LCDP SO算法在协同空战攻击决策中的运用?

为了探索提高协同空战攻击决策算法性能的途径,将多子群粒子群优化理论用于求解协同空战攻击决策,利用生命周期粒子群模型（ LCPSO）,提出了一种生命周期离散粒子群（ LCDPSO）协同空战攻击决策算法。基于典型空战想定背景,仿真验证了算法的有效性。通过统计实验的方法,分析比较了LCDPSO协同空战攻击决策算法与多种智能决策算法的准确性、可靠性和快速性,研究结果证明LCDPSO协同空战攻击决策算法优良的综合性能。     

LCDP SO算法在协同空战攻击决策中的运用

为了探索提高协同空战攻击决策算法性能的途径,将多子群粒子群优化理论用于求解协同空战攻击决策,利用生命周期粒子群模型（ LCPSO）,提出了一种生命周期离散粒子群（ LCDPSO）协同空战攻击决策算法。基于典型空战想定背景,仿真验证了算法的有效性。通过统计实验的方法,分析比较了LCDPSO协同空战攻击决策算法与多种智能决策算法的准确性、可靠性和快速性,研究结果证明LCDPSO协同空战攻击决策算法优良的综合性能。     

引导方式的代价评估

引导方式的选择是超视距舰舰导弹攻击中最重要的决策问题。它牵动到兵力的使用问题,是目前作战部队急待解决的重点课题之一。传统的评估方法往往只侧重最终的数学结果而忽视对评估过程的揭示,难以被部队指挥员接受,从而影响了数学评估在作战指挥中的应用。给出了引导方式损益评估中代价评估的方法。该方法采用表格作业方式,可帮助指挥员全面掌握决策要素,自主控制各因素的作用,符合部队指挥员对决策透明的要求。计算过程简洁,易于在部队推广使用。     

基于云重心理论的装甲团主攻方向优选

主攻方向选择的合理与否对战斗的成败有重大影响,合理确定主攻方向是战斗决心的核心内容。为解决主攻方向优选问题,引入了云重心理论,根据主攻方向选择的基本要求,通过对相关军事问题的结构化分析,建立了主攻方向的评价指标体系,并采用层次分析法确定各指标的权重。然后通过专家打分法,确定各属性指标的值,再运用云重心理论进行评估,最后通过对比评估结果确定最优主攻方向。通过多种方法的运用,较好的完成了装甲团主攻方向优选的问题,为科学解决主攻方向的优选问题提供了一定的方法参考和理论依据。     

An application of yield management for Internet Service Providers

In this paper we study strategies for better utilizing the network capacity of Internet Service Providers (ISPs) when they are faced with stochastic and dynamic arrivals and departures of customers attempting to log‐on or log‐off, respectively. We propose a method in which, depending on the number of modems available, and the arrival and departure rates of different classes of customers, a decision is made whether to accept or reject a log‐on request. The problem is formulated as a continuous time Markov Decision Process for which optimal policies can be readily derived using techniques such as value iteration. This decision maximizes the discounted value to ISPs while improving service levels for higher class customers. The methodology is similar to yield management techniques successfully used in airlines, hotels, etc. However, there are sufficient differences, such as no predefined time horizon or reservations, that make this model interesting to pursue and challenging. This work was completed in collaboration with one of the largest ISPs in Connecticut. The problem is topical, and approaches such as those proposed here are sought by users. © 2001 John Wiley & Sons, Inc., Naval Research Logistics 48:348–362, 2001     

基于组合赋权方法的多目标攻击排序

针对未来超视距空战条件下的多目标攻击排序问题,以多属性决策理论为基础,提出了一种多目标攻击排序的综合优势指数模型.多属性决策问题的关键是确定各属性权重,综合各种赋权法的特点,采用离差平方和的最优赋权方法求解多目标攻击的综合优势指数.最后,通过实例分析,说明采用该方法进行多目标排序,有利于提高决策的准确性.     

在线学习的主用户仿冒攻击策略

在认知无线网络中,次用户通过频谱感知来学习频谱环境,从而接入那些没有被主用户占用的频谱空隙。事实上,多种恶意攻击的存在会影响次用户频谱感知的可靠性。只有深入研究恶意攻击策略,才能确保认知无线网络的安全。基于此,研究了一种认知无线网络中的欺骗性干扰策略,即主用户仿冒攻击策略,该攻击策略通过在信道上传输伪造的主用户信号来降低次用户频谱感知的性能。具体来说,将攻击策略问题建模为在线学习问题,并提出基于汤普森采样的攻击策略以实现在探索不确定信道和利用高性能信道间的权衡。仿真结果表明,与现有的攻击策略相比,提出的攻击策略能更好地通过在线学习优化攻击决策以适应非平稳的认知无线网络。     

A new storage reduction technique for the solution of the group problem

This paper shows that by making use of an unusual property of the decision table associated with the dynamic programming solution to the goup problem, it is possible to dispense with table storage as such, and instead overlay values for both the objective and history functions. Furthermore, this storage reduction is accomplished with no loss in computational efficiency. An algorithm is presented which makes use of this technique and incorporates various additional efficiencies. The reduction in storage achieved for problems from the literature is shown.     

Dynamic pricing and inventory control with learning

Optimal operating policies and corresponding managerial insight are developed for the decision problem of coordinating supply and demand when (i) both supply and demand can be influenced by the decision maker and (ii) learning is pursued. In particular, we determine optimal stocking and pricing policies over time when a given market parameter of the demand process, though fixed, initially is unknown. Because of the initially unknown market parameter, the decision maker begins the problem horizon with a subjective probability distribution associated with demand. Learning occurs as the firm monitors the market's response to its decisions and then updates its characterization of the demand function. Of primary interest is the effect of censored data since a firm's observations often are restricted to sales. We find that the first‐period optimal selling price increases with the length of the problem horizon. However, for a given problem horizon, prices can rise or fall over time, depending on how the scale parameter influences demand. Further results include the characterization of the optimal stocking quantity decision and a computationally viable algorithm. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 303–325, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10013     

Optimizing usage and maintenance decisions for k‐out‐of‐n systems of moving assets

We consider an integrated usage and maintenance optimization problem for a k‐out‐of‐n system pertaining to a moving asset. The k‐out‐of‐n systems are commonly utilized in practice to increase availability, where n denotes the total number of parallel and identical units and k the number of units required to be active for a functional system. Moving assets such as aircraft, ships, and submarines are subject to different operating modes. Operating modes can dictate not only the number of system units that are needed to be active, but also where the moving asset physically is, and under which environmental conditions it operates. We use the intrinsic age concept to model the degradation process. The intrinsic age is analogous to an intrinsic clock which ticks on a different pace in different operating modes. In our problem setting, the number of active units, degradation rates of active and standby units, maintenance costs, and type of economic dependencies are functions of operating modes. In each operating mode, the decision maker should decide on the set of units to activate (usage decision) and the set of units to maintain (maintenance decision). Since the degradation rate differs for active and standby units, the units to be maintained depend on the units that have been activated, and vice versa. In order to minimize maintenance costs, usage and maintenance decisions should be jointly optimized. We formulate this problem as a Markov decision process and provide some structural properties of the optimal policy. Moreover, we assess the performance of usage policies that are commonly implemented for maritime systems. We show that the cost increase resulting from these policies is up to 27% for realistic settings. Our numerical experiments demonstrate the cases in which joint usage and maintenance optimization is more valuable. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 418–434, 2017     

基于三角模糊数的登陆点选择模型

针对登陆点选择的特点,对登陆点选择步骤进行分析,确定影响的四个基本因素,利用三角模糊数型多属性决策方法建立登陆点选择模型。与层次分析法相比,此算法时更能突出登陆点选择时,指挥者对登陆点的偏好所影响登陆点选择的基本因素值只能大概给出,基本因素之间的权重信息也不能完全确知的特点,通过计算机编程,此模型能够快速地实现对登陆点的排序选优,为指挥员提供辅助决策。     

The LP/POMDP marriage: Optimization with imperfect information

A new technique for solving large‐scale allocation problems with partially observable states and constrained action and observation resources is introduced. The technique uses a master linear program (LP) to determine allocations among a set of control policies, and uses partially observable Markov decision processes (POMDPs) to determine improving policies using dual prices from the master LP. An application is made to a military problem where aircraft attack targets in a sequence of stages, with information acquired in one stage being used to plan attacks in the next. © 2000 John Wiley & Sons, Inc., Naval Research Logistics 47: 607–619, 2000     

舰船装备修理规划方案的DEA分析与优化

运用DEA方法对舰船装备修理规划方案的有效性进行了分析,对非有效单元的改进途径进行了探讨,为舰船修理规划的制定提供了科学依据.     

武器装备论证及AHP层次分析法的应用

论证是装备研发过程中首要的、不可或缺的过程，涉及到不同层次、不同领域、不同学科的知识（自然科学、社会科学、工程技术等），要处理定性的和定量的不同类型的信息和数据。如何把这些知识综合集成起来，解决决策和组织管理问题，需要有科学的方法来支持。本文试以AHP层次分析法解决实际问题一例，见诸论证方法之一斑，为涉及装备论证的科研、管理、决策人员自觉运用科学论证方法起到一点积极作用。     

Scheduling policies for an antiterrorist surveillance system

This article concerns scheduling policies in a surveillance system aimed at detecting a terrorist attack in time. Terrorist suspects arriving at a public area are subject to continuous monitoring, while a surveillance team takes their biometric signatures and compares them with records stored in a terrorist database. Because the surveillance team can screen only one terrorist suspect at a time, the team faces a dynamic scheduling problem among the suspects. We build a model consisting of an M/G/1 queue with two types of customers—red and white—to study this problem. Both types of customers are impatient but the reneging time distributions are different. The server only receives a reward by serving a red customer and can use the time a customer has spent in the queue to deduce its likely type. In a few special cases, a simple service rule—such as first‐come‐first‐serve—is optimal. We explain why the problem is in general difficult and we develop a heuristic policy motivated by the fact that terrorist attacks tend to be rare events. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

Capacity expansion and cost efficiency improvement in the warehouse problem

The warehouse problem with deterministic production cost, selling prices, and demand was introduced in the 1950s and there is a renewed interest recently due to its applications in energy storage and arbitrage. In this paper, we consider two extensions of the warehouse problem and develop efficient computational algorithms for finding their optimal solutions. First, we consider a model where the firm can invest in capacity expansion projects for the warehouse while simultaneously making production and sales decisions in each period. We show that this problem can be solved with a computational complexity that is linear in the product of the length of the planning horizon and the number of capacity expansion projects. We then consider a problem in which the firm can invest to improve production cost efficiency while simultaneously making production and sales decisions in each period. The resulting optimization problem is non‐convex with integer decision variables. We show that, under some mild conditions on the cost data, the problem can be solved in linear computational time. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 367–373, 2016     

基于变异离散粒子群的协同空战攻击决策算法

为了提高智能协同空战攻击决策算法性能,将变异策略引入到DPSO(Discrete Particle Swarm Optimization)协同空战攻击决策算法中,提出了一种新的基于变异离散粒子群(Mutation Discrete Particle Swarm Optimization,MDPSO)的协同空战攻击决策算法。基于典型空战想定背景,仿真验证了算法的有效性。采用对比实验方法,基于准确性、可靠性和快速性等关键性能指标,分析比较了基于MDPSO协同空战攻击决策算法与多种智能决策算法,验证了基于MDPSO的协同空战攻击决策算法有着较好的综合性能。     

反舰导弹攻击三位一体图形化战术决策

反舰导弹攻击三位一体图形化战术决策,是将反舰导弹攻击时的捕捉概率决策、火分目标决策、航路规划决策等三种决策合为一体,以图形化的操作界面作为主要的决策交互手段,通过对各种图形的操作达到战术决策的目的。重点分析和研究了反舰导弹攻击三位一体图形化战术决策的基本思路、决策过程、设计原则、作战流程和图形设计,对开展反舰导弹攻击战术决策软件的研制具有一定的理论参考价值。     

一种锥比率数据包络分析模型及有关结论

本文研究一种锥比率数据包络分析模型,它既能反映各项输入或输出的相对重要性和对某些决策单元的偏好,又可单纯地评价决策单元间的相对技术有效性。本文着重讨论这一模型的DEA有效性与多目标规划非支配解的关系以及有效决策单元的存在性等问题,论证了有关结论。     

基于区间数TOPSIS法优选空军战役作战计划

针对战役作战计划优选中的不确定性,运用区间分析和TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)法探索空军战役作战计划优选问题.通过引进区间数乘法运算,将区间数多指标决策问题转变为指标为区间数的多指标决策问题,进而给出区间数多指标决策问题的TOPSIS法,对空军战役作战计划进行排序选优.与传统方法相比较,该方法较好地解决了评价指标为不确定值时的多指标决策问题.