Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty

We propose a novel simulation‐based approach for solving two‐stage stochastic programs with recourse and endogenous (decision dependent) uncertainty. The proposed augmented nested sampling approach recasts the stochastic optimization problem as a simulation problem by treating the decision variables as random. The optimal decision is obtained via the mode of the augmented probability model. We illustrate our methodology on a newsvendor problem with stock‐dependent uncertain demand both in single and multi‐item (news‐stand) cases. We provide performance comparisons with Markov chain Monte Carlo and traditional Monte Carlo simulation‐based optimization schemes. Finally, we conclude with directions for future research.     

策略不确定条件下基于高阶超博弈的军事对抗决策方法

军事对抗决策中面临的一个主要问题是难以确定敌方的策略集。基于超博弈分析策略不确定条件下的军事对抗决策问题。在分析超博弈中信念迭代原理的基础上,提出了基于高阶超博弈的对抗决策方法。将高阶信念中的结果反思到低阶信念,并最终反思到一阶信念中,将己方的决策建立在对敌方可能决策的分析的基础上。使得己方的决策具有针对性且更加合理。以二战中西线战役为例验证了方法的可行性和有效性。     

变分自编码器结合聚类算法在空战态势评估问题上的应用

针对传统态势评估方法确定权值的主观性强、处理大数据能力弱、特征提取能力不足等问题,提出基于改进变分自编码器和聚类算法的无监督空战态势评估方法。根据态势变化连续性特点,提出基于时间段的空战态势分类方法,将敌我双方态势划分为四类。在变分自编码器的基础上,提出了VAE-WRBM-MDN特征提取模型,即使用混合密度网络优化变分自编码器的特征提取能力和生成数据的相似度,使用权值不确定限制玻尔兹曼机优化网络的初始权值。将提取的特征分别输入到两种典型的聚类算法中进行聚类,并结合态势函数和实际战场情况修正聚类结果,形成正确的态势分类标准。在实验部分,分别进行了最优参数调整、关键特征提取、聚类以及修正实验。实验结果表明,模型态势分类正确率和运行时间均满足应用需求,实例评估结果与客观态势一致性强,所提方法具有实际应用价值。     

基于不确定性的末制导初始参数优化设计方法

为量化无动力滑翔飞行器末制导初始参数不确定性的综合影响,提升飞行器落点精度,提出基于不确定性的末制导初始参数优化设计方法。面向飞行器末端高动态打击需求,采用落角约束下的滑模变结构导引律进行实时弹道成型,进而考虑末制导初始参数的不确定性。以落点有效毁伤半径概率和落点圆概率偏差为多优化目标,建立基于不确定性的末制导初始参数及制导律参数优化模型。针对这一不确定性优化模型,研究利用高效全局优化和蒙特卡洛方法,给出末制导初始参数及制导律参数的最优设计方案。仿真结果表明:该方法能显著提升落点精度,为方案设计阶段飞行器末制导交接点的选取提供决策支持。     

证据推理法在C4ISR系统可靠性评价中的应用

首先参考外军NATO COBP阐述了不确定性处理在C4ISR系统评价中的重要性,鉴于ER方法的先进思想,介绍了ER(Evidential Reasoning)方法在不确定条件下的基本评价框架,并对ER方法和AHP方法,D-S证据理论以及模糊综合评价方法进行了比较综合,针对我军C4ISR系统可靠性评价中的数据不完备等实际问题,初步应用此方法进行评价,对决策人员有一定的参考意义.     

飞机结构疲劳可靠度贝叶斯组合预测

在实际工程中,常常需要利用模型去描述和分析问题。然而模型亦存在不确定性,即可能存在多个描述同一现象的模型,例如多个疲劳分析的模型。针对飞机结构的疲劳可靠性问题,提出在考虑三种裂纹扩展模型下基于贝叶斯公式的疲劳可靠度组合预测方法。针对不同应力水平下飞机结构试件的裂纹扩展数据建立了三种随机裂纹扩展模型;在考虑模型参数不确定性条件下,采用贝叶斯模型平均方法对三种模型进行组合;基于组合模型分析结构的可靠度。所提方法在分析飞机结构疲劳可靠度上,采用了组合模型,能够最大限度保障结果的稳定性。此外,考虑了模型参数的不确定性,能够得到更为合理的裂纹扩展预测分布和可靠度预测值。给出的实例及分析结果表明所提方法可行。     

Prospect theory and foreign policy decision-making: Underexposed issues,advancements, and ways forward

How much mileage can we get out of prospect theory to explain foreign policy decision-making? To answer this question, we first argue that risk as outcome uncertainty is the appropriate definition in prospect-theoretical applications. Then, we indicate that probability weighting—a crucial component of prospect theory—is typically ignored in such applications. We argue why this is problematic and suggest how to move forward. Next, we discuss how to establish the reference point in the face of outcomes in multiple dimensions, as is typically the case in foreign policy decision-making. Finally, we discuss what we have learnt regarding prospect theory’s scope conditions and the differences across individuals in the theory’s applicability. Overall, our contribution lies in identifying several underexposed or neglected issues (e.g., the definition of risk and probability weighting), in examining the advancements regarding prospect theory’s scope conditions, and in discussing avenues for further research.     

A note on “resource flexibility with responsive pricing”

We revisit the capacity investment decision problem studied in the article “Resource Flexibility with Responsive Pricing” by Chod and Rudi [Operations Research 53, (2005) 532–548]. A monopolist firm producing two dependent (substitutable or complementary) products needs to determine the capacity of one flexible resource under demand risk so as to maximize its expected profit. Product demands are linear functions of the prices of both products, and the market potentials are random and correlated. We perform a comparative statics analysis on how demand variability and correlation impact the optimal capacity and the resulting expected profit. In particular, C&R study this problem under the following assumptions/approximations: (i) demand intercepts follow a bivariate Normal distribution; (ii) demand uncertainty is of an additive form; (iii) and under approximate expressions for the optimal capacity and optimal expected profit. We revisit Propositions 2, 3, 4, 5, and 10 of C&R without these assumptions and approximations, and show that these results continue to hold (i) for the exact expressions for the optimal expected profit and optimal capacity, and (ii) under any arbitrary continuous distribution of demand intercepts. However, we also show that the additive demand uncertainty is a critical assumption for the C&R results to hold. In particular, we provide a case of multiplicative uncertainty under which the C&R results (Propositions 2 and 3) fail. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

基于Monte-Carlo方法的火炮瞄准校验系统精度评定

基于双经纬仪的火炮瞄准校验系统需要给出其校验精度,但传统的测量不确定度传递公式需要大量微分运算,校验精度计算很复杂,为此,提出了基于Monte-Carlo方法的火炮瞄准校验系统测量不确定度的评定。以调炮精度校验为例分析了校验系统的原理,结合调炮精度校验过程设计了基于Monte-Carlo方法的测量不确定度评定过程,分析比较了计算量的大小,并将评定结果与传统评定方法的结果进行比较。结果表明:二者一致性良好,Monte-Carlo方法能够完成对瞄准校验系统各校验项目的校验精度评定,并且简化了计算,节省了时间。     

基于改进蚁群算法的战时不确定性路径规划模型求解

战时路径规划的不确定性包括参数、约束条件和决策目标等的不确定性。以基本蚁群算法为基础,结合随机模拟和模糊模拟技术,提出了一种改进蚁群算法来求解战时不确定性路径规划问题,并通过仿真示例与其他算法进行了比较。结果表明：该算法求解效率更高,求解过程更为直观,能够满足战时不确定性路径规划问题的研究。