Equitable bandwidth allocation in content distribution networks

Applications for content distribution over networks, such as Video‐on‐Demand (VOD), are expected to grow significantly over time. Effective bandwidth allocation schemes that can be repeatedly executed must be deployed since new programs are often installed at various servers while other are deleted. We present a model for bandwidth allocation in a content distribution network that consists of multiple trees, where the root of each tree has a server that broadcasts multiple programs throughout the tree. Each network link has limited capacity and may be used by one or more of these trees. The model is formulated as an equitable resource allocation problem with a lexicographic maximin objective function that attempts to provide equitable service performance for all requested programs at the various nodes. The constraints include link capacity constraints and tree‐like ordering constraints imposed on each of the programs. We present an algorithm that provides an equitable solution in polynomial time for certain performance functions. At each iteration, the algorithm solves single‐link maximin optimization problems while relaxing the ordering constraints. The algorithm selects a bottleneck link, fixes various variables at their lexicographic optimal solution while enforcing the ordering constraints, and proceeds with the next iteration. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

基于主观综合评判的作战仿真可信度评估方法研究

可信度是作战仿真的生命线,对作战仿真系统建设全生命周期进行校核、验证与确认(verification,validation and accreditation,VVA)是确保其可信度的重要途径。合适的评估方法是进行可信度评估的前提条件。在全面分析与比较已有可信度评估方法基础上,针对作战仿真可信度评估中突出主题专家作用的特点,提出了一种基于主观综合评判的可信度评估方法,重点设计了基于专长权的主题专家权重定量计算方法。这些方法都被应用于"××作战仿真系统"的可信度评估工作中。实践表明,本文的研究对于作战仿真系统等复杂仿真系统的可信度评估非常有效。     

Revenue management with aircraft reassignment flexibility

A well‐studied problem in airline revenue management is the optimal allocation of seat inventory among different fare‐classes, given a capacity for the flight and a demand distribution for each class. In practice, capacity on a flight does not have to be fixed; airlines can exercise some flexibility on the supply side by swapping aircraft of different capacities between flights as partial booking information is gathered. This provides the airline with the capability to more effectively match their supply and demand. In this paper, we study the seat inventory control problem considering the aircraft swapping option. For theoretical and practical purposes, we restrict our attention to the class of booking limit policies. Our analytical results demonstrate that booking limits considering the swapping option can be considerably different from those under fixed capacity. We also show that principles on the relationship between the optimal booking limits and demand characteristics (size and risk) developed for the fixed‐capacity problem no longer hold when swapping is an option. We develop new principles and insights on how demand characteristics affect the optimal booking limits under the swapping possibility. We also develop an easy to implement heuristic for determining the booking limits under the swapping option and show, through a numerical study, that the heuristic generates revenues close to those under the optimal booking limits. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

协同通信中的基于信道统计信息的联合中继选择的功率分配

分析Rayleigh信道下分集/无分集的AF( Amplify and Forward)和DF( Decode and Forward)模式单中继传输的平均中断概率,给出解析表达式.在总功率约束下推导了4种模式下的统计最优功率分配的解析表达式来最小化系统的平均中断概率,并提出通过最佳中继选择进一步降低系统的平均中断概率...     

改进合同网协议的Agent动态任务分配

针对导弹作战任务规划中的动态任务分配问题,研究了MAS框架下任务分配的形式化描述和数学模型,对动态任务分配的特点进行了分析,运用合同网理论,提出了基于改进合同网协议的Agent动态任务分配算法,实现了对不确定开放环境中动态变化的任务分配方法。算例表明算法是有效的。     

基于改进广义成本函数的可靠性分配优化

可靠性分配优化问题的难点在于影响分配的各类因素与可靠性指标间关系的描述与表达.从可靠性成本与各类影响因素的内在关系人手,通过改进广义成本函数进行各类影响因素的融合处理,构建得出新的可靠性分配优化模型实现可靠性指标的综合求解.     

熵权灰色关联分析法的坦克夜间射击目标选择

针对夜间战场上坦克目标威胁评估问题,结合多目标决策问题,将熵权和灰色关联分析法相结合,提出一种基于熵权灰色关联分析的坦克夜间射击目标选择模型,并详细给出了该算法的思想、算法.该方法克服了传统评估算法中主观赋值的缺陷,最后利用实例证明了该方法的有效性.     

THEORY AND APPLICATION TO THE US MILITARY

An ever‐growing share of defence R&D expenditures is being dedicated to the development and fielding of integrative technologies that enable separate individual systems to work in a coordinated and synergistic fashion as a single system. This study explores the optimal defence budget allocation to the development and acquisition of weapon systems and to the development of integrative technologies. We develop a suitable optimization framework, and then use it to derive the optimal budget allocation and analyse its properties. Finally, we use US defence budget data to calibrate the parameters of the model and provide a quantitative measure for the apparent US military supremacy.     

多目标多决策者环境下防空反导装备体系资源分配与优化

作战体系具有层次高、规模大,各作战分系统间协调配合密切的特征。在作战任务中,这种多层次、多系统、多决策者、多目标的结构使得对各作战子系统的武器装备分配变得更为复杂。为解决此类复杂结构的优化问题,在层次化多目标分析方法基础之上,将层次系统的风险管理引入到模型中来,形成多目标多决策者资源分配模型多目标多决策者资源分配模型,用以解决在不确定风险环境下进行多目标资源分配的问题。利用MOMDRA模型建模空袭风险下防空反导体系的武器装备分配问题,在求解该模型的过程中采用系统分解的思路,利用权重法,站在不同决策者的角度得到该体系的Pareto最优资源分配方案,并通过一个实例来说明此方法的可行性。     

Screening multi-dimensional heterogeneous populations for infectious diseases under scarce testing resources,with application to COVID-19

Testing provides essential information for managing infectious disease outbreaks, such as the COVID-19 pandemic. When testing resources are scarce, an important managerial decision is who to test. This decision is compounded by the fact that potential testing subjects are heterogeneous in multiple dimensions that are important to consider, including their likelihood of being disease-positive, and how much potential harm would be averted through testing and the subsequent interventions. To increase testing coverage, pooled testing can be utilized, but this comes at a cost of increased false-negatives when the test is imperfect. Then, the decision problem is to partition the heterogeneous testing population into three mutually exclusive sets: those to be individually tested, those to be pool tested, and those not to be tested. Additionally, the subjects to be pool tested must be further partitioned into testing pools, potentially containing different numbers of subjects. The objectives include the minimization of harm (through detection and mitigation) or maximization of testing coverage. We develop data-driven optimization models and algorithms to design pooled testing strategies, and show, via a COVID-19 contact tracing case study, that the proposed testing strategies can substantially outperform the current practice used for COVID-19 contact tracing (individually testing those contacts with symptoms). Our results demonstrate the substantial benefits of optimizing the testing design, while considering the multiple dimensions of population heterogeneity and the limited testing capacity.