Two‐machine flow shop scheduling with common due window to minimize weighted number of early and tardy jobs

This article studies two due window scheduling problems to minimize the weighted number of early and tardy jobs in a two‐machine flow shop, where the window size is externally determined. These new scheduling models have many practical applications in real life. However, results on these problems have rarely appeared in the literature because of a lack of structural and optimality properties for solving them. In this article, we derive several dominance properties and theorems, including elimination rules and sequencing rules based on Johnsos order, lower bounds on the penalty, and upper bounds on the window location, which help to significantly trim the search space for the problems. We further show that the problems are NP‐hard in the ordinary sense only. We finally develop efficient pseudopolynomial dynamic programming algorithms for solving the problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

灰色逼近理想解排序法在目标威胁评估中的应用

针对空袭目标的威胁判断问题,分析了空袭目标威胁评判的准则;据此给出了影响目标威胁的主要因素.并给出了主要因素的隶属函数.在此基础上,结合灰色关联度和逼近理想解排序法,用灰色关联度替代逼近理想解排序法中传统的距离,给出了灰色逼近理想解排序法模型;最后,将模型运用于空袭目标威胁排序中,以实例证实此方法的科学性与可行性.     

备件保障能力评估模型研究

备件保障在武器装备作战中具有重要地位.筹措率、缺货数和适用率是备件保障能力评估的三个主要指标,在此基础上建立了战时备件保障能力评估的数学模型.实例表明评估模型符合备件保障的实际情况,模型运行的结果合理、可信,部分解决了部队战时或机动时备件保障评估的问题,为确定合理的备件存储策略,提高武器装备的战备完好性和作战能力提供了决策的依据.     

基于模糊折衷理论的目标威胁度综合评价模型

由于海洋环境的复杂性和水声信道的特殊性,多目标跟踪过程中的目标威胁评估一直是水声信号处理领域的突出难题.目标威胁度是目标分配的重要依据,必须综合考虑目标的多种特征信息.针对水下声自导系统,提出了目标威胁度评估指标体系,构建隶属度函数对各个指标进行了量化,并建立了基于模糊折衷的目标威胁度综合评价模型.仿真结果证明该方法能有效节省航程,为提高水下航行器的作战效能提供了有效途径.     

陆基导弹武器系统生存能力研究

导弹武器系统的生存能力是一个关键的战技指标.国内外对导弹武器系统生存能力的分析评估十分重视,已开展了许多研究工作.介绍了研究陆基导弹武器系统生存能力的重要性,对其概念、定性评估模型、解析评估模型、仿真评估模型和提高生存能力的途径等几方面的研究进行了综述和分析,并就相关问题提出了一些看法.     

装备采购风险的BP神经网络评价模型

装备采购具有高风险的特点,科学、准确的风险评价对装备采购至关重要.传统的评价方法主观因素太强,而BP神经网络模型为装备采购风险评价开辟了新途径.首先介绍了人工神经网络模型的原理和BP算法,然后结合实例给出了用于装备采购风险评价的BP神经网络模型,采用该模型获得结果令人满意.     

雷达分队作战能力评估

雷达系统本身就是一个庞大复杂的系统,而雷达分队的作战能力评估涉及的指标因素更是众多且难以量化,作战能力评估的关键是建立作战能力评估指标体系,在参考众多专家意见的基础上建立了评估指标体系,并运用特尔斐法确定了各指标的权重,最后运用改进的模糊综合评估法对雷达分队的作战能力进行了量化评估,该模型对雷达分队作战能力的评估有很强的实用性.     

坦克火力运用自动化系统目标威胁评估模块

信息化条件下战场威胁的复杂性和现代信息技术的高度发展,使坦克火力动用自动化系统既十分必要,又将成为可能.目标威胁评估模块是该系统的核心部分.通过建立"目标类型、相对距离、相对速度、打击角度和攻击状态"的指标体系,动用模糊多属性决策理论和方法,建立目标威胁评估模型,对于未来坦克火力动用自动化系统的建设具有一定的借鉴作用.     

基于模糊熵的装备保障性评估模型

给出了一种基于模糊熵的不确定区间属性评估方法,用于在大量不确定区间信息和只有判断矩阵情况下,对多个待评价指标的方案进行评估.通过不确定属性评价矩阵的熵权计算,对多个方案进行优选评估,得出最优方案,并将这一方法应用在装备保障性评估中,实例证明此评估方法结果准确,可操作性较强,具有一定的推广价值.