基于可信性理论的生产计划期望值模型

基于可信性理论,提出一类新的模糊生产计划期望值模型.然后,讨论这个模糊生产计划模型的基本性质.最后,利用这个模糊模型的基本性质我们可以把模糊生产计划期望值模型转化为一个线性规划模型并且设计相应的算法求解模糊生产计划问题的一个数值例子.     

基于DNA遗传算法的随机整数规划期望值模型求解

为了求解随机整数规划问题,提出了随机整数规划期望值模型的概念,分析了利用DNA遗传算法求解此类问题的优点,并设计了求解算法,最后通过报童问题,验证了算法的可行性和有效性.     

企业合作联盟成员位次竞争战略的Shapley值分析

针对Yanowitz和Bruckstein的二值字图像后处理算法存在的问题，提出了一种新的基于灰度期望值的后处理算法。实验结果表明，该算法能自动选取阈值、运算速度快、处理效果好，具有良好的噪声适应性。     

不同风险偏好下虚拟企业风险规划研究

针对虚拟企业风险规划问题,在分析其各种风险具有随机性的特点的基础上,运用随机规划理论,分别建立风险规划的期望值模型和机会约束规划模型来描述决策者在不同风险偏好下的决策行为。针对所建立的模型,分别设计了基于蒙特卡罗模拟的粒子群优化算法、遗传算法和蚁群算法对其进行求解。仿真分析表明期望值模型较好地描述了风险中性决策者的决策行为,机会约束规划模型随着其偏好系数取值的不同描述了不同风险偏好(风险厌恶、风险中性、风险爱好)决策者的决策行为。通过对三种算法仿真结果的比较分析,表明基于蒙特卡罗模拟的粒子群优化算法在寻优能力、稳定性和收敛速度等方面优于其余两种算法,是解决此类风险规划问题的有效手段。     

二阶段随机规划问题基于随机模拟的遗传算法

利用遗传算法不过多依赖目标函数性质．适应于全局搜索的特点．提出了求解二阶段随机规划的基于随机模拟的遗传算法，算法采用随机模拟技术利用样本均值近似代替期望值，使计算得以简化，计算实例表明该算法是有效和可行的。     

模糊随机需求的连续盘点存储策略研究

考虑到需求的模糊随机性,建立模糊随机需求情况下连续盘点存储策略的模糊随机成本模型。利用模糊随机变量的期望值理论,推导出了其成本期望值模型的解析表达式,进而给出了最优再订货点所属区间的判别条件以及最优再订货点和经济订货量的计算式;基于此,设计了一模糊随机需求的连续盘点最优存储策略算法。最后结合数值算例,分析了模糊随机需求概率分布及缺货成本对最优存储策略的影响。     

基于g_λ变量的冗余优化模型

为了解决Sugeno测度空间上系统寿命期望值、系统寿命的α乐观值和系统可靠性的系统性能优化问题,提出用g_λ变量来表征冗余元件寿命的冗余优化模型。首先,建立Sugeno冗余期望值模型、Sugeno冗余机会约束规划模型和Sugeno冗余相关机会规划模型;其次,为了求解模型,设计一种基于Sugeno模拟、多层神经网络和遗传算法的混合智能算法;最后,通过桥式系统的系统性能优化的算例验证所提模型和算法的可行性。     

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.     

基于模糊环境的战时多目标物资供应任务规划

战时保障物资的供应对现代战争起到了至关重要的作用,然而物资供应任务却面临诸多不确定性,传统方法难以对此进行科学准确的任务规划.针对该问题,基于可信性理论,建立了模糊环境下的战时多目标保障物资供应任务规划模型,以及相关期望值等价模型.并结合一种先进的智能启发算法-蝙蝠算法对模型进行高效准确的求解,最后,通过一个任务规划应用实例,验证模型及算法的有效性.结果表明设计的模型与算法与实际问题更为接近,符合实际决策需要.     

单台机器E-T随机排序问题的多项式算法

本文研究排序问题中的E—T问题，工件在单台机器上加工，n个工件的加工时间都为整数P，相同的工期d为离散分布，满足∑i=1^mP（d=ξi）=1,其中ξ为整数，目标是使E（∑（Ei＋Tj））的期望值最小。应用贪婪算法和二分法思想，我们提出解决该问题的一个最优算法，并得出该算法的复杂性为O（nmlogp）。     

交易成本型证券投资中求最优投资方案的一个算法

给出一个交易成本型证券投资中求最优投资方案的数值算法 ,证明了算法的理论依据 ,并举例说明算法的应用 .     

Improving the Data Augmentation Algorithm in the Two-Block Setup

The data augmentation (DA) approach to approximate sampling from an intractable probability density f_X is based on the construction of a joint density, f_{X, Y}, whose conditional densities, f_X|Y and f_Y|X, can be straightforwardly sampled. However, many applications of the DA algorithm do not fall in this “single-block” setup. In these applications, X is partitioned into two components, X = (U, V), in such a way that it is easy to sample from f_Y|X, f_{U|V, Y}, and f_{V|U, Y}. We refer to this alternative version of DA, which is effectively a three-variable Gibbs sampler, as “two-block” DA. We develop two methods to improve the performance of the DA algorithm in the two-block setup. These methods are motivated by the Haar PX-DA algorithm, which has been developed in previous literature to improve the performance of the single-block DA algorithm. The Haar PX-DA algorithm, which adds a computationally inexpensive extra step in each iteration of the DA algorithm while preserving the stationary density, has been shown to be optimal among similar techniques. However, as we illustrate, the Haar PX-DA algorithm does not lead to the required stationary density f_X in the two-block setup. Our methods incorporate suitable generalizations and modifications to this approach, and work in the two-block setup. A theoretical comparison of our methods to the two-block DA algorithm, a much harder task than the single-block setup due to nonreversibility and structural complexities, is provided. We successfully apply our methods to applications of the two-block DA algorithm in Bayesian robit regression and Bayesian quantile regression. Supplementary materials for this article are available online.     

基于不确定性回报的行为控制博弈分析

不确定型的行为控制较之确定型的行为控制更为复杂,但在现实中又有一定的广泛性,且目前的研究基础比较薄弱.把研究的视角放在不确定性回报下行为控制的博弈机理上,考察了博弈双方的行为观测偏差,定义了在信息不对称条件下管理者对被管理者的行为的观测矩阵、管理者的博弈策略矩阵、被管理者对管理者观测能力的估计矩阵及对管理者博弈策略的估计矩阵等,分析了管理者与被管理者之间的动态博弈过程和行为策略选择,给出了满足博弈均衡的不确定性回报的设计要求.这些都可以推广应用于各种具体管理制度的设计和改进当中,为行为控制研究提供了定量分析的新方法.在此基础上,选择房地产投资开发行为控制机制的实例进行了诊断研究.     

Optimization of a special case of continuous-time Markov decision processes with compact action set

Performance optimization is considered for average-cost multichain Markov decision processes (MDPs) with compact action set. Since, for a general compact multichain model, the optimality equation system may have no solution, and also a policy iteration algorithm may yield a suboptimal policy rather than an optimal one, we concentrate only on a special case of multichain models in this paper, where we assume that the classifications of states are fixed identically rather than varying with policies. By using the concept of performance potentials, the existence of solutions to the optimality equation system is established, and then a potential-based policy iteration algorithm is supposed to solve this system. In addition, the optimality convergence, for recurrent classes, of the algorithm has been proved. Finally, a numerical example is provided.     

Finding the response matrix to the external action from a subspace for a discrete linear stochastic dynamical system

For a discrete linear stochastic dynamical system, computation of the response matrix to the external action from a subspace using given observational data is examined. An algorithm is proposed and substantiated that makes it possible to improve the numerical accuracy and to reduce the amount of observational data compared to the general case where an arbitrary external action is allowed. As an illustration, a discrete system arising in the analysis of a linear stochastic dynamical continuous-time system is considered more thoroughly. Some numerical results are presented.     

A variable dimension fixed point algorithm and the orientation of simplices

A variable dimension algorithm with integer labelling is proposed for solving systems ofn equations inn variables. The algorithm is an integer labelling version of the 2-ray algorithm proposed by the author. The orientation of lower dimensional simplices is studied and is shown to be preserved along a sequence of adjacent simplices.     

房地产开发活动均衡分析与过度开发行为控制

在把房地产投资开发项目看作为一种具有弱再生性经济资源的基点上,对房地产投资开发活动进行了均衡分析,讨论了房地产开发活动中的最优平衡解及其相互之间的制约关系,分析了税收和配额价格等经济参数对最优平衡解的影响,给出了抑制房地产过度投资开发行为的控制模型与方法,并得出了相应的政策启示.这些研究结果,可以推广应用于其他各种可再生资源的投资开发与管理当中去,有着广泛的应用前景.     

A variable neighborhood search based algorithm for finite-horizon Markov Decision Processes

This paper considers the application of a variable neighborhood search (VNS) algorithm for finite-horizon (H stages) Markov Decision Processes (MDPs), for the purpose of alleviating the “curse of dimensionality” phenomenon in searching for the global optimum. The main idea behind the VNSMDP algorithm is that, based on the result of the stage just considered, the search for the optimal solution (action) of state x in stage t is conducted systematically in variable neighborhood sets of the current action. Thus, the VNSMDP algorithm is capable of searching for the optimum within some subsets of the action space, rather than over the whole action set. Analysis on complexity and convergence attributes of the VNSMDP algorithm are conducted in the paper. It is shown by theoretical and computational analysis that, the VNSMDP algorithm succeeds in searching for the global optimum in an efficient way.     

Wonderful blowups associated to group actions

A group action on a smooth variety provides it with the natural stratification by irreducible components of the fixed point sets of arbitrary subgroups. We show that the corresponding maximal wonderful blowup in the sense of MacPherson-Procesi has only abelian stabilizers. The result is inspired by the abelianization algorithm of Batyrev.     

确定双指数曲线参数初始值的循环搜索法

提出了在最小二乘意义下用 Gauss-Newton法拟合双指数曲线时 ,充分利用观测值确定参数初始值的一种算法——循环搜索法 .据此可编制一个能自动拟合 2 0种单、双指数曲线中指定曲线的 Qbasic程序 .并成功地以多个模型为例对此进行了验证