Network design problems usually include the selection of nodes and arcs from lists of potential sets to accomplish certain desirable properties. Foremost is often the capability to accommodate the flow demands at a reasonable cost. In many applications it is also imperative to have built-in reliability or survivability of the network. Delays of traffic are undesirable since it affects Quality of Service (QoS) to clients of the network. It is seldom possible to start a design for a new network and have the luxury of designing topology as well as the optimal flow(routing). In this dissertation we consider the construction of a network optimization system. This system may be used in the planning of network mesh topologies and link capacities to avoid costly designs and congestion or to give advice on congestion relief in existing networks. This is done by selecting parts of a network that may be prone to congestion and model this part by using mixed integer programming techniques. These models are then solved by using a software product called CPLEX and various facilities are built into the decision support system to allow the decision maker to experiment with some topological and flow requirement changes. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2005.

Climatic variability and change can have profound impacts on human societies and wildlife habitats. Extreme events and natural hazards such as floods, droughts, and windstorms, can lead to loss of lives, economic damages, and disruption in livelihoods, infrastructure, and ecosystems. These impacts depend on the intensity and the magnitude of the hazard and the characteristics of the society hit by the disaster. Investigating and predicting adverse effects of frequent climatic hazards are essential for policy makers and resource managers to plan for the future and be prepared for the consequences of these types of natural disasters. Vulnerability assessments provide a framework to detect the potential threats by exploring the nature of the hazard as well as the political, economic, and social conditions that are expected to affect the capacity of communities to cope with or adapt to that hazard. This research involves the development of a framework for vulnerability assessment of flood and drought at the river basin, sub-catchment, and community scale. The vulnerability assessment method is composed of three major components of exposure, sensitivity, and adaptive capacity. Several indicators are identified to represent these major components of the vulnerability structure. The developed vulnerability assessment has then been implemented on the Upper Ottawa River Basin, Canada. A Geographic Information System-based methodology is used to manage a wide variety of data, to aggregate and integrate several indicators including socio-economic and biophysical indicators, and to visualize the final vulnerability map. The studied areas are categorized in three levels of the vulnerability, high, moderate, and low. North Bay is identified as highly vulnerable to both flood and drought risk. Noranda is also classified as a highly flood-vulnerable area. The vulnerability assessment will provide a valuable insight for mitigation planning as well as prioritizing resource allocation for decision makers. In this research, the location and adequacy of the hydrometric monitoring stations in the Upper Ottawa River Basin are evaluated using the vulnerability map for optimum design of monitoring network.

Network design problems usually include the selection of nodes and arcs from lists of potential sets to accomplish certain desirable properties. Foremost is often the capability to accommodate the flow demands at a reasonable cost. In many applications it is also imperative to have built-in reliability or survivability of the network. Delays of traffic are undesirable since it affects Quality of Service (QoS) to clients of the network. It is seldom possible to start a design for a new network and have the luxury of designing topology as well as the optimal flow(routing). In this dissertation we consider the construction of a network optimization system. This system may be used in the planning of network mesh topologies and link capacities to avoid costly designs and congestion or to give advice on congestion relief in existing networks. This is done by selecting parts of a network that may be prone to congestion and model this part by using mixed integer programming techniques. These models are then solved by using a software product called CPLEX and various facilities are built into the decision support system to allow the decision maker to experiment with some topological and flow requirement changes. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2005.

Network design problems involving survivability usually include trade-off of the potential for lost revenues and customer goodwill against the extra costs required to increase the network survivability. It also involves selection of nodes and edges from lists of potential sets to accomplish certain desirable properties. In many applications it is imperative to have built-in reliability or survivability of the network. Delays of traffic are undesirable since it affects quality of service (QoS) to clients of the network. In this dissertation we consider the construction of an optimization system for network design with survivability properties that may help in the planning of mesh topologies while maintaining a certain degree of survivability of the network. This is done by providing for at least two diverse paths between certain "special" nodes to provide protection against any single edge or node failure. This part is modelled by using mixed integer programming techniques. A software product called CPLEX then solves these models and various facilities are built into the decision support system to allow the decision maker to experiment with some topological and flow requirement changes. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2007

In recent years the amount of newly registered electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles has increased rapidly in the Swedish market. These vehicles could be classified as battery-powered electric vehicles, and a majority carry a lithium-ion battery. The demand for lithium is expected to increase considerably, as a result of such a swift growth in battery-powered electric vehicles. Thus, if the recycling rate of lithium stays at a low level, demand could reach a scarcity-level by 2050. While neither any infrastructure nor an established process for recycling lithium-ion batteries currently exists in Sweden, this study aims to provide necessary input and verified tools for the design of a future reverse supply chain for discarded lithium-ion batteries in Sweden. The literature review of this study covers the subjects of reverse logistics, supply chain network design, and operations research. A thorough situation analysis of the Swedish market for battery-powered electric vehicles is conducted, and the composition, function, and characteristics of lithium-ion batteries are studied. The study finds that estimations of future demand of recyclable lithium-ion batteries in Sweden could be between 206 711 and 726 974 tons accumulated, based on actual and predicted sales numbers until 2030. Even if it is obvious that there are going to be large quantities of such batteries requiring recycling in the future, and even if some established processes exist, there is no defined supply chain for the collection of those batteries. Finally, a mixed-integer programming model for the design and development of a future reverse supply chain is presented. The model, characterized as a discrete multi-period facility location/allocation model, can with minor modifications be used for problems with fluctuating demand or when the demand is assumed to slowly progress until it has reached a steady state.

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Future electrical power networks should support the integration of distributed renewable energy sources, which may be contributed by individual customers instead of utility companies. Such a demand poses new challenges to power distribution network design, since the energy generation, energy consumption, and power flow all become highly dynamic. An inappropriate network design may not only waste much energy in power distribution but also incur high cost in network construction. In this thesis, we study the optimal network design problem under a dynamic current injection model. We investigate different optimization methods to obtain the optimal network structure that can better adapt to dynamic energy generation/consumption requirements and is more efficient than traditional tree-structured power networks. By predicting users' potential load in the network, network design with our method results in significant energy saving. / Graduate / 0984

We consider several combinatorial optimization problems in a geometric set- ting. The first problem we consider is the problem of clustering to minimize the sum of radii. Given a positive integer k and a set of points with interpoint distances that satisfy the definition of being a "metric", we define a ball centered at some input point and having some radius as the set of all input points that are at a distance smaller than the radius of the ball from its center. We want to cover all input points using at most k balls so that the sum of the radii of the balls chosen is minimized. We show that when the points lie in some Euclidean space and the distance measure is the standard Euclidean metric, we can find an exact solution in polynomial time under standard assumptions about the model of computation. The second problem we consider is the Network Spanner Topology Design problem. In this problem, given a set of nodes in the network, represented by points in some geometric setting - either a plane or a 1.5-D terrain, we want to compute a height assignment function h that assigns a height to a tower at every node such that the set of pairs of nodes that can form a direct link with each other under this height function forms a connected spanner. A pair of nodes can form a direct link if they are within a bounded distance B of each other and the heights of towers at the two nodes are sufficient to achieve Line-of-Sight connectivity - i.e. the straight line connecting the top of the towers lies above any obstacles. In the planar setting where the obstacles are modeled as having a certain maximum height and minimum clearance distance, we give a constant factor approximation algorithm. In the case where the points lie on a 1.5-D terrain we illustrate that it might be hard to use Computational Geometry to achieve efficient approximations. The final problem we consider is the Multiway Barrier Cut problem. Here, given a set of points in the plane and a set of unit disk sensors also in the plane such that any path in the plane between any pair of input points hits at least one of the given sensor disks we consider the problem of finding the minimum size subset of these disks that still achieves this separation. We give a constant factor approximation algorithm for this problem.

We consider the survivable network design problem - the problem of designing, at minimum cost, a network with edge-connectivity requirements. As special cases, this problem encompasses the Steiner tree problem, the traveling salesman problem and the k-connected network design problem. We establish a property, referred to as the parsimonious property, of the linear programming (LP) relaxation of a classical formulation for the problem. The parsimonious property has numerous consequences. For example, we derive various structural properties of these LP relaxations, we present some algorithmic improvements and we perform tight worstcase analyses of two heuristics for the survivable network design problem.

Un des buts principaux de la biologie évolutive et de la médecine moléculaire consiste à reconstruire les relations phylogénétiques entre organismes à partir de leurs séquences moléculaires. En littérature, cette question est connue sous le nom d’inférence phylogénétique et a d'importantes applications dans la recherche médicale et pharmaceutique, ainsi que dans l’immunologie, l’épidémiologie, et la dynamique des populations. L’accumulation récente de données de séquences d’ADN dans les bases de données publiques, ainsi que la facilité relative avec laquelle des données nouvelles peuvent être obtenues, rend l’inférence phylogénétique particulièrement difficile (l'inférence phylogénétique est un problème NP-Hard sous tous les critères d’optimalité connus), de telle manière que des nouveaux critères et des algorithmes efficaces doivent être développés. Cette thèse a pour but: (i) d’analyser les limites mathématiques et biologiques des critères utilisés en inférence phylogénétique, (ii) de développer de nouveaux algorithmes efficaces permettant d’analyser de plus grands jeux de données.