战术MANET中基于多态转移策略的蚁群优化QoS路由算法

战术MANET的QoS路由计算是一个NP完全问题,可以采用蚁群优化算法来求解.为了提高蚁群优化QoS路由算法的效率,降低时延和网络开销,提出了基于多态转移策略的蚁群优化QoS路由算法(MTS-AQRA).MTS-AQRA将链路稳定性和路由拥塞度与常规的QoS路由约束条件结合起来,利用多态转移策略产生的多样化路由搜索蚁群和并行路由搜索处理,能够在MANET网络中快速地建立满足业务QoS要求的稳定路由.仿真实验结果表明,MTS-AQRA在分组到达率、端到端时延、网络吞吐量等指标上综合性能优于AODV、AntHocNet、QoS-Aware ACO等路由算法.     

Estimating distribution functions from partially observed samples

Suppose that some components are initially operated in a certain condition and then switched to operating in a different condition. Working hours of the components in condition 1 and condition 2 are respectively observed. Of interest is the lifetime distribution F of the component in the second condition only, i.e., the distribution without the prior exposure to the first condition. In this paper, we propose a method to transform the lifetime obtained in condition 1 to an equivalent lifetime in condition 2 and then use the transformed data to estimate F. Both parametric and nonparametric approaches each with complete and censored data are discussed. Numerical studies are presented to investigate the performance of the method. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 521–530, 2000     

Balancing and optimizing a portfolio of R&D projects

A mathematical formulation of an optimization model designed to select projects for inclusion in an R&D portfolio, subject to a wide variety of constraints (e.g., capital, headcount, strategic intent, etc.), is presented. The model is similar to others that have previously appeared in the literature and is in the form of a mixed integer programming (MIP) problem known as the multidimensional knapsack problem. Exact solution of such problems is generally difficult, but can be accomplished in reasonable time using specialized algorithms. The main contribution of this paper is an examination of two important issues related to formulation of project selection models such as the one presented here. If partial funding and implementation of projects is allowed, the resulting formulation is a linear programming (LP) problem which can be solved quite easily. Several plausible assumptions about how partial funding impacts project value are presented. In general, our examples suggest that the problem might best be formulated as a nonlinear programming (NLP) problem, but that there is a need for further research to determine an appropriate expression for the value of a partially funded project. In light of that gap in the current body of knowledge and for practical reasons, the LP relaxation of this model is preferred. The LP relaxation can be implemented in a spreadsheet (even for relatively large problems) and gives reasonable results when applied to a test problem based on GM's R&D project selection process. There has been much discussion in the literature on the topic of assigning a quantitative measure of value to each project. Although many alternatives are suggested, no one way is universally accepted as the preferred way. There does seem to be general agreement that all of the proposed methods are subject to considerable uncertainty. A systematic way to examine the sensitivity of project selection decisions to variations in the measure of value is developed. It is shown that the solution for the illustrative problem is reasonably robust to rather large variations in the measure of value. We cannot, however, conclude that this would be the case in general. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 18–40, 2001     

The selection allocation problem

The Selection Allocation Problem (SAP) is a single period decision problem which involves selecting profit‐maximizing (or cost‐minimizing) activities from various distinct groups, and determining the volume of those activities. The activities in each group are selected subject to the availability of that group's resource, which is provided by either pooling or blending raw inputs from several potential sources. Imbedded in the decision process is the additional task of determining how much raw input is to be allocated to each group to form the resource for that group. Instances of this problem can be found in many different areas, such as in tool selection for flexible manufacturing systems, facility location, and funding for social services. Our goal in this paper is to identify and exploit special structures in the (SAP) and use those structures to develop an efficient solution procedure. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 707–725, 1999     

自适应交互多模型算法在机动目标跟踪中的应用

针对多模型算法在机动目标跟踪中存在的问题,运用交互多模型算法(IMM)和自适应滤波理论,设计了一种自适应交互多模型算法(AIMM),结合目标运动模型对目标当前加速度和其方差进行估计,并在此基础上给出了AIMM中模型集和模型转移概率的设计方法,进行了计算机仿真.蒙特卡罗仿真结果表明,与标准IMM算法相比,该算法比IMM算法的跟踪性能有很大提高,跟踪复杂机动目标比IMM有更快的收敛速度,跟踪滞后问题得到较好的解决,跟踪目标的稳定性和精确性均优于IMM算法,有利于机动目标的实时跟踪.     

基于多属性决策方法的防空C3I系统效能评估

介绍了防空C3I系统效能的定义及效能评估的一般处理过程.针对效能指标的多样性,提出了基于多属性决策(MADM)的效能评估方法,并通过一个示例给出MADM方法的处理过程,有效地解决了防空C3I系统效能评估这一复杂而困难的问题,仿真结果证明了方法的可行性.     

Spatial pricing and product allocation in online retailing

Spatial pricing means a retailer price discriminates its customers based on their geographic locations. In this article, we study how an online retailer should jointly allocate multiple products and facilitate spatial price discrimination to maximize profits. When deciding between a centralized product allocation ((i.e., different products are allocated to the same fulfillment center) and decentralized product allocation (ie, different products are allocated to different fulfillment centers), the retailer faces the tradeoff between shipment pooling (ie, shipping multiple products in one package), and demand localization (ie, stocking products to satisfy local demand) based on its understanding of customers' product valuations. In our basic model, we consider two widely used spatial pricing policies: free on board (FOB) pricing that charges each customer the exact amount of shipping cost, and uniform delivered (UD) pricing that provides free shipping. We propose a stylized model and find that centralized product allocation is preferred when demand localization effect is relatively low or shipment pooling benefit is relatively high under both spatial pricing policies. Moreover, centralized product allocation is more preferred under the FOB pricing which encourages the purchase of virtual bundles of multiple products. Furthermore, we respectively extend the UD and FOB pricing policies to flat rate shipping (ie, the firm charges a constant shipping fee for each purchase), and linear rate shipping (ie, the firm sets the shipping fee as a fixed proportion of firm's actual fulfillment costs). While similar observations from the basic model still hold, we find the firm can improve its profit by sharing the fulfillment cost with its customers via the flat rate or linear rate shipping fee structure.     

考虑禁飞区的高超声速飞行器再入跟踪控制

为确立高超声速飞行器再入精确模型,参考NASA的研究数据对动力学方程中的气动参数进行拟合。基于飞行器结构的相关性质及战略应用背景,加入热流密度、动压、过载和禁飞区等参考约束的限制,融合成强非线性、复杂耦合的最优控制问题。采用改进自适应伪谱法求解模型所构建的微分方程组,通过设置自适应网格细化与配点,使单位时间内状态变量和控制变量波动过大的时间区间被进一步细划,并利用SNOPT求解器解算出符合条件的最优轨迹。设计闭环控制器,实现对最优轨迹的姿态变化的跟踪,测试系统的性能并进行评估。仿真结果表明:整个再入过程中,飞行器速度下降过程偏于平稳,再入轨迹可以满足约束条件,在避开禁飞区的同时取得最大横向航程;三通道角速度收敛可控,对姿态的跟踪较理想,控制器基本可以实现精确调姿。     

Pricing and capacity allocation strategies: Implications for manufacturers with product sharing

Emerging sharing modes, like the consumer-to-consumer (C2C) sharing of Uber and the business-to-consumer (B2C) sharing of GoFun, have considerably affected the retailing markets of traditional manufacturers, who are motivated to consider product sharing when making pricing and capacity decisions, particularly electric car manufacturers with limited capacity. In this paper, we examine the equilibrium pricing for a capacity-constrained manufacturer under various sharing modes and further analyze the impact of capacity constraint on the manufacturer's sharing mode selection as well as equilibrium outcomes. We find that manufacturers with low-cost products prefer B2C sharing while those with high-cost products prefer C2C sharing except when the sharing price is moderate. However, limited capacity motivates manufacturers to enter into the B2C sharing under a relatively low sharing price, and raise the total usage level by sharing high-cost products. We also show that the equilibrium capacity allocated to the sharing market with low-cost products first increases and then decreases. Finally, we find that sharing low-cost products with a high limited capacity leads to a lower retail price under B2C sharing, which creates a win-win situation for both the manufacturer and consumers. However, sharing high-cost products with a low limited capacity creates a win-lose situation for them.     

GSVM:一种支持Gather/Scatter的向量存储器

宽单指令多数据流(Single Instruction Multiple Data, SIMD)架构数字信号处理器一般都能高效支持地址连续或等距跨步等规则应用的向量访存,但对于科学与工程计算中广泛存在的不规则应用的数据访存则带宽利用率往往较低,从而大幅降低了其整体运算能效。为了提高不规则应用的向量访存性能,基于某SIMD数字信号处理器的体系结构,设计了一种支持Gather/Scatter访存的向量存储器GSVM。通过设计与SIMD宽度相匹配的向量地址计算单元和合适深度的冲突缓冲器阵列,实现了Gather/Scatter指令向量地址计算、仲裁与缓存的全流水访存操作。实验结果表明,相比以前不支持Gather/Scatter访存的存储器,GSVM在增加22%的硬件代价基础上,基于稀疏矩阵向量乘的测试程序集获得了2～8的性能加速比。