基于模糊综合评判的航空公司战略联盟合作伙伴选择研究

航空公司战略联盟成功的关键是能否选择出理想的合作伙伴,这是一个复杂的决策问题.提出了一个比较新颖的算法.首先根据AHP方法确定模糊评价矩阵和权重向量,并针对航空联盟的敏捷性要求和特定的市场需求,提出了评价指标两两间的比较标度的修正方法,然后通过模糊运算对目标函数进行排序,实现对合作伙伴的最优选择.最后以实例表明本算法能有效的支持伙伴选择.     

团队成员选择的模型及算法

本针对组织中组建团队或重组现有团队时的成员选择问题，提出了反映团队成员之间、成员和团队之间关系的群体效用模型，并根据此模型进行团队成员的选择，从而把团队成员选择问题转化为一个组合优化问题。证明了基于群体效用模型进行团队成员选择的问题是NP-hard问题，并且提出了基于ORASP技术和禁忌算法的启发式算法，最后给出了算例。     

Ad Hoc网络中基于时分的分布式QoS路由算法

捷径冲突是AdHvc网络中QoS路由特有的一种现象，它由WenjianShao在[2]中首次提出．本文进一步研究了捷径冲突现象，给出了一个更加准确的定义，且给出了一个基于时分的分布式QoS路由算法成功地避免了捷径冲突现象．本算法是基于TDMA的分布式算法，每个节点只需了解网络的局部信息即可．数据分析表明本算法预留的最大带宽比较接近AdHoc网络中所能用的最大带宽．     

基于经典逻辑系统的模糊推理方法

给出了一种基于经典二值逻辑系统的模糊推理方法.由于经典二值逻辑系统和我们提出的一种新的三段论原则是比较令人信服的,故我们的算法的基础是较为坚实的;同时在新的推理方法中避免了蕴合算子的选择,使得本算法不存在选择蕴含算子的困难;本算法具有还原性且计算简单;通过实例的计算结果与三Ⅰ算法和CRI算法的计算结果相比较,说明由新算法计算出的结果也是较为令人信服的,因此新算法是一种较好的算法.     

基于径向基函数网络的虚拟物流企业伙伴选择方法研究

在对虚拟物流企业系统研究的基础上 ,针对虚拟物流企业伙伴选择问题的特点 ,给出了一个面对虚拟物流企业伙伴选择问题的较为全面的选择过程框架 .并提出了一个基于径向基函数网络算法的虚拟物流企业伙伴选择模型 ,实例仿真说明了该算法和模型的有效性 .     

企业多风险的综合评估法

本顺应企业整体风险管理的需求，对企业的风险体系进行了分析研究；发展了一种新颖的企业多风险综合评估法，提出求解多个风险的总损失分布函数的算法，并据此分析了企业总体风险状况。最后，通过一个例子简单说明了该算法的具体应用。     

一种改进的进化规划算法

本对于全局优化问题提出一个改进的进化规划算法，该算法以概率p接收基于电磁理论求出合力方向作为随机搜索方向，以概率1-p接收按正态分布产生的随机搜索方向。改进算法不仅克服了传统进化规划算法随机搜索的盲目性，而且保留了传统进化规划算法全局搜索性。本算法应用于几个典型例题，数值结果表明本算法是可行的，有效的。     

企业能力指数测评模型研究

在新经济和全球化的影响下，企业之间的竞争演变为企业能力的竞争。企业要想生存发展，必须构建和提高自身的能力。因此，对企业能力做出科学的评估，从而确定企业战略、选择合作伙伴、优化供应链是十分重要的。本从企业能力及其指数定义出发，研究了企业能力的构成体系，利用模糊层次分析法，建立了企业能力指数测评模型，并给出了一个实例。     

调度问题中两类分离约束传播算法的比较及一种改进算法

约束传播算法是求解约束满足问题的一种重要方法。调度问题是一种特殊的约束满足问题。本介绍了调度问题中的Edge-Finding和Energy-Reasoning两种分离约束传播算法，并对它们进行了比较，中最后给出了一种结合Energy-Reasoning的Edge-Finding改进算法。     

不可恢复道路堵塞路径选择问题及其算法

针对不可恢复道路堵塞的路径选择问题，分析了堵塞发生的不同起始时间对通过被堵塞边的费用(时间)产生的影响，这种影响引起的后效性使得这个问题不适合用动态规划中逆序推算方法求解。本设计了一种算法(MDA)，对经典的Dijkstra算法中的权值计算进行了修正，证明了算法的复杂性为O(n^2)，并用该算法对模型进行了求解。最后通过一个算例分析，对算法进行了验证。     

An Arbitrary Starting Variable Dimension Algorithm for Computing an Integer Point of a Simplex

An arbitrary starting variable dimension algorithm is proposed to compute an integer point of an n-dimensional simplex. It is based on an integer labeling rule and a triangulation of Rⁿ. The algorithm consists of two interchanging phases. The first phase of the algorithm is a variable dimension algorithm, which generates simplices of varying dimensions,and the second phase of the algorithm forms a full-dimensional pivoting procedure, which generates n-dimensional simplices. The algorithm varies from one phase to the other. When the matrix defining the simplex is in the so-called canonical form, starting at an arbitrary integer point, the algorithm within a finite number of iterations either yields an integer point of the simplex or proves that no such point exists.     

不恢复余数法的等效变形—阵列除法器的数学分析(Ⅱ)

运算器对于CPU的性能有重要影响,除法器是运算器的一个重要组件.除法器电路常用不恢复余数法,但声称采用了不恢复余数法的各种电路采用的算法却有明显区别.及其前导文试图对不恢复余数法和不恢复余数阵列除法器电路进行分析.前导给出了不恢复余数法的一种初始形式,将给出初始形式的等效变形并展示阵列除法器电路是这种等效算法的硬件实现.     

New Algorithm for Computing LQ Suboptimal Output Feedback Gains of Decentralized Control Systems

In this paper, the problem of computing the suboptimal output feedback gains of decentralized control systems is investigated. First, the problem is formulated. Then, the gradient matrices based on the index function are derived and a new algorithm is established based on some nice properties. This algorithm shows that a suboptimal gain can be computed by solving several ordinary differential equations (ODEs). In order to find an initial condition for the ODEs, an algorithm for finding a stabilizing output feedback gain is exploited, and the convergence of this algorithm is discussed. Finally, an example is given to illustrate the proposed algorithm.     

含有等式约束非线性规划的全局优化算法

针对含有多个等式约束的非线性规划问题,提出一个全局优化算法.该方法基于可行集策略把改进的模拟退火方法与确定的局部算法方法相结合.对算法的收敛性进行了证明,数值结果表明算法的有效性及正确性.     

一种改进的禁忌搜索算法及其在连续全局优化中的应用

禁忌搜索算法是一种元启发式的全局优化算法,是局部搜索算法的一种推广,已被成功地应用于许多组合优化问题中。本文针对有界闭区域上的连续函数全局优化问题,提出了一种改进的禁忌搜索算法,并进行了理论分析和数值实验。数值实验表明,对于连续函数全局优化问题的求解该算法是可行有效的,并且结构简单,迭代次数较少,是一种较好的全局启发式优化算法。     

CurveLP-A MATLAB implementation of an infeasible interior-point algorithm for linear programming

Mehrotra’s algorithm has been the most successful infeasible interior-point algorithm for linear programming since 1990. Most popular interior-point software packages for linear programming are based on Mehrotra’s algorithm. This paper describes a proposal and implementation of an alternative algorithm, an arc-search infeasible interior-point algorithm. We will demonstrate, by testing Netlib problems and comparing the test results obtained by the arc-search infeasible interior-point algorithm and Mehrotra’s algorithm, that the proposed arc-search infeasible interior-point algorithm is a more reliable and efficient algorithm than Mehrotra’s algorithm.     

Multiobjective linear programming with context-dependent preferences

Multiobjective linear programming algorithms are typically based on value maximization. However, there is a growing body of experimental evidence showing that decision maker behavior is inconsistent with value maximization. Tversky and Simonson provide an alternative model for problems with a discrete set of choices. Their model, called the componential context model, has been shown to capture observed decision maker behavior. In this paper, an interactive multiobjective linear programming algorithm is developed which follows the rationale of Tversky and Simonson. The algorithm is illustrated with an example solved using standard linear programming software. Finally, an interactive decision support system based on this algorithm is developed to field test the usefulness of the algorithm. Results show that this algorithm compares favorably with an established algorithm in the field.     

On the internal stepsize of an extrapolation algorithm for IVP in ODE

We propose an extrapolation algorithm for initial value problems in ordinary differential equations. In the algorithm, an appropriately chosen stepsizeH is divided into smaller stepsizes by a sequence and a new stopping rule is proposed. The sequences applied to the algorithm are Romberg {2,4,8,16,32,...}, Bulirsch {2,4,6,8,16...} and Harmonic {2,4,6,8,10,12,...} types. The proposed algorithm is compared numerically with the algorithm introduced by Stoer. In view of the accuracy of numerical solutions, the relatively small number of calculations, the stability and reliability of the algorithm, we found that the algorithm with the Romberg sequence is the best.     

An Extension of Least Angle Regression Based on the Information Geometry of Dually Flat Spaces

We extend the least angle regression algorithm using the information geometry of dually flat spaces. The extended least angle regression algorithm is used for estimating parameters in generalized linear regression, and it can be also used for selecting explanatory variables. We use the fact that a model manifold of an exponential family is a dually flat space. In estimating parameters, curves corresponding to bisectors in the Euclidean space play an important role. Originally, the least angle regression algorithm is used for estimating parameters and selecting explanatory variables in linear regression. It is an efficient algorithm in the sense that the number of iterations is the same as the number of explanatory variables. We extend the algorithm while keeping this efficiency. However, the extended least angle regression algorithm differs significantly from the original algorithm. The extended least angle regression algorithm reduces one explanatory variable in each iteration while the original algorithm increases one explanatory variable in each iteration. We show results of the extended least angle regression algorithm for two types of datasets. The behavior of the extended least angle regression algorithm is shown. Especially, estimates of parameters become smaller and smaller, and vanish in turn.     

Finding feasible vectors of Edmonds-Giles polyhedra

A combinatorial algorithm for finding a feasible vector of the Edmonds-Giles polyhedron is presented. The algorithm is polynomially bounded provided that an oracle is available for minimizing submodular functions. A feasibility theorem is also proved by the algorithm and, as a consequence, a good algorithm for finding an integer-valued modular function between a sub- and a supermodular function is deduced. An important idea in the algorithm is due to Schönsleben and Lawler and Martel: the shortest augmenting paths have to be chosen in a lexicographic order.