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。     

一种航天侦察任务-资源匹配的负载均衡方法

当前航天侦察任务预处理方法一般仅从任务需求的角度考虑资源分配问题,容易导致负载不均衡,对此提出了一种综合考虑任务需求与资源负载的任务-资源匹配方法.分析了资源负载均衡需要考虑的任务要素以及相应的描述方法,提出用任务执行概率度量卫星资源负载状况,基于任务流模型给出了单任务执行概率的估算公式,并给出了一种简易的多任务执行概...     

基于SWE的空天资源对地观测协同任务规划服务模型

空天协同对地观测是对地观测领域的新趋势。为解决现有空天资源规划调度系统相对独立、协同困难的问题,分析总结了SWE(Sensor Web Enablement)标准,并在此基础上提出了空天资源对地观测协同任务规划服务模型。在此模型中,为实现观测资源共享,建立了空天观测资源传感器描述模型,能够描述典型空天资源的载荷平台、观测机理、定位信息、使用约束和工作特性等信息;为简化观测请求交互流程,基于SOA技术对SWE标准操作进行封装及简化,用户可以在不了解观测平台细节信息的情况下提交观测数据请求。为验证模型有效性,构建了空天资源对地观测协同任务规划实验平台,结果表明该模型具有较强的可实现性和适应性。     

目标数据关联多因素分布式推理判决系统推理结构

目标数据关联多因素分布式推理判决系统 ,是一基于分布式判决分类系统理论进行目标数据关联分析的大规模推理判决系统 ,在C3I系统中有着广泛的应用前景。本文在分析推理判决系统的推理规则类型和状态类型的基础上 ,给出系统的推理结构图 ,该推理结构是正关联神经推理结构     

ABDARDSS中基于候选消除学习算法的知识推理

飞机战伤评估和修理的知识表示与推理机制,是建立飞机战伤评估与修理决策支持系统(ABDARDSS)需要解决的一个关键问题.采用一种候选消除算法来提取战伤评估与修理样本数据的变型空间,以变型空间实现战伤评估与修理决策支持系统中的知识表示和推理.对于ABDARDSS来说,候选消除学习算法是一种新的有效的知识推理方法.     

不确定失效阈值影响下考虑设备剩余寿命预测信息的最优替换策略

为了进一步提升设备维修决策的科学性,通过建立综合设备剩余寿命预测数据与不确定失效阈值的最优维修决策模型,实现了不可维修设备的最优替换策略.构建基于非线性Wiener过程的设备性能退化模型,并采用极大似然法估计退化模型参数;提出一种基于期望最大(Expectation Maximization,EM)算法的不确定失效阈值...     

AHP-SWOT分析法在控制下交付行动方案决策中的应用

控制下交付作为打击毒品犯罪尤其是跨国有组织毒品犯罪的重要措施和手段,已发挥越来越重要的作用,是其他缉毒措施和手段无法取代的。但是,由于毒品犯罪的集团化、国际化、种类多元化程度加剧,使得控制下交付的复杂性与不确定性越来越明显。鉴于此,将AHP植入SWOT分析法中,以定性和定量相结合的科学方法解决控制下交付行动方案的优选问题,对提高控制下交付的成功率具有重要意义。     

Dynamic coordination of production planning and sales admission control in the presence of a spot market

We consider the decision‐making problem of dynamically scheduling the production of a single make‐to stock (MTS) product in connection with the product's concurrent sales in a spot market and a long‐term supply channel. The spot market is run by a business to business (B2B) online exchange, whereas the long‐term channel is established by a structured contract. The product's price in the spot market is exogenous, evolves as a continuous time Markov chain, and affects demand, which arrives sequentially as a Markov‐modulated Poisson process (MMPP). The manufacturer is obliged to fulfill demand in the long‐term channel, but is able to rein in sales in the spot market. This is a significant strategic decision for a manufacturer in entering a favorable contract. The profitability of the contract must be evaluated by optimal performance. The current problem, therefore, arises as a prerequisite to exploring contracting strategies. We reveal that the optimal strategy of coordinating production and sales is structured by the spot price dependent on the base stock and sell‐down thresholds. Moreover, we can exploit the structural properties of the optimal strategy to conceive an efficient algorithm. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Optimal control of a production‐inventory system with both backorders and lost sales

We consider the optimal control of a production inventory‐system with a single product and two customer classes where items are produced one unit at a time. Upon arrival, customer orders can be fulfilled from existing inventory, if there is any, backordered, or rejected. The two classes are differentiated by their backorder and lost sales costs. At each decision epoch, we must determine whether or not to produce an item and if so, whether to use this item to increase inventory or to reduce backlog. At each decision epoch, we must also determine whether or not to satisfy demand from a particular class (should one arise), backorder it, or reject it. In doing so, we must balance inventory holding costs against the costs of backordering and lost sales. We formulate the problem as a Markov decision process and use it to characterize the structure of the optimal policy. We show that the optimal policy can be described by three state‐dependent thresholds: a production base‐stock level and two order‐admission levels, one for each class. The production base‐stock level determines when production takes place and how to allocate items that are produced. This base‐stock level also determines when orders from the class with the lower shortage costs (Class 2) are backordered and not fulfilled from inventory. The order‐admission levels determine when orders should be rejected. We show that the threshold levels are monotonic (either nonincreasing or nondecreasing) in the backorder level of Class 2. We also characterize analytically the sensitivity of these thresholds to the various cost parameters. Using numerical results, we compare the performance of the optimal policy against several heuristics and show that those that do not allow for the possibility of both backordering and rejecting orders can perform poorly.© 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

基于混合粒子群优化的多目标决策新方法

多目标优化问题中的一个关键在于合理地评判各有效解的优劣。通过引入灰色系统理论中灰色关联度的概念作为评判准则,结合粒子群优化算法进行有约束多目标规划问题的研究。提出了一种新的不可行解的保留策略,进化过程中以此策略保留适量的不可行解,有利于增强对约束边界附近可能的最优解的搜索,同时,针对粒子群优化算法的容易陷入局部最优的缺点,实现了以粒子群优化为载体的混合算法:即对全局极值邻域进一步混沌搜索寻优。仿真结果表明改进的算法对多目标决策问题是有效的。