基于多目标影响图效果作战模型

针对目前基于效果作战评估中没有涉及多目标以及决策者的偏好的情况,将多目标影响图引入到基于效果作战模型.首先分析了基于效果作战中各主要要素之间关系,其次提出了基于效果作战模型构造过程,然后将其与多目标决策理论结合建立了多目标基于效果作战模型,用多目标影响图对其进行建模,并给出了求解方法,最后将基于多目标影响图效果作战模型应用于空战任务分配中.仿真结果表明该模型的有效性.     

连续区间数的中位算子及其灵敏度分析

研究决策信息以区间数形式给出的方案决策问题。考虑到区间数非均匀分布的特点,为避免区间极端值在区间数确定中产生较大误差,本文将中位数概念运用到区间数的确定上,提出OIP(Ordered Interval Point)有序中位算子。取单位区间单调函数(BUM函数)为二分之一所表示的值为权重,将区间数确定为一个实数,并研究算子单调性和有界性的初等运算性质。通过比较OIP算子与COWA算子对态度参数和区间长度的反应灵敏度,获得了在一定条件下OIP算子对态度参数反应更稳健,对区间长度反应更灵敏的结论。最后用算例证明该算子的可行性和有效性。     

Dynamic models of segregation†

Some segregation results from the practices of organizations, some from specialized communication systems, some from correlation with a variable that is non‐random; and some results from the interplay of individual choices. This is an abstract study of the interactive dynamics of discriminatory individual choices. One model is a simulation in which individual members of two recognizable groups distribute themselves in neighborhoods defined by reference to their own locations. A second model is analytic and deals with compartmented space. A final section applies the analytics to ‘neighborhood tipping.’ The systemic effects are found to be overwhelming: there is no simple correspondence of individual incentive to collective results. Exaggerated separation and patterning result from the dynamics of movement. Inferences about individual motives can usually not be drawn from aggregate patterns. Some unexpected phenomena, like density and vacancy, are generated. A general theory of ‘tipping’ begins to emerge.     

Strategic control and interests,its effects on decision outcomes

Application of the model to artificial data shows that actors with strong preferences in the center have more possibilities to realize good outcomes than other actors. On the basis of an empirical application it is shown that a Nash equilibrium does not always arise after a large number of iterations unless actors have learning capabilities or are severely restricted in their strategic behavior.

In political systems and large organizations, ultimate decision makers are usually just a small subset of all actors in the social system. To arrive at acceptable decisions, decision makers have to take into account the preferences of other actors in the system. Typically preferences of more interested and more powerful actors are weighted heavier than those of less interested and powerful actors. This implies that the total leverage of an actor on the decision is determined by the combination of his power (his potential) and his interest (his willingness to mobilize his power). As the exact level of an actor's leverage is difficult to estimate for the other actors in the system, an actor is able to optimize his effects on outcomes of decisions by providing strategic informatioa

In this paper, first an analytic solution is presented for the optimization of strategic leverage in collective decision making by one single actor. In this solution, the actor makes assumptions about the leverage other actors will show in decision making. Subsequently, the actor optimizes the outcomes of decisions by manipulating the distribution of his leverage over a set of issues.

The analytic solution can be theoretically interpreted by decomposing the solution into three terms, the expected external leverage of the other actors on the issue, the evaluation of the deviance of the expected from the preferred outcome of the issue, and the restrictions on the distribution of leverage over the issues. The higher the expectation of the leverages the other actors will allocate to the issue, the less an actor is inclined to allocate leverage to the issue. The higher the evaluation of the deviance, the more an actor is inclined to allocate leverage to the issue. This is restricted, however, by the required distribution of leverages over the issues. The researcher is able to manipulate these restrictions to investigate its consequences for the outcomes.

In the next step, we investigate whether we can find a Nash equilibrium if all actors optimize their leverage simultaneously. Under certain conditions, a Nash equilibrium can be found by an iterative process in which actors update their estimates oh each other's leverages on the basis of what the other actors have shown in previous iterations.     

Performance of Organizational Design Models and Their Impact on Organization Learning

The performance of various organizational structures is an essential parameter in the reengineering of organizations, particularly in the current rapidly changing, competitive and information technology-dependent environment. This situation has generally resulted in a smaller workforce confronted with voluminous information-processing requirements. Naturally, the focus has been on the design of organzations that will support effective and efficient learning processes. This specific study is on the contingency modely by Ouksel et al. (1997) on the impact of organizational structure on organizational learning and performance. It expands the empirical evaluation of the model to test its robustness. This is done by conducting extensive simulations with (a) a larger number of variables in the decision tasks, and (b) a larger set of numbers for each of the numeric variables, than has been used in the past. Overall, the validity of previous results achieved has been confirmed, but the selection of the most appropriate design, that which will indeed improve learning, is even more nuanced than past studies have concluded.     

A new strong optimality criterion for nonstationary Markov decision processes

This paper deals with a new optimality criterion consisting of the usual three average criteria and the canonical triplet (totally so-called strong average-canonical optimality criterion) and introduces the concept of a strong average-canonical policy for nonstationary Markov decision processes, which is an extension of the canonical policies of Herna′ndez-Lerma and Lasserre [16] (pages: 77) for the stationary Markov controlled processes. For the case of possibly non-uniformly bounded rewards and denumerable state space, we first construct, under some conditions, a solution to the optimality equations (OEs), and then prove that the Markov policies obtained from the OEs are not only optimal for the three average criteria but also optimal for all finite horizon criteria with a sequence of additional functions as their terminal rewards (i.e. strong average-canonical optimal). Also, some properties of optimal policies and optimal average value convergence are discussed. Moreover, the error bound in average reward between a rolling horizon policy and a strong average-canonical optimal policy is provided, and then a rolling horizon algorithm for computing strong average ε(>0)-optimal Markov policies is given.     

不确定群组决策的一致性调整及专家的赋权

对不确定群组决策的一致性问题进行了研究,给出了一种调整区间判断矩阵一致性的算法,并对参加决策的专家进行了赋权。     

时间敏感选项依赖下的基础设施恢复决策研究

突发事件发生后,基础设施之间的恢复依赖为恢复过程带来了严重挑战。为了能够在突发事件后高效而有序的实现基础设施恢复运行,根据相互之间的恢复依赖关系制定合理的恢复决策非常关键。本文基于网络流理论,以累积恢复效能最大化为目标,建立了时间敏感选项依赖下的恢复设计与调度决策混合整数规划模型。然后,讨论了模型在完全中心化、完全分散和信息共享决策环境下的应用方法。最后,通过真实基础设施数据集测试了模型,结果表明:(1)该模型在突发事件后的基础设施恢复决策中具有应用可行性;(2)决策环境显著影响存在恢复依赖的基础设施网络整体累积恢复效能;(3)与完全分散决策环境相比,在信息共享决策环境下独立决策的整体累积恢复效能可以得到大幅提升。     

考虑专利保护和渠道偏好的再制造双渠道闭环供应链决策与协调

本文在电子商务环境下考虑消费者对零售渠道和直销渠道具有不同的渠道偏好,研究了专利许可零售商实施再制造的双渠道闭环供应链定价决策和协调问题。运用博弈论方法求得了集中决策和分散决策情形下的最优定价策略,并分析了消费者渠道偏好系数对节点企业最优定价策略及利润的影响。针对分散决策存在效率损失的问题,以集中决策的最优解为基准,通过联合运用一个由批发价格、直销价格和专利许可费构成的定价机制和一个利润分享机制,实现了双渠道闭环供应链的完美协调。