遗传算法的预防性维修周期优化方法

考虑到预防维修可以提高系统生产效益的同时,故障率会随着维修次数的增加而上升,引入役龄回退因子对预防维修活动前后系统性能的动态变化进行描述;而且还充分考虑到系统预防维修周期随维修次数的变化情况,建立了系统预防维修周期优化模型,并将改进后的遗传算法运用于模型的优化求解.重点研究了系统总效益随预防维修次数的变化率, 从而有效地帮助决策者判定系统何时进行更新替换.     

基于模糊推理的目标攻潜武器判断

介绍了模糊推理理论基本思想,分析了判断目标攻潜武器的战术依据.针对潜艇水下收集目标信息的特点,应用模糊推理理论建立了目标攻潜武器类型的判断规则,并给出推导过程和实现方式,在把模糊推理理论应用于潜艇威胁判断模型的研究上做了初步尝试.     

舰艇编队对空中目标的威胁程度判断模型

根据舰艇编队对空防御的要求,综合考虑了目标运动状态和历史战术动作,将目标攻击企图和攻击达成概率纳入目标威胁判断之中,建立了空中运动目标实时威胁程度判断模型,为编队防空作战火力组织提供了基本依据。该模型解决了传统静态威胁判断模型存在的一些问题,模型的仿真结果符合一般战术推理和经验判断,能较好地描述空中目标威胁程度的变化,可以为编队防空指挥自动化决策研究提供一定的参考。     

多UCAV协同作战自主任务规划系统

在分析未来多UCAV协同作战任务的基础上,构建了一个多UCAV协同作战的自主任务规划系统结构,探讨了其运行机制。提出这个系统的核心是自主规划器,它应该具有任务分解、智能信息处理和机载任务规划与重规划能力。设计了自主规划器的初步方案。指出了这个系统下一步研究的任务和方向。     

基于多属性目标决策的威胁评估排序模型

针对当前海战中对舰艇编队构成威胁的空中目标的特点,运用目标多属性理论探索对空中目标的威胁排序问题.提出了一种对空中目标进行威胁评估和排序的方法,并通过示例介绍了威胁评估和排序的求解过程.该方法有效地解决了目标威胁评估与排序问题,提高了舰艇编队防空作战能力.     

海上机动目标群威胁预警建模及仿真实现

在陆基弹道导弹与海上机动目标的攻防作战过程中,需要研究双方火力控制范围的变化情况,并对海上机动目标群的威胁进行报警.根据海上机动目标运动特性,提出用灰色模型与自回归时序AR组合建模方法构建机动目标运动预测模型.依据此模型对双方火力控制区的变化进行预测,最后给出了海上机动目标威胁预警仿真系统的实现思路.解决了陆基弹道导弹对抗海上机动目标群的作战保障工程的部分问题,具有较强军事应用价值.     

基于串联模型框架的目标威胁评估

针对防空武器系统防空作战特点,在对目标威胁程度影响因素分析的基础之上,运用模糊理论和多模型理论探索目标威胁评估问题。设计了一种基于串联模型框架的目标威胁评估方法,该方法采用3个基本模型,基于串联系统思想,经模糊推理融合,得到空中来袭目标的威胁程度。该评估方法层次分明、直观有效,能够较好地满足海上对空防御作战中对目标威胁评估的需要,为舰指挥员决策和防空武器系统火力分配提供一定的依据。     

On the reduction of total‐cost and average‐cost MDPs to discounted MDPs

This article provides conditions under which total‐cost and average‐cost Markov decision processes (MDPs) can be reduced to discounted ones. Results are given for transient total‐cost MDPs with transition rates whose values may be greater than one, as well as for average‐cost MDPs with transition probabilities satisfying the condition that there is a state such that the expected time to reach it is uniformly bounded for all initial states and stationary policies. In particular, these reductions imply sufficient conditions for the validity of optimality equations and the existence of stationary optimal policies for MDPs with undiscounted total cost and average‐cost criteria. When the state and action sets are finite, these reductions lead to linear programming formulations and complexity estimates for MDPs under the aforementioned criteria.© 2017 Wiley Periodicals, Inc. Naval Research Logistics 66:38–56, 2019     

DEA models for two‐stage processes: Game approach and efficiency decomposition

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). This tool has been utilized by a number of authors to examine two‐stage processes, where all the outputs from the first stage are the only inputs to the second stage. The current article examines and extends these models using game theory concepts. The resulting models are linear, and imply an efficiency decomposition where the overall efficiency of the two‐stage process is a product of the efficiencies of the two individual stages. When there is only one intermediate measure connecting the two stages, both the noncooperative and centralized models yield the same results as applying the standard DEA model to the two stages separately. As a result, the efficiency decomposition is unique. While the noncooperative approach yields a unique efficiency decomposition under multiple intermediate measures, the centralized approach is likely to yield multiple decompositions. Models are developed to test whether the efficiency decomposition arising from the centralized approach is unique. The relations among the noncooperative, centralized, and standard DEA approaches are investigated. Two real world data sets and a randomly generated data set are used to demonstrate the models and verify our findings. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

基于证据熵对不确定性度量的决策表约简

知识约简是粗糙集理论的核心内容之一,产生的粗糙决策规则往往具有一定的不确定性.在变精度粗糙集的基础上,本文构造了符合证据理论框架的一组焦元,利用基本概率分配函数计算了证据的总体信息熵,度量了决策表的不确定性;以该度量作为启发信息,给出了决策表的启发式知识约简算法.计算实例表明了本文方法的有效性.