An attacker‐defender model for analyzing the vulnerability of initial attack in wildfire suppression

Wildfire managers use initial attack (IA) to control wildfires before they grow large and become difficult to suppress. Although the majority of wildfire incidents are contained by IA, the small percentage of fires that escape IA causes most of the damage. Therefore, planning a successful IA is very important. In this article, we study the vulnerability of IA in wildfire suppression using an attacker‐defender Stackelberg model. The attacker's objective is to coordinate the simultaneous ignition of fires at various points in a landscape to maximize the number of fires that cannot be contained by IA. The defender's objective is to optimally dispatch suppression resources from multiple fire stations located across the landscape to minimize the number of wildfires not contained by IA. We use a decomposition algorithm to solve the model and apply the model on a test case landscape. We also investigate the impact of delay in the response, the fire growth rate, the amount of suppression resources, and the locations of fire stations on the success of IA.     

武器系统研制单位管理水平的贝叶斯评估分析

将Bayes理论应用于对武器系统研制单位管理水平的评估.克服了验前评估方法主观性大的缺陷,充分利用收集到的补充信息,对评估结果作了后验预分析,得到了更加可信的评估结论,从而改进了决策过程.     

美国空军研究实验室组织管理与科研规划分析

美国空军研究实验室是一个在空中、太空和网络空间领域处于全球领先地位的科研机构,承担着美国空军国防高技术项目的规划、管理和实施工作,是美国空军保持技术优势的重要研究力量。本文剖析了美国空军实验室的使命任务、组织管理和科研规划,总结了实验室在人才培养和科研任务制定方面的经验和方法。     

论强化消防部队管理执行力

安全事故频发,引发部队管理危机,究其原因是多方面的,但是,管理中的＂三弱化＂是主因。如何提升管理中的执行力,应从四个方面入手,即强化培训式管理、走动式管理、数字化管理、问责制管理。夯实执行根基,提升执行监管,量化执行标准,重施执行奖惩,确保消防部队管理执行力的提升,是减少安全事故,确保部队高度安全稳定的根本途径。     

一个通用数据库管理工具的研究与实现

目前我国行业信息化建设已经跨越了初级阶段,但在许多行业的信息化应用中还存在着烟囱效应和信息孤岛效应。为了将行业信息化引向深入,需要对原有系统进行数据集成。文中首先对数据集成的基本概念进行了探讨,并进一步对数据集成的一个重要工具——通用数据库管理工具的功能及实现方法进行了研究。文中所讨论的通用数据库管理工具与其他工具的区别在于:能够对物理上分布的、异构的多个数据库进行管理,还能够在更高的层次上对多个数据库中的数据进行逻辑上的重新组织。文中所讨论的通用数据库管理工具是为最终用户服务的,而不是一个开发工具。     

Dynamic inventory management with cash flow constraints

In this article, we consider a classic dynamic inventory control problem of a self‐financing retailer who periodically replenishes its stock from a supplier and sells it to the market. The replenishment decisions of the retailer are constrained by cash flow, which is updated periodically following purchasing and sales in each period. Excess demand in each period is lost when insufficient inventory is in stock. The retailer's objective is to maximize its expected terminal wealth at the end of the planning horizon. We characterize the optimal inventory control policy and present a simple algorithm for computing the optimal policies for each period. Conditions are identified under which the optimal control policies are identical across periods. We also present comparative statics results on the optimal control policy. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2008     

Technical note: Finite-time regret analysis of Kiefer-Wolfowitz stochastic approximation algorithm and nonparametric multi-product dynamic pricing with unknown demand

We consider the problem of nonparametric multi-product dynamic pricing with unknown demand and show that the problem may be formulated as an online model-free stochastic program, which can be solved by the classical Kiefer-Wolfowitz stochastic approximation (KWSA) algorithm. We prove that the expected cumulative regret of the KWSA algorithm is bounded above by where κ₁, κ₂ are positive constants and T is the number of periods for any T = 1, 2, … . Therefore, the regret of the KWSA algorithm grows in the order of , which achieves the lower bounds known for parametric dynamic pricing problems and shows that the nonparametric problems are not necessarily more difficult to solve than the parametric ones. Numerical experiments further demonstrate the effectiveness and efficiency of our proposed KW pricing policy by comparing with some pricing policies in the literature.     

基于多Agent的分布式防空战场资源管理研究

分布式防空战场资源管理是构建现代防空体系的核心问题。基于对分布式防空战场资源管理特点的分析,提出了基于多智能体(Agent)的分布式防空战场资源管理体系结构,并深入分析了该管理系统的组成和功能,为实现定性和定量相结合的防空战场资源管理的综合集成,提供了一条有效的途径。     

Value management of diagnostic equipment with cancelation,no‐show,and emergency patients

Magnetic resonance imaging and other multifunctional diagnostic facilities, which are considered as scarce resources of hospitals, typically provide services to patients with different medical needs. This article examines the admission policies during the appointment management of such facilities. We consider two categories of patients: regular patients who are scheduled in advance through an appointment system and emergency patients with randomly generated demands during the workday that must be served as soon as possible. According to the actual medical needs of patients, regular patients are segmented into multiple classes with different cancelation rates, no‐show probabilities, unit value contributions, and average service times. Management makes admission decisions on whether or not to accept a service request from a regular patient during the booking horizon to improve the overall value that could be generated during the workday. The decisions should be made by considering the cancelation and no‐show behavior of booked patients as well as the emergency patients that would have to be served because any overtime service would lead to higher costs. We studied the optimal admission decision using a continuous‐time discrete‐state dynamic programming model. Identifying an optimal policy for this discrete model is analytically intractable and numerically inefficient because the state is multidimensional and infinite. We propose to study a deterministic counterpart of the problem (i.e., the fluid control problem) and to develop a time‐based fluid policy that is shown to be asymptotically optimal for large‐scale problems. Furthermore, we propose to adopt a mixed fluid policy that is developed based on the information obtained from the fluid control problem. Numerical experiments demonstrate that this improved policy works effectively for small‐scale problems. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 287–304, 2016     

时间管理在实时仿真中的改进

时问是分布仿真中的核心概念;时间管理是分布交互仿真的关键技术.探讨了分布交互仿真中高层体系结构(HLA)的时间管理机制,针对HLA在实时系统仿真方面的不足,分析其原因,提出了时间管理在实时仿真中的改进方法,对HLA在实时系统仿真中的应用有一定的参考价值.