基于协同效应的知识创新团队伙伴选择方法

在知识创新团队的伙伴选择问题中着重考虑了伙伴间的协同效应信息。首先,分析了伙伴之间的协同关系与协同效应,描述了考虑多个协同效应评价指标的知识创新团队伙伴选择问题;然后,建立了团队伙伴选择的数学模型,该模型是一个0-1二次整数规划问题,为了求解该问题,开发了一种GRASP启发式算法;最后,通过一个实例分析说明了所提出方法的可行性和实际应用价值。     

基于集对分析和模糊多属性群决策的动态联盟盟员选择模型

动态联盟盟员选择是一个十分重要的问题,它直接关系到动态联盟的成败。为了求得动态联盟的最佳伙伴,考虑到不同决策者其决策可信性的区别,结合盟员选择中不同决策者判断模糊性的特点,本文基于模糊环境下的多属性群决策的方法,应用集对分析理论和方法,构建了一种新的动态联盟盟员选择模型。实例计算表明,应用该模型进行动态联盟盟员选择具有可行性,为选择动态联盟盟员提供了一种有效的方法,具有一定的实用价值。     

基于IEM的虚拟物流企业伙伴选择DP模型

在应用0-1整数规划建立虚拟物流企业伙伴选择多目标决策模型的基础上,给出了支持伙伴选择的多目标决策工具改进熵值法IEM和动态规划DP模型,并通过算例证明了该模型的有效性.     

企业动态联盟伙伴选择的经济分析

根据企业的动态联盟伙伴选择经济分析的需要,提出了一种新的按项目开发阶段及提供的优势资源的特征确定伙伴类型方法,并在此基础上提出了企业动态联盟伙伴选择经济分析的两种方法。     

具有联盟优先关系的模糊合作博弈的目标规划求解模型

用合作博弈研究实际管理问题中的分配方案时，常常存在一些不重要联盟或无效联盟，这些联盟影响公平合理的分配方案。因此，联盟的重要程度成为求解合作博弈必不可少的因素。本文考虑了联盟的重要性和局中人参与联盟的不确定性，研究了具有优先关系的模糊联盟合作博弈(简称为模糊合作博弈)。首先，借助于目标规划模型的优先因子可以表征联盟重要程度的思想，通过构建多优先级目标规划模型，得到模糊合作博弈新的解。其次，证明了构建的多优先级目标规划模型的解和模糊合作博弈的核心之间具有重要对应关系。最后，通过数值实例和比较分析，说明本文提出的多优先级目标规划模型求解模糊合作博弈问题的合理性和有效性。研究表明：(1)本文提出的多优先级目标规划模型考虑不同联盟重要程度，得到的解符合“多劳多得”原则，能够更公平合理解决实际管理中的分配问题。(2)本文的目标规划模型同时适用于求解存在联盟特征函数值缺失的合作博弈。与已有合作博弈的解进行比较分析，该模型无需估算无效联盟的特征函数缺失数据得到的分配值更为准确。从而，说明本文给出的目标规划求解模糊合作博弈解的模型，更符合许多管理学问题的实际情况。(3)通过多优先级目标规划模型最优解是否存在可判断模糊合作博弈的核心存在情况，若核心存在则该模型通过目标规划软件包可得到核心内的一个解，这样也得到了一个判断合作博弈核心是否存在的标准。(4)目标规划模型可弥补已有合作博弈解的一些不足，如核心可能为空集，Shapley值和最小二乘预核仁可能不满足个体合理性等。本文构建的多优先级目标规划模型不仅能求解联盟具有优先关系的模糊合作博弈，而且能够求解一般合作博弈的解，该目标规划模型作为合作博弈一种新的求解方法，能更有效地解决实际管理中的分配问题，具有更加广泛的应用价值。     

企业战略联盟风险防范体系的架构研究

在对企业战略联盟存在的风险及规避措施进行理论透析的基础上,给出了企业战略联盟伙伴选择机制的权值排序模型。提出了企业战略联盟的3种风险防范整合架构,并论述了三者之间的相关关系。最后通过实例对企业战略联盟伙伴选择机制的权值排序模型和风险防范整合体系进行了验证。     

战略联盟企业间的和谐性分析与优化

联盟伙伴在合作过程中战略的不协同和文化的不融合是战略联盟失败的主要原因。以和谐理论为基础,运用和谐分析矩阵对企业战略联盟的现实和谐状态进行分析和评价,通过演化过程模型实现较理想的和谐状态。     

基于证据推理的效能评估及其在临近空间多任务规划中的应用

在临近空间系统多任务规划过程中,对不同的多任务规划场景下,临近空间系统综合效能及其变化规律的研究,可以帮助决策者寻找到相对最优的临近空间多任务规划方案。由于临近空间系统涉及多资源、多任务和复杂的内外部环境,影响其效能的因子较多且动态多变,因此,其效能评估将遇到多种确定性和不确定性因素共生环境下的评价问题,传统的方法难以独立处理该问题。鉴于此,将证据推理的方法引入效能评估中,设计了基于证据推理的效能评估模型,该模型综合考虑了无知、模糊、不准确、区间数和区间信念度等多种不确定性因素,并实现了多源证据的有效合成。最后,在实现的2组面向点目标的临近空间多任务规划仿真实验中,运用该模型分别计算出了不同场景下临近空间系统综合效能,计算结果显示资源数和任务数的变化对临近空间系统综合效能的影响符合客观规律,表明该模型具有一定的可用性。     

海运集装箱收益管理舱位动态控制策略研究

文章基于收益管理的思想对不确定环境下集装箱班轮的动态舱位分配问题进行了定量研究.考虑了班轮运输业中的团体预订行为,建立了离散时间、离散状态的随机动态规划模型,并分别对考虑团体需求和不考虑团体需求模型的性质进行了分析,提出了在不考虑团体需求情况下的阈值控制策略并指出了在考虑团体需求情况下该策略不成立的情况,最后,用数值仿真对以上性质和策略进行了验证.     

强势企业与弱势企业联盟的博弈分析及其启示

在现有战略联盟理论研究中,传统的成本-收益分析方法只是关注联盟伙伴之间的互补性收益,忽视了竞争动态环境下强(弱)势企业联盟的战略选择.实际上,强(弱)势企业建立战略联盟不仅是为了获得互补性收益,更重要的是预防损失,也就是为了预防被排斥在联盟活动之外所带来的潜在损失.而竞争性动态是企业建立战略联盟的一个重要因素,强(弱)势企业的联盟战略需要根据竞争者联盟行为所带来的可预防损失与联盟成本的比较做出相机性战略选择.     

改进区间可接受度的证券投资组合区间二次规划模型

本文首先基于Markowitz的经典均值方差模型，针对不确定环境下的投资组合问题，把证券的收益率、风险损失率和流动性用区间数描述，建立了一种新的含交易成本的证券投资组合区间二次规划模型。其次，为求解该模型，提出了改进的区间可接受度确定性转换方法，通过引入优化水平α与可接受水平η将不确定二次规划转化为确定型规划。最后，通过数值实验将提出的方法与传统方法进行比较，结果表明本文所提出的方法与模型具有相对较好的可行性与实用性。     

不允许卖空下证券投资组合的区间二次规划问题

本文基于经典的Markowitz均值-方差模型,针对市场上不允许卖空的情况,提出了证券投资组合的区间二次规划模型,通过应用区间数排序方法(区间序关系、区间可能度和区间可接受度),给出了两种证券投资组合的区间非线性优化的数学转化模型,从而将不确定性证券投资组合模型转化为确定性的证券投资组合二次规划模型进行求解,并对由本文给出的三种求解方法与传统方法进行了比较。     

摩擦市场上允许买空卖空的投资组合问题

证券交易市场上存在着诸如交易费用、税收等摩擦.投资者在交易过程中,不可避免地要受到市场摩擦的影响.本文以投资者所获取的最大投资效用为目标函数,建立了摩擦市场上最优投资组合问题的数学模型;同时对于之前解决此类问题的很多文章中“证券市场不允许买空卖空风险资产和借贷无风险资产”的假设条件做了扩展,得到一个摩擦市场上适用于“允许买空卖空或借贷”的证券投资组合的二次规划模型.     

Linear programming approach to solve interval-valued matrix games

Matrix game theory is concerned with how two players make decisions when they are faced with known exact payoffs. The aim of this paper is to develop a simple and an effective linear programming method for solving matrix games in which the payoffs are expressed with intervals. Because the payoffs of the matrix game are intervals, the value of the matrix game is an interval as well. Based on the definition of the value for matrix games, the value of the matrix game may be regarded as a function of values in the payoff intervals, which is proven to be non-decreasing. A pair of auxiliary linear programming models is formulated to obtain the upper bound and the lower bound of the value of the interval-valued matrix game by using the upper bounds and the lower bounds of the payoff intervals, respectively. By the duality theorem of linear programming, it is proven that two players have the identical interval-type value of the interval-valued matrix game. Also it is proven that the linear programming models and method proposed in this paper extend those of the classical matrix games. The linear programming method proposed in this paper is demonstrated with a real investment decision example and compared with other similar methods to show the validity, applicability and superiority.     

摩擦市场上最优投资组合的一种数值解法

在实际的证券交易市场上存在着诸如交易费用、税收等摩擦.投资者在交易的过程中,不可避免的要受到市场摩擦的影响.作者以投资者为了获取最大的投资效用为目标函数,建立了摩擦市场上最优投资组合问题的数学模型,并提出一种适用于此类模型求解的内点算法,同时也给出了算法的具体实现步骤和一个具体的算例.     

Separation Failure in Linear Programming Discriminant Models

Linear programming discriminant analysis (LPDA) models are designed around a variety of objective functions, each representing a different measure of separation of the training samples by the resulting discriminant function. A separation failure is defined to be the selection of an “optimal” discriminant function which incompletely separates a pair of completely separable training samples. Occurrence of a separation failure suggests that the chosen discriminant function may have an unnecessarily low classification accuracy on the actual populations involved. In this paper, a number of the LPDA models proposed for the two-group case are examined to learn which are subject to separation failure. It appears that separation failure in any model can be avoided by applying the model twice, reversing group designations.     

An integrated approach for the electronic contract manufacturer selection problem

In the global competitive environment, lead companies have to outsource their manufacturing to electronic contract manufacturers (ECM) in order to reduce operational cost and capture higher profit. Thus, the process of selecting contract manufacturers to be strategic partners is important for these companies. For this reason, this paper proposes an integrated approach for the electronic contract manufacturer selection problem, combining the voting method and the goal programming (GP) model to take into account quantitative factors involved in the selection process, which is applied to a real-world ECM selection problem encountered by a leading provider of complete broadband access solutions. Results of the case study indicate that the voting method can acquire the rating information quickly from manufacturing managers based on suitable evaluating criteria. The rating information simultaneously incorporates ECMs' performance scores as a weight for each ECM. A production planner can easily employ the GP model and ECMs’ weight information to effectively assign demand quantities to ECMs. Therefore, the integrated approach is a practical and useful tool for solving the contract manufacturer selection problem.     

An application of multiple objective linear programming to media selection

A multiple objective linear programming (MOLP) method utilizing interval criterion weights is applied to the problem of media selection. Using this technique, it is possible to analyze a problem more explicitly in terms of the several objectives inherent in many media selection situations. In order to illustrate the interval criterion weights approach, a multiple objective hierarchical media selection model is presented and its computer results discussed. In addition to being able to deal more directly with different decision criteria, a distinguishing feature of the mathematical analysis applied here is that its output enables the media-planner to be presented with a small cluster of candidate media schedules (rather than just one). Then, from this list, the media-planner should be in a position to qualitatively make a final choice as a close approximation to his optimal solution.     

考虑不同碳排放处理模式的动态供应商选择及采购批量问题研究

不同的碳排放处理模式及不确定的市场需求等因素影响下,如何选择供应商并确定采购批量直接影响企业的运营和效益。本文在多时间周期、多产品种类、多供应商及随机需求情形下,同时考虑不同碳排放处理模式,分析动态供应商选择及采购批量等最优决策问题,构建混合整数非线性规划模型。通过设计变异算子和扰动因子来改进粒子群算法,力求在短时间内求解大规模决策问题。针对不同规模供应商选择及采购批量决策问题,采用精确方法、近似方法和改进粒子群算法求解。数值实验验证了模型及改进粒子群算法的有效性和可行性,分析了碳税、碳交易价格及碳限额对供应链管理的影响,并给出了供应商选择及碳排放处理的决策参考建议。     

Models and algorithms for the design of survivable multicommodity flow networks with general failure scenarios

We consider the design of a multicommodity flow network, in which point-to-point demands are routed across the network subject to link capacity restrictions. Such a design must build enough capacity and diverse routing paths through the network to ensure that feasible multicommodity flows continue to exist, even when components of the network fail. In this paper, we examine several methodologies to optimally design a minimum-cost survivable network that continues to support a multicommodity flow under any of a given set of failure scenarios, where each failure scenario consists of the simultaneous failure of multiple arcs. We begin by providing a single extensive form mixed-integer programming formulation for this problem, along with a Benders decomposition algorithm as an alternative to the extensive form approach. We next investigate strategies to improve the performance of the algorithm by augmenting the master problem with several valid inequalities such as cover constraints, connectivity constraints, and path constraints. For the smallest instances (eight nodes, 10 origin–destination pairs, and 10 failure scenarios), the Benders implementation consumes only 10% of the time required by the mixed-integer programming formulation, and our best augmentation strategy reduces the solution time by another 50%. For medium- and large-sized instances, the extensive form problem fails to terminate within 2 h on any instance, while our decomposition algorithms provide optimal solutions on all but two problem instances.