The building evacuation problem with shared information

In this article, the Building Evacuation Problem with Shared Information (BEPSI) is formulated as a mixed integer linear program, where the objective is to determine the set of routes along which to send evacuees (supply) from multiple locations throughout a building (sources) to the exits (sinks) such that the total time until all evacuees reach the exits is minimized. The formulation explicitly incorporates the constraints of shared information in providing online instructions to evacuees, ensuring that evacuees departing from an intermediate or source location at a mutual point in time receive common instructions. Arc travel time and capacity, as well as supply at the nodes, are permitted to vary with time and capacity is assumed to be recaptured over time. The BEPSI is shown to be NP‐hard. An exact technique based on Benders decomposition is proposed for its solution. Computational results from numerical experiments on a real‐world network representing a four‐story building are given. Results of experiments employing Benders cuts generated in solving a given problem instance as initial cuts in addressing an updated problem instance are also provided. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

基于AdaBoost-SVM的P2P流量识别方法

针对传统的P2P流量识别技术存在识别率低和误判率高的缺点,将机器学习中Ada Boost算法的良好分类能力和SVM的泛化能力结合起来,提出一种基于Ada Boost-SVM组合算法的P2P网络流量识别模型,将SVM作为Ada Boost的基分类器,运用最小近邻法计算支持向量与训练集的样本间的距离实现分类进行P2P流量识别。最后,以4种P2P流量数据为研究对象在MATLAB上进行仿真,仿真结果表明,提出的Ada Boost-SVM的组合算法在P2P网络流量的分类性能和分类准确率上都优于单纯的Ada Boost和SVM,组合算法的P2P流量平均识别率高达98.7%,远高于Ada Boost和SVM的识别率。     

对空向量瞄具设计

给出了对空向量瞄具的设计计算方法,并对一个实例进行了详细的分析。     

雷达网抗干扰性能综合评估指标

如何对雷达网的抗干扰性能进行评估一直是国内外的研究热点之一。从雷达网的战术功能总体需求出发,指出可以根据雷达网在干扰前后的情报信息提供能力的变化来进行评估,然后结合对雷达网内部影响其抗干扰性能的各因素分析,并考虑到外场实际开展对雷达网抗干扰性能试验评估的需求,在现有评估指标的基础上,提出用于综合评估雷达网抗干扰性能的指标。     

军用雷达成本波动的源头探索与关键控制

根据军用雷达成本涉及的各个方面,建立雷达成本波动评价指标体系,运用主成分分析法确定其重要影响因子及权重,构造具有望小特性的评价函数可用于竞标决策选优,为军用雷达成本控制提供即时管理。     

Generating Pareto‐optimal boundary points in multiparty negotiations using constraint proposal method

In this paper a constraint proposal method is developed for computing Pareto‐optimal solutions in multiparty negotiations over continuous issues. Constraint proposal methods have been previously studied in a case where the decision set is unconstrained. Here we extend the method to situations with a constrained decision set. In the method the computation of the Pareto‐optimal solutions is decentralized so that the DMs do not have to know each others' value functions. During the procedure they have to indicate their optimal solutions on different sets of linear constraints. When the optimal solutions coincide, the common optimum is a candidate for a Pareto‐optimal point. The constraint proposal method can be used to generate either one Pareto‐optimal solution dominating the status quo solution or several Pareto‐optimal solutions. In latter case a distributive negotiation among the efficient points can be carried out afterwards. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 210–225, 2001     

后方油库整体生存概率分析计算

针对我军后方油库特点,探讨了油库整体生存概率计算的基本思路和方法,分析了各类分项目标生存概率的计算方法,采用层次分析对后方油库各分项目标权值进行了详细分析计算,可为后方油库伪装防护效能评估提供依据。     

Effectiveness of (R,Q) policies in one‐warehouse multiretailer systems with deterministic demand and backlogging

Consider a distribution system with a central warehouse and multiple retailers. Customer demand arrives at each of the retailers continuously at a constant rate. The retailers replenish their inventories from the warehouse which in turn orders from an outside supplier with unlimited stock. There are economies of scale in replenishing the inventories at both the warehouse and the retail level. Stockouts at the retailers are backlogged. The system incurs holding and backorder costs. The objective is to minimize the long‐run average total cost in the system. This paper studies the cost effectiveness of (R, Q) policies in the above system. Under an (R, Q) policy, each facility orders a fixed quantity Q from its supplier every time its inventory position reaches a reorder point R. It is shown that (R, Q) policies are at least 76% effective. Numerical examples are provided to further illustrate the cost effectiveness of (R, Q) policies. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 422–439, 2000     

注意力机制量化剪枝优化方法

面向基于注意力机制模型的巨大计算和访存开销问题,研究量化和剪枝协同优化的模型压缩技术,提出针对注意力机制中查询、键、值、概率共四个激活值矩阵的对称线性定点量化方法。同时,提出概率矩阵剪枝方法和渐进式剪枝策略,有效降低剪枝精度损失。在不同数据集上的实验结果表明,针对典型基于注意力机制模型BERT,在较低或者没有精度损失的情况下该优化方法可达到4位或8位定点量化、0.93～0.98的稀疏度,大幅度降低模型计算量,为加速量化稀疏模型的推理奠定良好的基础。     

Optimal policies for M/M/m queue with two different kinds of (N,T)‐policies

In this paper, two different kinds of (N, T)‐policies for an M/M/m queueing system are studied. The system operates only intermittently and is shut down when no customers are present any more. A fixed setup cost of K > 0 is incurred each time the system is reopened. Also, a holding cost of h > 0 per unit time is incurred for each customer present. The two (N, T)‐policies studied for this queueing system with cost structures are as follows: (1) The system is reactivated as soon as N customers are present or the waiting time of the leading customer reaches a predefined time T, and (2) the system is reactivated as soon as N customers are present or the time units after the end of the last busy period reaches a predefined time T. The equations satisfied by the optimal policy (N*, T*) for minimizing the long‐run average cost per unit time in both cases are obtained. Particularly, we obtain the explicit optimal joint policy (N*, T*) and optimal objective value for the case of a single server, the explicit optimal policy N* and optimal objective value for the case of multiple servers when only predefined customers number N is measured, and the explicit optimal policy T* and optimal objective value for the case of multiple servers when only predefined time units T is measured, respectively. These results partly extend (1) the classic N or T policy to a more practical (N, T)‐policy and (2) the conclusions obtained for single server system to a system consisting of m (m ≥ 1) servers. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 240–258, 2000