands

In this paper, the vehicle routing problem with fuzzy demands (VRPFD) is considered, and a fuzzy chance constrained program model is designed, based on fuzzy credibility theory. Then stochastic simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy chance constrained program model. Moreover, the influence of the dispatcher preference index on the final objective of the problem is discussed using stochastic simulation, and the best value of the dispatcher preference index is obtained.     

基于可信性理论的均值-熵-偏度投资组合模型及其算法求解

本文考虑到证券市场的投资者往往面临着随机和模糊两种不确定性的情形,在模糊随机环境下把证券的收益率视作三角模糊变量,在可信性理论基础上建立了带融资约束条件的均值-熵-偏度三目标投资组合决策模型,拓展了基于可信性理论的投资组合决策模型的研究内容,同时通过对约束条件处理方法,外部档案维护方法等关键算子的改良,提出了一种新的约束多目标粒子群算法。本文运用该算法对模型进行求解,把得到的最优解与传统的多目标粒子群算法得到的最优解进行对比,结果表明新算法得到的最优解的质量会显著地优于传统的多目标粒子群算法的最优解,从而验证了算法的有效性和准确性。该算法可以在三维空间中得到一个分布性和逼近性较好的Pareto最优曲面,满足投资者对不同目标的差异需求,为投资者提供合理的投资组合决策方案。     

考虑破产控制的多期模糊资产-负债组合优化模型

在实际的投资决策过程中,一些投资者需要同时管理资产和负债,因此本文研究考虑破产控制和偿债行为的资产-负债管理问题。假设风险资产的收益率和负债的增长率为模糊数,用资产-负债组合的可能性期望和下半绝对偏差度量其收益和风险,以最大化最终期望净财富和最小化最终累积风险为目标,建立了允许限制性卖空的多期模糊资产-负债组合优化模型。然后,设计了一个基于粒子群算法和模拟退火算法的混合智能算法对模型进行求解。最后,通过实例分析说明了所设计算法与传统粒子群算法相比具有更好的优化性能和稳定性。本文所提出策略可以为需要同时管理资产和负债的投资者提供决策支持。     

Capacitated location allocation problem with stochastic location and fuzzy demand: A hybrid algorithm

In this article, a capacitated location allocation problem is considered in which the demands and the locations of the customers are uncertain. The demands are assumed fuzzy, the locations follow a normal probability distribution, and the distances between the locations and the customers are taken Euclidean and squared Euclidean. The fuzzy expected cost programming, the fuzzy β-cost minimization model, and the credibility maximization model are three types of fuzzy programming that are developed to model the problem. Moreover, two closed-form Euclidean and squared Euclidean expressions are used to evaluate the expected distance between customers and facilities. In order to solve the problem at hand, a hybrid intelligent algorithm is applied in which the simplex algorithm, fuzzy simulation, and a modified genetic algorithm are integrated. Finally, in order to illustrate the efficiency of the proposed hybrid algorithm, some numerical examples are presented.     

摩擦市场条件下的双目标投资组合模型模糊优化

首先建立了摩擦市场条件下基于收益率分布偏度水平的双目标投资组合模型.在此基础上,将模糊集合的概念引入到该模型中,用模糊数学中的线性隶属函数处理了其中的风险目标和收益目标,建立了摩擦市场条件下基于收益率分布偏度水平的模糊型双目标投资组合模型.然后,针对该模型进行了新型遗传算法设计(动态遗传算法).最后用一个具体的算例给出了该模型的一个实例最优解,体现了多样化投资分散风险的组合投资原理.