Maximizing the ratio of two convex functions over a convex set

The purpose of this article is to present an algorithm for globally maximizing the ratio of two convex functions f and g over a convex set X. To our knowledge, this is the first algorithm to be proposed for globally solving this problem. The algorithm uses a branch and bound search to guarantee that a global optimal solution is found. While it does not require the functions f and g to be differentiable, it does require that subgradients of g can be calculated efficiently. The main computational effort of the algorithm involves solving a sequence of subproblems that can be solved by convex programming methods. When X is polyhedral, these subproblems can be solved by linear programming procedures. Because of these properties, the algorithm offers a potentially attractive means for globally maximizing ratios of convex functions over convex sets. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

A branch‐and‐price algorithm for parallel machine scheduling with time windows and job priorities

This paper presents a branch‐and‐price algorithm for scheduling n jobs on m nonhomogeneous parallel machines with multiple time windows. An additional feature of the problem is that each job falls into one of ρ priority classes and may require two operations. The objective is to maximize the weighted number of jobs scheduled, where a job in a higher priority class has “infinitely” more weight or value than a job in a lower priority class. The methodology makes use of a greedy randomized adaptive search procedure (GRASP) to find feasible solutions during implicit enumeration and a two‐cycle elimination heuristic when solving the pricing subproblems. Extensive computational results are presented based on data from an application involving the use of communications relay satellites. Many 100‐job instances that were believed to be beyond the capability of exact methods, were solved within minutes. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

海上编队雷达网近程协同抗ARM方法与仿真

根据反辐射导弹（ARM）攻击时捕捉目标的局限性,提出了海上编队雷达网利用编队内各平台之间的间距优势来协同抗ARM的方法,建立了海上编队雷达网在近程距离内协同抗ARM的能力评估模型和对抗行为模型。其中,能力评估模型可以对海上编队雷达网抗ARM的能力进行评估。舰艇编队在面临ARM威胁时,通过对上述两个模型结合使用,可以对ARM实施有效诱偏。仿真试验结果表明,所建立模型具有一定的可行性和正确性。     

复杂系统理论对作战实践的指导

比较研究了机械模式与复杂系统模式的两种作战系统观,分析了复杂系统的整体性、非线性、开放性、自适应性、不确定性等特点。然后,根据作战系统是一复杂系统这一基本观点,并以复杂系统理论为指导,探讨了作战中的指挥控制、进攻作战、防御作战的一些基本策略。     

基于MATLAB／SIMULINK的某型高炮伺服系统的仿真研究

伺服系统是高炮系统的重要组成部分.基于MATLAB环境中的SIMULINK仿真软件,对某型高炮伺服系统进行了动态仿真和分析,仿真结果可用来指导实际系统的参数调整.     

LabWindows／CVI环境下的仪器驱动器开发

研究了VPP下的仪器驱动器、软面板和安装程序等的功能和特性,并结合寄存器基VXI多路开关模块的研制,说明了在LabWindows/CVI环境下开发仪器驱动器的一般方法与研制过程.     

Iterative coloring extension of a maximum clique

In this paper we present an improved branch and bound algorithm for the vertex coloring problem. The idea is to try to extend the coloring of a maximum clique to its adjacent vertices. If this succeeds, its successive neighbors are considered; in case of failure (i.e., in the case the initial colors are not sufficient), working on the subgraph induced by the maximum clique and its neighborhood, the lower bound is improved by seeking for an optimal coloring of this subgraph by branch and bound. The process is repeated iteratively until the whole graph is examined. The iterative scheme exploits a further lower bound obtained by integrating a simple algorithm into the maximum clique search, and a new method to compute upper bounds on subgraphs. Furthermore, a new branching rule and a method for the selection of the initial maximum clique are presented. Extensive computational results and comparisons with existing exact coloring algorithms on random graphs and benchmarks are given. © 2001 John Wiley & Sons, Inc. Naval Research Logistic 48: 518–550, 2001     

水体富营养化危害、成因及防治

水体富营养化严重影响人类健康、可用水量、水体经济价值和水体寿命,而大量污水超标排放、农业面源污染、水产养殖业失当、周边环境恶化和底泥氮磷溶出是其形成主因。在治理和恢复富营养化水体的实践中,必须强化污染预防意识,从源头上削减污染负荷,同时选用切实可行、系统协调、针对性强的政策和措施并长期坚持实施,才能起到事半功倍的效果。     

Path optimization for the resource‐constrained searcher

We formulate and solve a discrete‐time path‐optimization problem where a single searcher, operating in a discretized three‐dimensional airspace, looks for a moving target in a finite set of cells. The searcher is constrained by maximum limits on the consumption of one or more resources such as time, fuel, and risk along any path. We develop a specialized branch‐and‐bound algorithm for this problem that uses several network reduction procedures as well as a new bounding technique based on Lagrangian relaxation and network expansion. The resulting algorithm outperforms a state‐of‐the‐art algorithm for solving time‐constrained problems and also is the first algorithm to solve multi‐constrained problems. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Rank‐Cluster‐and‐Prune: An algorithm for generating clusters in complex set partitioning problems

Clustering problems are often difficult to solve due to nonlinear cost functions and complicating constraints. Set partitioning formulations can help overcome these challenges, but at the cost of a very large number of variables. Therefore, techniques such as delayed column generation must be used to solve these large integer programs. The underlying pricing problem can suffer from the same challenges (non‐linear cost, complicating constraints) as the original problem, however, making a mathematical programming approach intractable. Motivated by a real‐world problem in printed circuit board (PCB) manufacturing, we develop a search‐based algorithm (Rank‐Cluster‐and‐Prune) as an alternative, present computational results for the PCB problem to demonstrate the tractability of our approach, and identify a broader class of clustering problems for which this approach can be used. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009