装备保障指挥决策支持模型体系构建

以装备保障指挥决策过程为研究对象,针对传统决策方式准确性低、决策支持模型不全等问题,在分析装备保障指挥决策基本内涵及程序的基础上,提出了装备保障指挥决策支持模型三维框架结构,并对按功能和内容维展开的预测模型、评估模型和优化模型作了较为详细的描述。     

基于遗传算法(GA)的通信抗干扰决策引擎

基于认知无线电思想设计了一种多参数联合自适应通信抗干扰决策引擎,并利用遗传算法(Genet-ic Algorithm)作为决策算法,实现了系统频率、功率、调制方式、信道编码方式等参数的自适应选择。文章给出了算法的流程和仿真分析,仿真结果表明,该引擎对单目标函数以及有偏好的多目标函数的参数选择与理论值相符,验证了抗干扰决策引擎实现参数自适应选择的可行性和有效性。     

基于Matlab优化工具箱和VB语言的消磁电流决策

针对舰船消磁电流决策问题,利用Matlab优化工具箱对舰船模型进行了消磁实验,取得了良好的消磁效果.还应用VisualBasic语言与Matlab混合编程,开发了实用的消磁电流决策软件,能够提高消磁电流的决策效率.     

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     

机械设备视情维修决策系统的研究与开发

从技术的角度讨论了为机械设备开发维修决策支持系统的必要性、可行性及系统技术关键的解决策略,以及系统开发中对人力资源的需求,为开发这样的系统提供技术与管理上的支持.     

器材保障DSS中面向对象的模型库系统

主要论述了装甲器材保障决策支持系统中的面向对象的模型表示方法和模型库系统设计,并介绍了模型库系统设计中的关键问题模型规范、模型建立、模型控制和模型监控等.     

多人多任务动态决策建模的环境

就人的组队如何解决多任务环境中具有不定性情况下分布式资源管理等问题 ,在微机上采用具有音响—图形功能的 Turbo pascal程序语言 ,建立了一个可以控制的实验环境 ,经分析得出 :在此环境中 ,可以模拟、产生 C3分布系统方面基本的决策制作问题 ,且能充分适应于复杂性的逐步引入 ,可用作建立多人多任务动态决策模型的载体 ,开发组队决策的规范—描述理论。     

Optimal production ramp-up in the smartphone manufacturing industry

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production ramp-up model that can be applied to economically satisfy nonstationary demand for short-life-cycle products by high-tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp-up has been increasingly critical to the success of a new smartphone. In the production ramp-up, the key challenge is to match the increasing capacity to nonstationary demand. The high-tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high-dimensional Markov decision process (MDP) model to characterize the production ramp-up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high-tech smartphone manufacturers to better plan the production ramp-up.     

Revenue maximization in two-station tandem queueing systems

We study optimal pricing for tandem queueing systems with finite buffers. The service provider dynamically quotes prices to incoming price sensitive customers to maximize the long-run average revenue. We present a Markov decision process model for the optimization problem. For systems with two stations, general-sized buffers, and two or more prices, we describe the structure of the optimal dynamic pricing policy and develop tailored policy iteration algorithms to find an optimal pricing policy. For systems with two stations but no intermediate buffer, we characterize conditions under which quoting either a high or a low price to all customers is optimal and provide an easy-to-implement algorithm to solve the problem. Numerical experiments are conducted to compare the developed algorithms with the regular policy iteration algorithm. The work also discusses possible extensions of the obtained results to both three-station systems and two-station systems with price and congestion sensitive customers using numerical analysis.     

多属性模糊决策对舰炮射击目标优选

舰炮在现代高科技条件下海战中承担着重要的作战使命和任务,它能否完成被赋予的作战使命和任务,射击目标的优选起着决定性作用.为解决舰炮射击目标优选、决策问题,研究分析了多属性模糊决策方法,以及影响舰炮射击目标优选的因素,提出了多属性模糊决策方法的数学模型和求解最优射击目标排序的新方法.该方法在舰炮射击目标优选中的应用实例表明:通过定量和定性相结合,专家判断和信息处理相结合的多属性模糊决策方法对舰炮射击目标优选、决策具有一定的借鉴意义.