一种求解最小双费用流问题的算法

多目标优化是网络最优化的一个重要子问题。通过实际应用案例,抽象出一种带容量限制的双费用权网络模型,并由此提出了相应的最小双费用流问题。之后,借鉴网络分层的思想,根据双费用权网络的特点设计出一个求解该问题的双层原始对偶算法,并严谨地证明了算法的正确性,估计出算法的复杂度为O(n2v0)。此外,对算法进行了推广改进,使其能求解一般k费用权网络中的最小k费用流问题。最后,通过一个实例来演示算法的执行。     

基于大流调度的软件定义数据中心网络负载均衡算法

传统负载均衡算法对数据中心网络中的大流进行调度时,会造成部分链路负载过重、网络整体负载不均衡等问题。将负载均衡问题转化为多商品流问题进行求解,结合软件定义网络集中控制的思想和数据中心网络的流量特征,提出一种基于大流调度的软件定义数据中心网络负载均衡算法。根据阈值将数据流划分为大流和小流,结合路径上大流分布度和可用负载度对大流进行重路由,以减小大流对网络负载均衡的影响。仿真实验表明,在流量大小分布不均衡的数据中心网络中,该算法与传统的等价多路径算法和基于全局最先匹配的动态流量调度算法相比,在平均对分带宽上获得了更大的提升,能够更好地实现数据中心网络的负载均衡。     

Modeling and optimizing traffic light settings in road networks

We discuss continuous traffic flow network models including traffic lights. A mathematical model for traffic light settings within a macroscopic continuous traffic flow network is presented, and theoretical properties are investigated. The switching of the traffic light states is modeled as a discrete decision and is subject to optimization. A numerical approach for the optimization of switching points as a function of time based upon the macroscopic traffic flow model is proposed. The numerical discussion relies on an equivalent reformulation of the original problem as well as a mixed-integer discretization of the flow dynamics. The large-scale optimization problem is solved using derived heuristics within the optimization process. Numerical experiments are presented for a single intersection as well as for a road network.     

Distributed intelligent architecture for logistics (DIAL)

An ideal logistics problem is considered as a network flow problem which generates a logistics plan and subsequently executes the plan. A real-world logistics plan is different from its ideal counterpart modeled as a network flow problem in the sense that each node of the logistics graph is operated independently with disparate objectives. In contrast to the nodes of a network flow problem, agents are considered as software entities which embody elegant reasoning ability to justify their own actions towards individual objectives, and also interact with other agents. Hence, a group of agents or a multiagent system is best suited to solve real-world logistics problems with each agent representing a node of the graph. We have built a three-tier framework where a customer's problem can be decomposed and assigned to all the agents which together generate a logistics plan. We employ two simulation software as planning tools which enable us to simulate appropriate events. The key ideas behind this paper are large-scale multiagent architectural modeling issues (scalability), computation task control, information sharing among several customers, and a problem solving procedure before the planning process. The problem solving procedure is considered as determining the computational tasks required to be invoked to initiate the planning process. We describe the implementation of the framework.     

随机流量网络中流量分配控制的多目标优化研究

现实世界的网络比如：物流网络、通信网络、交通网络,电网等可以被抽象成一个随机流量网络。以传输成功率和整个传输所花费的成本为目标,对随机流量网络上流量的分配控制的多目标优化问题进行了研究。采用MPs的概念对问题建模,大大简化了模型的复杂程度。最后提出一个多目标遗传算法,通过实例验证,该算法较好地解决了随机流量网络上的流量分配控制问题。     

多用户动态交通分配和信号优化的组合研究

提出了一种饱和路网中考虑多用户行为下的动态交通分配和交通信号优化的组合模型。模型采用广义双层规划来表示。模型的上层是信号控制优化,下层是考虑多用户路径选择条件下的动态交通分配,进行交通网络流的配置。同时,模型中采用具有物理排队的动态网络模型,从而考虑了饱和路网中物理排队对基于路段的网络条件的影响。通过仿真说明了饱和路网中考虑多用户行为下的动态交通分配和交通信号优化的组合模型可以实现交通信号的优化配置和交通网络流的优化,并反映了排队的物理效应。     

Minimization of Energy Losses in Electric Power Distribution Systems by Intelligent Search Strategies

Energy is continuously dissipated in electric power systems due to electrical resistance in transmission and distribution lines. This paper addresses the problem of obtaining a network topology with minimum energy losses for electric power distribution systems. As distribution networks must operate radially, the problem can be formulated as a generalization of the minimum spanning tree problem. The generalization is due to variation in costs as network configuration changes. Nonlinear network flow techniques are teamed with search strategies borrowed from the field of artificial intelligence to overcome computation intractability.     

流身份鉴别技术研究进展

由于流交换中流源身份的不可信、流交换范围的不可控和流路径的不可追踪问题与日俱增, 流身份鉴别技术逐渐成为近年来网络安全研究的热点。为了更深入地解决流身份鉴别技术存在的不足, 采用文献分析方法论证了必要性, 简述了其应用领域, 介绍了相关概念和基本框架; 概括了流身份鉴别技术面临的关键问题——流变换, 并且多维度地将现有方案进行分类, 从多个角度进行对比分析。最后归纳了该领域当前的热点和发展前景, 以期为下一步研究提供参考。     

网络最大流问题求解的符号ADD增广路径算法

本文通过对网络及网络最大流问题的符号代数判定图（ADD）描述,将网络中的结点和边用ADD隐式表示,并利用Gabow的容量变尺度算法的主要思想,将一般网络最大流问题化为一系列的单位容量网络最大流问题,结合Hachtel等的单位容量网络最大流问题的求解算法,给出了网络最大流问题求解的符号ADD增广路径算法,简称为符号ADD算法.与Dinic算法、Karzanov算法相比,本文算法的空间复杂度得到了改善.实验结果表明,本文算法是切实有效的,且可处理更大规模的问题.     

Approximation Algorithms for a Capacitated Network Design Problem

We study a capacitated network design problem with applications in local access network design. Given a network, the problem is to route flow from several sources to a sink and to install capacity on the edges to support the flow at minimum cost. Capacity can be purchased only in multiples of a fixed quantity. All the flow from a source must be routed in a single path to the sink. This NP-hard problem generalizes the Steiner tree problem and also more effectively models the applications traditionally formulated as capacitated tree problems. We present an approximation algorithm with performance ratio (ρ_ST + 2) where ρ_ST is the performance ratio of any approximation algorithm for the minimum Steiner tree problem. When all sources have unit demand, the ratio improves to ρ_ST + 1) and, in particular, to 2 when all nodes in the graph are sources.     

Flows in dynamic networks with aggregate arc capacities

Dynamic networks are characterized by transit times on edges. Dynamic flow problems consider transshipment problems in dynamic networks. We introduce a new version of dynamic flow problems, called bridge problem. The bridge problem has practical importance and raises interesting theoretical issues. We show that the bridge problem is NP-complete. Traditional static flow techniques for solving dynamic flow problems do not extend to the new problem. We give a linear programming formulation for the bridge problem which is based on the time-expanded network of the original dynamic network.     

基于原始对偶方法求解网络流量监测集算法

考虑网络节点的流守恒特性,网络流量的有效监测问题可抽象为求给定图G(V,E)的最小弱顶点覆盖集的问题和基于流划分的最小弱顶点覆盖集的问题,这是NP难的问题.首先分析了弱顶点覆盖集的约束关系,并给出了问题的整数规划形式.然后利用原始对偶方法构造了求解最小弱顶点覆盖集的近似算法,并分析了算法的比界为2.进一步分析了求解基于最大流划分的最小弱顶点覆盖集的近似算法.     

An FPTAS for Quickest Multicommodity Flows with Inflow-Dependent Transit Times

Given a network with capacities and transit times on the arcs, the quickest flow problem asks for a "flow over time" that satisfies given demands within minimal time. In the setting of flows over time, flow on arcs may vary over time and the transit time of an arc is the time it takes for flow to travel through this arc. In most real-world applications (such as, e.g., road traffic, communication networks, production systems, etc.), transit times are not fixed but depend on the current flow situation in the network. We consider the model where the transit time of an arc is given as a non-decreasing function of the rate of inflow into the arc. We prove that the quickest s-t-flow problem is NP-hard in this setting and give various approximation results, including a fully polynomial time approximation scheme (FPTAS) for the quickest multicommodity flow problem with bounded cost.     

Joint optimization for area traffic control and network flow

A joint optimization problem for solving area traffic control and network flow is investigated. A bilevel programming is used to formulate this joint optimization problem where the network flow following Wardrop's principles can be obtained by solving traffic assignment problems. In this paper, we present a solution approach for jointly optimizing the area traffic control and network flow on the basis of a newly presented algorithm for concurrent flow (Comput. Oper. Res. (2004) in press). We propose three kinds of formulations for this joint optimization problem and present a gradient-based method to effectively solve this problem via a mixture of locally optimal search and global search heuristic where a near global optimum may be found. Numerical comparisons are made for the values of performance index achieved by the joint optimization problem with system optimal flow and those did by equilibrium flow at various sets of initial signal settings. Substantially good results have demonstrated the robustness of the proposed algorithm in solving both system optimal and user equilibrium flow for the joint optimization problem at large-scale networks.     

Heuristic transporter routing model for manufacturing facility design

In this study, a transporter routing problem is analyzed and adapted for use in manufacturing facility design. Given fixed facility layout and predetermined material flow paths, this study determines the minimum number of transporters required to transfer material within a given manufacturing facility with minimal handling effort. The manufacturing facility design problem is particularly complex and involves the sub-problems such as design of the material network and the transporter routing problem, which provides the fleet size and the routing of each transporter over the flow network. The problem is formulated as an integer program. To solve the problem, we used a heuristic and integrated vehicle routing model. We also developed a heuristic solution program and several tests along with an industrial example to indicate the effectiveness of this method.     

带有时间和费用双重限制的网络容量扩充问题

该文将网络容量定义为最大s-t流的流量,建立了带有时间和费用双重限制下的网络容量扩充问题的一般模型。通过网络变换,将带有时间限制的容量扩充问题转化为线性最小费用流问题,并给出了具体证明和求解容量扩充问题的算法。该模型和算法不仅适用于各种情形的容量扩充问题,而且还可应用于网络流规划。最后通过具体例子的求解,说明了模型和算法的正确性和有效性。     

小容量网络上的最大流算法

最大流问题是一类经典的组合优化问题。描述了一种小容量网络,这种网络有强的实际应用背景,同时给出了专门解这种网络上最大流问题的算法。该算法比通用的算法快。它已经突破了最大流问题的O（mn)时间障碍,具有较强的理论意义,也为解决许多实际应用问题提供了更有效的算法。同时,由于判断一个网络是否为小容量网络非常简单,因此该算法也具有普遍意义。     

基于最大流的车辆容迟网络路由算法

为提高车辆容迟网络的吞吐率,将一对节点之间的数据传输过程视为最大流问题,提出基于最大流的车辆容迟网络路由算法。容迟网络中的最大流问题被转化为静态网络中的问题,从而可用最大流方案进行求解。实验结果证明,该算法的预测准确率高于传统算法,附带的额外开销较小。     

An algorithm for the biobjective integer minimum cost flow problem

In this paper, we study the single commodity flow problems, optimizing two objectives simultaneously, where the flow values must be integer values. We propose a method that finds all the efficient integer points in the objective space. Our algorithm performs two phases. In the first phase, all integer points on the efficient boundary are found and in the second phase, the efficient integer points that do not lie on the efficient boundary are calculated. In addition, we carry out a computational experiment showing that the number of efficient integer solutions that do not lie on the efficient boundary is greater than the number of integer solutions on the efficient boundary.Scope and purposeIn many combinatorial optimization problems, the selection of the optimum solution takes into account more than one criterion. For example, in transportation problems or in network flows problems, the criteria that can be considered are the minimization of the cost for selected routes, the minimization of arrival times at the destinations, the minimization of the deterioration of goods, the minimization of the load capacity that would not be used in the selected vehicles, the maximization of safety, reliability, etc. Often, these criteria are in conflict and for this reason, a multiobjective network flow formulation of the problem is necessary. The solution to this problem is searched for among the set of efficient points. Although multiobjective network flow problems can be solved using the techniques available for the multiobjective linear programming problem, network-based methods are computationally better. The multicriteria minimum cost flow problem has already merited the attention of several authors and the case which has been considered in literature is that which has two objectives, where the continuous flow values are permissible. However, the integer case of the biobjective minimum cost flow problem has scarcely been studied. Whereas, in many real network flow problems, integer values on flow values are required. In this paper, we propose an approach to solve the biobjective integer minimum cost flow problem. An algorithm to obtain all efficient integer solutions of this problem is introduced. This method is characterized by the use of the classic resolution tools of network flow problems, such as the network simplex method. It does not utilize the biobjective integer linear programming methodology. Furthermore, the method does not calculate dominated solutions, so it is not necessary to incorporate tools to eliminate dominated solutions.     

因特网拥塞控制的公平性研究综述

将网络拥塞控制的公平性研究划分为在同质流网络中的公平性和在异质流网络中的公平性两个方面,公平性研究在两类网络中均有重大的意义.依此划分,分别介绍了近年来拥塞控制公平性研究的重要进展.同质流网络中公平性研究主要是围绕解决TCP流的RTT歧视这一问题而展开和深入的;异质流网络中公平性研究主要是围绕保护正当行为流的问题而不断推进的,目前的研究热点是对用户公平的AQM算法.最后对拥塞控制公平性研究领域未来有价值的研究问题给出了预测,并阐述了对这几个问题的理解.