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Network
Kinds of Network Terms modified by Network Selected AbstractsGOVERNING BY NETWORK: THE NEW SHAPE OF THE PUBLIC SECTOR by Stephen Goldsmith and William D. EggersECONOMIC AFFAIRS, Issue 2 2006Keith Boyfield No abstract is available for this article. [source] STUDY ON THE MOTION ERROR OF MAIN SPINDLE OF LATHE BASED ON THE HARMONIC WAVELET NEURAL NETWORKEXPERIMENTAL TECHNIQUES, Issue 3 2009X.-J. Fu First page of article [source] A NEW APPROACH TO MODELING AND CONTROL OF A FOOD EXTRUSION PROCESS USING ARTIFICIAL NEURAL NETWORK AND AN EXPERT SYSTEMJOURNAL OF FOOD PROCESS ENGINEERING, Issue 1 2001OTILIA POPESCU ABSTRACT The paper presents a new approach to the modeling of the start-up part of a food extrusion process. A neural network model is proposed and its parameters are determined. Simulation results with real data are also presented. The inputs and outputs of the model are among those used by the human operator during the start-up process for control. An intelligent controller structure that uses an expert system and "delta-variations" to modify inputs is also proposed. [source] ETHANOL-INDUCED SUPEROXIDE RADICALS IN FETAL CORTICAL NEURONS: CELLULAR ROS NETWORKALCOHOLISM, Issue 2008Amina E Jamali Alcohol exposure to the developing brain compromises both neurons and glial functions. While neurons are considered the primary targets, microglia may play a neurotoxic role in this process. Previous studies demonstrated that neuron death is due to oxidative stress and mitochondrially mediated (Intrinsic). These studies showed a rapid increase (within minutes) in reactive oxygen species (ROS). Due to the diffusive nature of ethanol and multiple sources of free radicals, we sought to determine the primary source of superoxide targeted by ethanol. Confocal studies of neurons suggest that the superoxide radicals may originate from the mitochondria. Using whole neurons in a luminol-based chemiluminescence assay (Diogenes) we detected superoxide radicals in the extracellular mileu. We observed a two-three fold transient increase in the steady state generation of superoxide radicals between 20 minutes to one hour of ethanol exposure (4mg/ml). However, the presence of Rotenone (mitochondrial complex I inhibitor) and DPI (an inhibitor of all flavinoids) blocked the release of these superoxide radicals. Interestingly, cortical microglia treated identically with ethanol, showed a greater than five fold increase in superoxide generation with a maximum at one hour. Moreover, since ethanol is known to induce hydrogen peroxide generation, it was used as a mimetic. Hydrogen peroxide also induced the production of superoxide different time kinetics. Thus, together these data demonstrate that ethanol induces the steady state production of superoxide radicals in the extracellular mileu in a mitochondrial dependent manner. Since NOX2 an NADPH oxidase is expressed in neurons, it is a potential candidate for the secondary sites of superoxide generation. The ROS network between mitochondria and the plasma membrane highlights new therapeutical targets to counter ethanol toxicity. [source] BP NEURAL NETWORK FOR EVALUATING SENSORY TEXTURE PROPERTIES OF COOKED SAUSAGEJOURNAL OF SENSORY STUDIES, Issue 6 2009QING-LI DONG ABSTRACT In order to replace sensory evaluation by instrumental measurement with more accuracy for texture properties of cooked sausage, correlation analysis between sensory and instrumental texture was established by multiple regression and back propagation (BP) neural network, respectively. Effect of different fat, salt, moisture and starch addition on the texture of cooked sausage was also investigated in this paper. It indicated that the accuracy and goodness of fit of predicting sensory hardness, cohesiveness and juiciness by BP neural network were more significant than those by multiple regressions with lower root mean square error and standard error of prediction. Although both accuracy and bias factors of two models were in acceptable range, BP neural network provides an accurate and selective method for predicting sensory texture evaluation in similar meat products. PRACTICAL APPLICATIONS The effect of different fat, salt, moisture and starch addition on textural properties of cooked sausage could be valuable to the meat industry in order to select the appropriate components for improving the texture of sausage. Artificial neural network technology used in this study can be useful for the fast, on-time and convenient detection of texture measurement by instrumental instead of sensory evaluation. [source] Approximation algorithms for constructing wavelength routing networksNETWORKS: AN INTERNATIONAL JOURNAL, Issue 1 2002Refael Hassin Abstract Consider a requirement graph whose vertices represent customers and an edge represents the need to route a unit of flow between its end vertices along a single path. All these flows are to be routed simultaneously. A solution network consists of a (multi)graph on the same set of vertices, such that it is possible to route simultaneously all of the required flows in such a way that no edge is used more than K times. The SYNTHESIS OF WAVELENGTH ROUTING NETWORK (SWRN) problem is to compute a solution network of a minimum number of edges. This problem has significant importance in the world of fiber-optic networks where a link can carry a limited amount of different wavelengths and one is interested in finding a minimum-cost network such that all the requirements can be carried in the network without changing the wavelength of a path at any of its internal vertices. In this paper, we prove that the SWRN problem is NP-hard for any constant K (K , 2). Then, we assume that GR is a clique with n vertices and we find an "almost" optimal solution network for all values of K (K = o(n)) and present a Min{(K + 1)/2, 2 + 2/(K , 1)}-approximation algorithm for the general case and a 2-approximation algorithm for d -regular graphs. © 2002 Wiley Periodicals, Inc. [source] ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORKAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 3 2007Catriona M. Queen Summary The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM. [source] PREPROCESSING RULES FOR TRIANGULATION OF PROBABILISTIC NETWORKS,COMPUTATIONAL INTELLIGENCE, Issue 3 2005Hans L. Bodlaender Currently, the most efficient algorithm for inference with a probabilistic network builds upon a triangulation of a network's graph. In this paper, we show that pre-processing can help in finding good triangulations for probabilistic networks, that is, triangulations with a maximum clique size as small as possible. We provide a set of rules for stepwise reducing a graph, without losing optimality. This reduction allows us to solve the triangulation problem on a smaller graph. From the smaller graph's triangulation, a triangulation of the original graph is obtained by reversing the reduction steps. Our experimental results show that the graphs of some well-known real-life probabilistic networks can be triangulated optimally just by preprocessing; for other networks, huge reductions in their graph's size are obtained. [source] RACE, FRIENDSHIP NETWORKS, AND VIOLENT DELINQUENCYCRIMINOLOGY, Issue 4 2006DANA L. HAYNIE Although a growing body of literature emphasizes the role of friendship networks and peer relations for youth involvement in violence and delinquency, little research has examined the role of friendship networks in understanding the varying involvement of different racial-ethnic groups in violence. Using data from approximately 13,000 respondents to the first two waves of the National Longitudinal Study of Adolescent Health (Add Health), we explore the ability of friendship networks to account for the differential rates of violence among racial-ethnic groups. In addition, we evaluate whether race moderates the degree to which friendship characteristics predict adolescent violence. Findings indicate significant differences in the structure and behavioral orientation of friendship networks across racial-ethnic identities. Moreover, incorporating characteristics of friendship networks into multivariate analyses accounts for greater involvement in violence among black and Hispanic youths. Network racial heterogeneity and friends' popularity also emerge as particular network characteristics that operate differently for black and white youth. [source] RICE PRODUCER-PROCESSOR NETWORKS IN CÔTE D'IVOIRE,GEOGRAPHICAL REVIEW, Issue 2 2009Laurence Becker ABSTRACT. Pressured by structural adjustment loan conditions, Côte d'Ivoire reduced state support for rice production and processing during the 1990s. In this article we examine how various actors in the rice commodity chain adapted to the macroeconomic reforms. Following a brief history of the rice sector, we present the results of fieldwork based on interviews conducted in 2002 of farmers, millers, traders, and workers in the state extension service and nongovernmental organizations. We found that, in the absence of state supports for farmers, private millers became the focal point of regional producer-processor rice networks. The four networks identified became the sole source of domestic commercial rice when the state removed subsidies for fertilizer and modern seeds, privatized extension, and liberalized prices and imports. To increase their role in the national rice supply, the rice networks may need support through microlending and a focus on niche markets. [source] COMPARING INVASIVE NETWORKS: CULTURAL AND POLITICAL BIOGRAPHIES OF INVASIVE SPECIES,GEOGRAPHICAL REVIEW, Issue 2 2004PAUL ROBBINS ABSTRACT. Under what cultural and political conditions do certain species become successful invaders? What impact does species invasion have on human culture and politics? The work assembled in this special issue of the Geographical Review suggests complex interspecies interactions that complicate any answer to these questions. It demonstrates the need to advance a more integrative human/environment approach to species invasion than has hitherto been seen. Reviewing the concepts demonstrated in these articles and applying them to case histories of Mimosaceae (a family that includes genera such as Acacia, Prosopis, and Mimosa) invasion, two general principles become clear. The status and identification of any species as an invader, weed, or exotic are conditioned by cultural and political circumstances. Furthermore, because the human "preparation of landscape" is a prerequisite for most cases of invasion, and because species invasions impact local culture and politics in ways that often feed back into the environmental system, specific power-laden networks of human and non-human actors tend to create the momentum for invasion. It is therefore possible to argue a more general cultural and political account of contemporary species expansion: It is not species but sociobiological networks that are invasive. [source] FROM EXOGENOUS TO ENDOGENOUS ECONOMIC NETWORKS: INTERNET APPLICATIONSJOURNAL OF ECONOMIC SURVEYS, Issue 5 2006Alessio D'Ignazio Abstract Economic agents' behaviour is affected by their position in a network, either exogenous or endogenous, in which they interact with a sub-set of neighbours only. The network's links, which may be generated by vertical and/or horizontal relations, or by more complex morphologies, may explain the transition between dynamic equilibria and the instability of economic aggregates. Moreover, networks shape strategic interaction among agents by determining their strategies; the problem of access and interconnection, particularly relevant in the Internet, is perhaps the best example. A two-way feedback between strategies and network structures arises instead when links are endogenous: those features are clearly shown in the mechanism underlying the formation of peering links and R & D networks. [source] DYNAMIC MODELING OF RETORT PROCESSING USING NEURAL NETWORKSJOURNAL OF FOOD PROCESSING AND PRESERVATION, Issue 2 2002C. R. CHEN ABSTRACT Two neural network approaches , a moving-window and hybrid neural network , which combine neural network with polynomial regression models, were used for modeling F(t) and Qv(t) dynamic functions under constant retort temperature processing. The dynamic functions involved six variables: retort temperature (116,132C), thermal diffusivity (1.5,2.3 × 10,7m2/s), can radius (40,61 mm), can height (40,61 mm), and quality kinetic parameters z (15,39C) and D (150,250 min). A computer simulation designed for process calculations of food thermal processing systems was used to provide the fundamental data for training and generalization of ANN models. Training data and testing data were constructed by both second order central composite design and orthogonal array, respectively. The optimal configurations of ANN models were obtained by varying the number of hidden layers, number of neurons in hidden layer and learning runs, and a combination of learning rules and transfer function. Results demonstrated that both neural network models well described the F(t) and Qv(t) dynamic functions, but moving-window network had better modeling performance than the hybrid ANN models. By comparison of the configuration parameters, moving-window ANN models required more neurons in the hidden layer and more learning runs for training than the hybrid ANN models. [source] WORKINGS OF THE MELTING POT: SOCIAL NETWORKS AND THE EVOLUTION OF POPULATION ATTRIBUTES,JOURNAL OF REGIONAL SCIENCE, Issue 2 2007Jan K. Brueckner ABSTRACT This paper links the two nascent economic literatures on social networks and cultural assimilation by investigating the evolution of population attributes in a simple model where agents are influenced by their acquaintances. The main conclusion of the analysis is that attributes converge to a melting-pot equilibrium, where everyone is identical, provided the social network exhibits a sufficient degree of interconnectedness. When the model is extended to allow an expanding acquaintance set, convergence is guaranteed provided a weaker interconnectedness condition is satisfied, and convergence is rapid. If the intensity of interactions with acquaintances becomes endogenous, convergence (when it occurs) is slowed when agents prefer to interact with people like themselves and hastened when interaction with dissimilar agents is preferred. [source] PREDICTION OF MECHANICAL PROPERTIES OF CUMIN SEED USING ARTIFICIAL NEURAL NETWORKSJOURNAL OF TEXTURE STUDIES, Issue 1 2010M.H. SAIEDIRAD ABSTRACT In this paper, two artificial neural networks (ANNs) are applied to acquire the relationship between the mechanical properties and moisture content of cumin seed, using the data of quasi-static loading test. In establishing these relationship, the moisture content, seed size, loading rate and seed orientation were taken as the inputs of both models. The force and energy required for fracturing of cumin seed, under quasi-static loading were taken as the outputs of two models. The activation function in the output layer of models obeyed a linear output, whereas the activation function in the hidden layers were in the form of a sigmoid function. Adjusting ANN parameters such as learning rate and number of neurons and hidden layers affected the accuracy of force and energy prediction. Comparison of the predicted and experimented data showed that the ANN models used to predict the relationships of mechanical properties of cumin seed have a good learning precision and good generalization, because the root mean square errors of the predicated data by ANNs were rather low (4.6 and 7.7% for the force and energy, respectively). PRACTICAL APPLICATIONS Cumin seed is generally used as a food additive in the form of powder for imparting flavor to different food preparations and for a variety of medicinal properties. Physical properties of cumin seeds are essential for the design of equipment for handling, harvesting, aeration, drying, storing, grinding and processing. For powder preparation especially the fracture behavior of the seeds are essential. These properties are affected by numerous factors such as size, form and moisture content of the grain and deformation speed. A neural network model was developed that can be used to predict the relationships of mechanical properties. Artificial neural network models are powerful empirical models approach, which can be compared with mathematical models. [source] APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2006Moon Seong Rang ABSTRACT: The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall-runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km2 watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth-order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea. [source] PREDICTION OF LOCAL SCOUR AROUND BRIDGE PIERS USING ARTIFICIAL NEURAL NETWORKS,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2006Sung-Uk Choi ABSTRACT: This paper describes a method for predicting local scour around bridge piers using an artificial neural network (ANN). Methods for selecting input variables, calibrations of network control parameters, learning process, and verifications are also discussed. The ANN model trained by laboratory data is applied to both laboratory and field measurements. The results illustrate that the ANN model can be used to predict local scour in the laboratories and in the field better than other empirical relationships that are currently in use. A parameter study is also carried out to investigate the importance of each input variable as reflected in data. [source] A BRIEF INTRODUCTION TO SCALE-FREE NETWORKSNATURAL RESOURCE MODELING, Issue 1 2006WILLIAM J. REED ABSTRACT. This article provides a brief introduction to scale-free networks. The notion of a scale-free network is defined and some examples given. Properties frequently exhibited by scale-free networks are discussed. The importance of the phenomenon of preferential attachment in generating scale-free networks is illustrated with two examples for the spread of a persistent disease. The models are similar in that they both yield a total infected population (1) which is geometrically distributed, and growing exponentially in expectation; and (2) in which the average distance from the original source of infection grows in a similar way over time. However one model, which has preferential attachment (infection), yields a scale-free network, while the other which has homogeneous infectivity does not. The possible application of the theory of scale-free networks to resource management is briefly discussed. [source] An ejection chain algorithm for the quadratic assignment problemNETWORKS: AN INTERNATIONAL JOURNAL, Issue 3 2010Cesar Rego Abstract In this study, we present a new tabu search algorithm for the quadratic assignment problem (QAP) that utilizes an embedded neighborhood construction called an ejection chain. Our ejection chain approach provides a combinatorial leverage effect, where the size of the neighborhood grows multiplicatively while the effort of finding a best move in the neighborhood grows only additively. Our results illustrate that significant improvement in solution quality is obtained in comparison to the traditional swap neighborhood. We also develop two multistart tabu search algorithms utilizing the ejection chain approach in order to demonstrate the power of embedding this neighborhood construction within a more sophisticated heuristic framework. Comparisons to the best large neighborhood approaches from the literature are presented. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] Mixed search number and linear-width of interval and split graphsNETWORKS: AN INTERNATIONAL JOURNAL, Issue 3 2010Fedor V. Fomin Abstract We show that the mixed search number and the linear-width of interval graphs and of split graphs can be computed in linear time and in polynomial time, respectively. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] A mean-variance model for the minimum cost flow problem with stochastic arc costsNETWORKS: AN INTERNATIONAL JOURNAL, Issue 3 2010Stephen D. Boyles Abstract This article considers a minimum cost flow problem where arc costs are uncertain, and the decision maker wishes to minimize both the expected flow cost and the variance of this cost. Two optimality conditions are given, one based on cycle marginal costs, and another based on concepts of network equilibrium. Solution methods are developed based on these conditions. The value of information is also studied, and efficient approximation techniques are developed for the specific case of learning the exact cost of one or more arcs a priori. Finally, numerical results compare the solution methods developed in this work: the minimum mean cycle canceling algorithm performs better on all of the networks tested, although the equilibrium-based algorithm is more competitive for large networks. Solution sensitivity to input parameters is also examined, as is the performance of the approximation techniques for the value of information. Approximation techniques based on arc cost distributions were found to outperform those based on properties of optimal flows. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] Join tree propagation with prioritized messagesNETWORKS: AN INTERNATIONAL JOURNAL, Issue 4 2010C. J. Butz Abstract Current join tree propagation algorithms treat all propagated messages as being of equal importance. On the contrary, it is often the case in real-world Bayesian networks that only some of the messages propagated from one join tree node to another are relevant to subsequent message construction at the receiving node. In this article, we propose the first join tree propagation algorithm that identifies and constructs the relevant messages first. Our approach assigns lower priority to the irrelevant messages as they only need to be constructed so that posterior probabilities can be computed when propagation terminates. Experimental results, involving the processing of evidence in four real-world Bayesian networks, empirically demonstrate an improvement over the state-of-the-art method for exact inference in discrete Bayesian networks. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] Rainbow trees in graphs and generalized connectivityNETWORKS: AN INTERNATIONAL JOURNAL, Issue 4 2010Gary Chartrand Abstract An edge-colored tree T is a rainbow tree if no two edges of T are assigned the same color. Let G be a nontrivial connected graph of order n and let k be an integer with 2 , k , n. A k -rainbow coloring of G is an edge coloring of G having the property that for every set S of k vertices of G, there exists a rainbow tree T in G such that S , V(T). The minimum number of colors needed in a k -rainbow coloring of G is the k -rainbow index of G. For every two integers k and n , 3 with 3 , k , n, the k -rainbow index of a unicyclic graph of order n is determined. For a set S of vertices in a connected graph G of order n, a collection {T1,T2,,,T,} of trees in G is said to be internally disjoint connecting S if these trees are pairwise edge-disjoint and V(Ti) , V(Tj) = S for every pair i,j of distinct integers with 1 , i,j , ,. For an integer k with 2 , k , n, the k -connectivity ,k(G) of G is the greatest positive integer , for which G contains at least , internally disjoint trees connecting S for every set S of k vertices of G. It is shown that ,k(Kn)=n,,k/2, for every pair k,n of integers with 2 , k , n. For a nontrivial connected graph G of order n and for integers k and , with 2 , k , n and 1 , , , ,k(G), the (k,,)-rainbow index rxk,,(G) of G is the minimum number of colors needed in an edge coloring of G such that G contains at least , internally disjoint rainbow trees connecting S for every set S of k vertices of G. The numbers rxk,,(Kn) are determined for all possible values k and , when n , 6. It is also shown that for , , {1, 2}, rx3,,(Kn) = 3 for all n , 6. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] Public congestion network situations and related gamesNETWORKS: AN INTERNATIONAL JOURNAL, Issue 4 2010John Kleppe Abstract This article analyses congestion in network situations from a cooperative game theoretic perspective. In network situations, players have to connect themselves to a source. As we consider publicly available networks, any group of players is allowed to use the entire network to establish their connections. We deal with the problem of finding an optimal network and discuss the associated cost allocation problem. For the latter, we introduce two different transferable utility cost games. For concave cost functions, we use the direct cost game, in which coalition costs are based on what a coalition can do in the absence of other players. This article, however, mainly discusses network situations with convex cost functions, which are analyzed by the use of the marginal cost game. In this game, the cost of a coalition is defined as the additional cost it induces when it joins the complementary group of players. We prove that this game is concave. Furthermore, we define a cost allocation by means of three equal treatment principles and show that this allocation is an element of the core of the marginal cost game. These results are extended to a class of continuous network situations and associated games. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] Distinguished vertices in probabilistic rooted graphsNETWORKS: AN INTERNATIONAL JOURNAL, Issue 3 2010Gary Gordon Abstract The expected number of vertices that remain joined to the root vertex s of a rooted graph Gs when edges are prone to fail is a polynomial EV(Gs; p) in the edge probability p that depends on the location of s. We show that optimal locations for the root can vary arbitrarily as p varies from 0 to 1 by constructing a graph in which every permutation of k -specified vertices is the "optimal" ordering for some p, 0 < p < 1. We also investigate zeroes of EV(Gs; p), proving that the number of vertices of G is bounded by the size of the largest rational zero. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] Application of semi definite relaxation and variable neighborhood search for multiuser detection in synchronous CDMANETWORKS: AN INTERNATIONAL JOURNAL, Issue 3 2010Abdel Lisser Abstract In this article, a detection strategy based on variable neighborhood search (VNS) and semidefinite relaxation of the multiuser model maximum likelihood (ML) is investigated. The VNS method provides a good method for solving the ML problem while keeping the integer constraints. A SDP relaxation is used as an efficient way to generate an initial solution in a limited amount of time, in particular using early termination. The SDP resolution tool used is the spectral bundle method developed by Helmberg. We show that using VNS can result in a better error rate, but at a cost of calculation time. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] A branch-and-cut algorithm for partition coloringNETWORKS: AN INTERNATIONAL JOURNAL, Issue 3 2010Yuri Frota Abstract Let G = (V, E, Q) be a undirected graph, where V is the set of vertices, E is the set of edges, and Q = {Q1,,,Qq} is a partition of V into q subsets. We refer to Q1,,,Qq as the components of the partition. The partition coloring problem (PCP) consists of finding a subset V, of V with exactly one vertex from each component Q1,,,Qq and such that the chromatic number of the graph induced in G by V, is minimum. This problem is a generalization of the graph coloring problem. This work presents a branch-and-cut algorithm proposed for PCP. An integer programing formulation and valid inequalities are proposed. A tabu search heuristic is used for providing primal bounds. Computational experiments are reported for random graphs and for PCP instances originating from the problem of routing and wavelength assignment in all-optical WDM networks. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] The ,-reliable minimax and maximin location problems on a network with probabilistic weightsNETWORKS: AN INTERNATIONAL JOURNAL, Issue 2 2010Jiamin Wang Abstract We study the ,-reliable minimax and maximin location problems on a network when the weights associated with the nodal points are random variables. In the ,-reliable minimax (maximin) problem, we locate a single facility so as to minimize (maximize) the upper (lower) bound on the maximum (minimum) weighted distance from the nodes to the facility with a probability greater than or equal to a pre-specified level ,. It is shown that under some conditions the two probabilistic models are equivalent to their deterministic counterparts. Solution procedures are developed to solve the problems with weights of continuous and discrete probability distributions. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] On the complexity of resilient network designNETWORKS: AN INTERNATIONAL JOURNAL, Issue 2 2010Artur Tomaszewski Abstract In this article we prove ,,,,-hardness of two well-known optimization problems related to the design of multicommodity flow networks with two different methods for providing network resiliency against failures: path diversity and flow restoration. Path diversity is a static mechanism that consists of using, for each demand, a number of paths and oversizing the flows assigned to these paths so that for any failure the total surviving flow is not less than the volume of the demand. By contrast, flow restoration is a dynamic mechanism that consists of reassigning the failed flows to backup paths when a failure occurs. Both mechanisms are of practical interest because although flow restoration is in general superior to path diversity in terms of the required amount of resource capacity, it might be too complicated to implement. By providing an appropriate reduction from the fractional graph coloring problem, we show that both problems are ,,,,-hard in the general case of failure scenarios that admit simultaneous failures of multiple links. Finally, we discuss how to efficiently solve the two problems using path generation techniques. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] A branch-and-cut algorithm for the k -edge connected subgraph problemNETWORKS: AN INTERNATIONAL JOURNAL, Issue 1 2010F. Bendali Abstract In this article, we consider the k -edge connected subgraph problem from a polyhedral point of view. We introduce further classes of valid inequalities for the associated polytope and describe sufficient conditions for these inequalities to be facet defining. We also devise separation routines for these inequalities and discuss some reduction operations that can be used in a preprocessing phase for the separation. Using these results, we develop a Branch-and-Cut algorithm and present some computational results. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010 [source] | ||||||||||||||||||||||||||||||||||||||||