作战协同关系超图模型

在分析超图和作战协同相关概念基础上,采用形式化方式对抽象化的作战协同关系进行了描述,定义了协同关系矩阵,创新性地根据超图相关概念构建了作战协同超图模型,并根据模型定义了顶点度和关联系数等支持作战协同关系分析的特征参数。通过实验建立了某想定数据中的火力协同关系超图模型,并进行了度和关联系数的分析,实验结果证明了作战协同关系超图模型的可用性。     

Balancing stability and efficiency in team formation as a generalized roommate problem

The assignment of personnel to teams is a fundamental managerial function typically involving several objectives and a variety of idiosyncratic practical constraints. Despite the prevalence of this task in practice, the process is seldom approached as an optimization problem over the reported preferences of all agents. This is due in part to the underlying computational complexity that occurs when intra-team interpersonal interactions are taken into consideration, and also due to game-theoretic considerations, when those taking part in the process are self-interested agents. Variants of this fundamental decision problem arise in a number of settings, including, for example, human resources and project management, military platooning, ride sharing, data clustering, and in assigning students to group projects. In this article, we study an analytical approach to “team formation” focused on the interplay between two of the most common objectives considered in the related literature: economic efficiency (i.e., the maximization of social welfare) and game-theoretic stability (e.g., finding a core solution when one exists). With a weighted objective across these two goals, the problem is modeled as a bi-level binary optimization problem, and transformed into a single-level, exponentially sized binary integer program. We then devise a branch-cut-and-price algorithm and demonstrate its efficacy through an extensive set of simulations, with favorable comparisons to other algorithms from the literature.     

A generalization of the weighted set covering problem

We study a generalization of the weighted set covering problem where every element needs to be covered multiple times. When no set contains more than two elements, we can solve the problem in polynomial time by solving a corresponding weighted perfect b‐matching problem. In general, we may use a polynomial‐time greedy heuristic similar to the one for the classical weighted set covering problem studied by D.S. Johnson [Approximation algorithms for combinatorial problems, J Comput Syst Sci 9 (1974), 256–278], L. Lovasz [On the ratio of optimal integral and fractional covers, Discrete Math 13 (1975), 383–390], and V. Chvatal [A greedy heuristic for the set‐covering problem, Math Oper Res 4(3) (1979), 233–235] to get an approximate solution for the problem. We find a worst‐case bound for the heuristic similar to that for the classical problem. In addition, we introduce a general type of probability distribution for the population of the problem instances and prove that the greedy heuristic is asymptotically optimal for instances drawn from such a distribution. We also conduct computational studies to compare solutions resulting from running the heuristic and from running the commercial integer programming solver CPLEX on problem instances drawn from a more specific type of distribution. The results clearly exemplify benefits of using the greedy heuristic when problem instances are large. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

二部图的(g,f)匹配

本文证明了二部图存在(g,f)匹配和f 因子的充要条件以及有关的几个结果,并且给出了求二部图的最大(g,f)匹配、最小(g,f)匹配和最小权最大f 匹配、最小权(g,f)匹配、最大权(g,f)匹配的算法。     

超短波信道建模方法

针对超短波信道传播条件复杂,缺乏相关成熟方法和模型的现状,对当前国内外现有超短波信道模型和建模方法进行了梳理、总结和归纳。简单介绍了超短波传播特性、信道参数以及现有模型,重点梳理了在不同应用场景下,可用于超短波信道建模的方法。然后,针对基于随机传播图理论的方法进行了重点介绍,总结了基于信道参数的模型验证方法。最后,针对不同的超短波应用条件,对相关的方法进行了展望。     

Disruption management in production planning

We study the problem of recovering a production plan after a disruption, where the disruption may be caused by incidents such as power failure, market change, machine breakdown, supply shortage, worker no‐show, and others. The new recovery plan we seek after has to not only suit the changed environment brought about by the disruption, but also be close to the initial plan so as not to cause too much customer unsatisfaction or inconvenience for current‐stage and downstream operations. For the general‐cost case, we propose a dynamic programming method for the problem. For the convex‐cost case, a general problem which involves both cost and demand disruptions can be solved by considering the cost disruption first and then the demand disruption. We find that a pure demand disruption is easy to handle; and for a pure cost disruption, we propose a greedy method which is provably efficient. Our computational studies also reveal insights that will be helpful to managing disruptions in production planning. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.