Relevant bibliographies by topics / Network analysis (Planning) Combinatorial optimization / Dissertations / Theses
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Dissertations / Theses on the topic 'Network analysis (Planning) Combinatorial optimization'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Hong, Chyi-Fu. "O(n) planar network shortest path algorithm." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/24841.

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2

Parmar, Amandeep. "Integer programming approaches to networks with equal-split restrictions." Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-05032007-170932/.

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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2008.<br>Nemhauser,George, Committee Member ; Gu, Zonghao, Committee Member ; Ergun, Ozlem, Committee Member ; Sokol, Joel, Committee Co-Chair ; Ahmed, Shabbir, Committee Chair.
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3

Gupta, Kapil. "Combinatorial optimization and application to DNA sequence analysis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26676.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Lee, Eva K.; Committee Member: Barnes, Earl; Committee Member: Fan, Yuhong; Committee Member: Johnson, Ellis; Committee Member: Yuan, Ming. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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4

Wang, Shentao. "Supply chain planning using network flow optimization." online access from Digital Dissertation Consortium access full-text, 2003. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?MQ89164.

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5

Zhao, Kun. "Mathematical Methods for Network Analysis, Proteomics and Disease Prevention." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_diss/6.

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This dissertation aims at analyzing complex problems arising in the context of dynamical networks, proteomics, and disease prevention. First, a new graph-based method for proving global stability of synchronization in directed dynamical networks is developed. This method utilizes stability and graph theories to clarify the interplay between individual oscillator dynamics and network topology. Secondly, a graph-theoretical algorithm is proposed to predict Ca2+-binding site in proteins. The new algorithm enables us to identify previously-unknown Ca2+-binding sites, and deepens our understanding towards disease-related Ca2+-binding proteins at a molecular level. Finally, an optimization model and algorithm to solve a disease prevention problem are described at the population level. The new resource allocation model is designed to assist clinical managers to make decisions on identifying at-risk population groups, as well as selecting a screening and treatment strategy for chlamydia and gonorrhea patients under a fixed budget. The resource allocation model and algorithm can have a significant impact on real treatment strategy issues.
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6

Hojnacki, Susan M. "Optimizing algorithms for shortest path analysis /." Online version of thesis, 1991. http://hdl.handle.net/1850/11143.

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7

He, Qie. "Topics in discrete optimization: models, complexity and algorithms." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50237.

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In this dissertation we examine several discrete optimization problems through the perspectives of modeling, complexity and algorithms. We first provide a probabilistic comparison of split and type 1 triangle cuts for mixed-integer programs with two rows and two integer variables in terms of cut coefficients and volume cutoff. Under a specific probabilistic model of the problem parameters, we show that for the above measure, the probability that a split cut is better than a type 1 triangle cut is higher than the probability that a type 1 triangle cut is better than a split cut. The analysis also suggests some guidelines on when type 1 triangle cuts are likely to be more effective than split cuts and vice versa. We next study a minimum concave cost network flow problem over a grid network. We give a polytime algorithm to solve this problem when the number of echelons is fixed. We show that the problem is NP-hard when the number of echelons is an input parameter. We also extend our result to grid networks with backward and upward arcs. Our result unifies the complexity results for several models in production planning and green recycling including the lot-sizing model, and gives the first polytime algorithm for some problems whose complexities were not known before. Finally, we examine how much complexity randomness will bring to a simple combinatorial optimization problem. We study a problem called the sell or hold problem (SHP). SHP is to sell k out of n indivisible assets over two stages, with known first-stage prices and random second-stage prices, to maximize the total expected revenue. Although the deterministic version of SHP is trivial to solve, we show that SHP is NP-hard when the second-stage prices are realized as a finite set of scenarios. We show that SHP is polynomially solvable when the number of scenarios in the second stage is constant. A max{1/2,k/n}-approximation algorithm is presented for the scenario-based SHP.
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8

Guan, Junfei. "Simultaneous optimization of transit line configuration and passenger line assignment /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?CIVL%202004%20GUAN.

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9

Boginski, Vladimir L. "Optimization and information retrieval techniques for complex networks." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011379.

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10

Haiba, Mohamed Salem. "A study and implementation of the network flow problem and edge integrity of networks." Virtual Press, 1991. http://liblink.bsu.edu/uhtbin/catkey/834644.

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Fundamental problems in graph theory are of four types existence, construction, enumeration and optimization problems. Optimization problems lie at the interface between computer science and the field of operations research and are of primary importance in decision-making. In this thesis, two optimization problems are studied: the edge-integrity of networks and the network flow problem. An implementation of the corresponding algorithms is also realized.The edge integrity of a communication network provides a way to assess the vulnerability of the network to disruption through the destruction or failure of some of its links. While the computation of the edge-integrity of graphs in general has been proven to be NPcomplete, a recently published paper was devoted to a good algorithm using a technique of edge separation sequence for computing the edge integrity of trees. The main results of this paper will be presented and an implementation of this algorithm is achieved.The network flow problem models a distribution system in which commodities are flowing through an interconnected network. The goal is to find a maximum feasible flow and its value, given the capacity constraints for each edge. The three majors algorithms for this problem (Ford -Fulkerso n, Edmonds-Karp method, MPKM algorithm) are discussed, their complexities compared and an implementation of the Ford-Fulkerson and the MPKM algorithms is presented.<br>Department of Computer Science
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11

Brayman, Vladimir. "Hierarchical distributed algorithm for optimization of flows and prices in logistics distribution networks /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/5876.

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12

Palhares, André Vitor de Almeida. "Probabilistic Risk Assessment in Clouds: Models and Algorithms." Universidade Federal de Pernambuco, 2012. https://repositorio.ufpe.br/handle/123456789/10423.

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Submitted by Pedro Henrique Rodrigues (pedro.henriquer@ufpe.br) on 2015-03-04T17:17:29Z No. of bitstreams: 2 dissert-avap.pdf: 401311 bytes, checksum: 5bd3f82323bd612e8265a6ab8a55eda0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)<br>Made available in DSpace on 2015-03-04T17:17:29Z (GMT). No. of bitstreams: 2 dissert-avap.pdf: 401311 bytes, checksum: 5bd3f82323bd612e8265a6ab8a55eda0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2012-03-08<br>Cloud reliance is critical to its success. Although fault-tolerance mechanisms are employed by cloud providers, there is always the possibility of failure of infrastructure components. We consequently need to think proactively of how to deal with the occurrence of failures, in an attempt to minimize their effects. In this work, we draw the risk concept from probabilistic risk analysis in order to achieve this. In probabilistic risk analysis, consequence costs are associated to failure events of the target system, and failure probabilities are associated to infrastructural components. The risk is the expected consequence of the whole system. We use the risk concept in order to present representative mathematical models for which computational optimization problems are formulated and solved, in a Cloud Computing environment. In these problems, consequence costs are associated to incoming applications that must be allocated in the Cloud and the risk is either seen as an objective function that must be minimized or as a constraint that should be limited. The proposed problems are solved either by optimal algorithm reductions or by approximation algorithms with provably performance guarantees. Finally, the models and problems are discussed from a more practical point of view, with examples of how to assess risk using these solutions. Also, the solutions are evaluated and results on their performance are established, showing that they can be used in the effective planning of the Cloud.
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13

Yim, Ka-wing. "A reliability-based land use and transportation optimization model." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B34618879.

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14

Wong, Chi-Fai. "CACAO : client-assisted channel assignment optimization for uncoordinated home WLANs /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20WONG.

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15

Yim, Ka-wing, and 嚴家榮. "A reliability-based land use and transportation optimization model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34618879.

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16

Madduri, Kamesh. "A high-performance framework for analyzing massive complex networks." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24712.

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Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2009.<br>Committee Chair: Bader, David; Committee Member: Berry, Jonathan; Committee Member: Fujimoto, Richard; Committee Member: Saini, Subhash; Committee Member: Vuduc, Richard
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17

Anisi, David A. "On Cooperative Surveillance, Online Trajectory Planning and Observer Based Control." Doctoral thesis, KTH, Optimeringslära och systemteori, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-9990.

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The main body of this thesis consists of six appended papers. In the  first two, different  cooperative surveillance problems are considered. The second two consider different aspects of the trajectory planning problem, while the last two deal with observer design for mobile robotic and Euler-Lagrange systems respectively.In Papers A and B,  a combinatorial optimization based framework to cooperative surveillance missions using multiple Unmanned Ground Vehicles (UGVs) is proposed. In particular, Paper A  considers the the Minimum Time UGV Surveillance Problem (MTUSP) while Paper B treats the Connectivity Constrained UGV Surveillance Problem (CUSP). The minimum time formulation is the following. Given a set of surveillance UGVs and a polyhedral area, find waypoint-paths for all UGVs such that every point of the area is visible from  a point on a waypoint-path and such that the time for executing the search in parallel is minimized.  The connectivity constrained formulation  extends the MTUSP by additionally requiring the induced information graph to be  kept recurrently connected  at the time instants when the UGVs  perform the surveillance mission.  In these two papers, the NP-hardness of  both these problems are shown and decomposition techniques are proposed that allow us to find an approximative solution efficiently in an algorithmic manner.Paper C addresses the problem of designing a real time, high performance trajectory planner for an aerial vehicle that uses information about terrain and enemy threats, to fly low and avoid radar exposure on the way to a given target. The high-level framework augments Receding Horizon Control (RHC) with a graph based terminal cost that captures the global characteristics of the environment.  An important issue with RHC is to make sure that the greedy, short term optimization does not lead to long term problems, which in our case boils down to two things: not getting into situations where a collision is unavoidable, and making sure that the destination is actually reached. Hence, the main contribution of this paper is to present a trajectory planner with provable safety and task completion properties. Direct methods for trajectory optimization are traditionally based on a priori temporal discretization and collocation methods. In Paper D, the problem of adaptive node distribution is formulated as a constrained optimization problem, which is to be included in the underlying nonlinear mathematical programming problem. The benefits of utilizing the suggested method for  online  trajectory optimization are illustrated by a missile guidance example.In Paper E, the problem of active observer design for an important class of non-uniformly observable systems, namely mobile robotic systems, is considered. The set of feasible configurations and the set of output flow equivalent states are defined. It is shown that the inter-relation between these two sets may serve as the basis for design of active observers. The proposed observer design methodology is illustrated by considering a  unicycle robot model, equipped with a set of range-measuring sensors. Finally, in Paper F, a geometrically intrinsic observer for Euler-Lagrange systems is defined and analyzed. This observer is a generalization of the observer proposed by Aghannan and Rouchon. Their contractivity result is reproduced and complemented  by  a proof  that the region of contraction is infinitely thin. Moreover, assuming a priori bounds on the velocities, convergence of the observer is shown by means of Lyapunov's direct method in the case of configuration manifolds with constant curvature.<br>QC 20100622<br>TAIS, AURES
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18

"Time-varying network optimization problems." 1999. http://library.cuhk.edu.hk/record=b6073189.

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Dan Sha.<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 1999.<br>Includes bibliographical references (p. 193-206).<br>Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Mode of access: World Wide Web.<br>Abstracts in English and Chinese.
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19

Pan, Feng. "Stochastic network interdiction models and methods /." Thesis, 2005. http://repositories.lib.utexas.edu/bitstream/handle/2152/1692/panf72229.pdf.

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20

Balasundaram, Balabhaskar. "Graph theoretic generalizations of clique: optimization and extensions." 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1539.

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This dissertation considers graph theoretic generalizations of the maximum clique problem. Models that were originally proposed in social network analysis literature, are investigated from a mathematical programming perspective for the first time. A social network is usually represented by a graph, and cliques were the first models of "tightly knit groups" in social networks, referred to as cohesive subgroups. Cliques are idealized models and their overly restrictive nature motivated the development of clique relaxations that relax different aspects of a clique. Identifying large cohesive subgroups in social networks has traditionally been used in criminal network analysis to study organized crimes such as terrorism, narcotics and money laundering. More recent applications are in clustering and data mining wireless networks, biological networks as well as graph models of databases and the internet. This research has the potential to impact homeland security, bioinformatics, internet research and telecommunication industry among others. The focus of this dissertation is a degree-based relaxation called k-plex. A distance-based relaxation called k-clique and a diameter-based relaxation called k-club are also investigated in this dissertation. We present the first systematic study of the complexity aspects of these problems and application of mathematical programming techniques in solving them. Graph theoretic properties of the models are identified and used in the development of theory and algorithms. Optimization problems associated with the three models are formulated as binary integer programs and the properties of the associated polytopes are investigated. Facets and valid inequalities are identified based on combinatorial arguments. A branch-and-cut framework is designed and implemented to solve the optimization problems exactly. Specialized preprocessing techniques are developed that, in conjunction with the branch-and-cut algorithm, optimally solve the problems on real-life power law graphs, which is a general class of graphs that include social and biological networks. Computational experiments are performed to study the effectiveness of the proposed solution procedures on benchmark instances and real-life instances. The relationship of these models to the classical maximum clique problem is studied, leading to several interesting observations including a new compact integer programming formulation. We also prove new continuous non-linear formulations for the classical maximum independent set problem which maximize continuous functions over the unit hypercube, and characterize its local and global maxima. Finally, clustering and network design extensions of the clique relaxation models are explored.
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21

Fan, Wei 1974. "Optimal transit route network design problem : algorithms, implementations, and numerical results." 2004. http://hdl.handle.net/2152/12769.

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22

Fan, Wei Machemehl Randy B. "Optimal transit route network design problem algorithms, implementations, and numerical results /." 2004. http://wwwlib.umi.com/cr/utexas/fullcit?p3143741.

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23

Marcon, Alister Justin. "Flows in networks : an algorithmic approach." Thesis, 2013. http://hdl.handle.net/10210/8364.

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M.Sc. (Mathematics)<br>In Chapter 1, we consider the relevant theory pertaining to graphs and digraphs that will be used in the study of flows in networks. Warshall’s algorithm for reachability is also considered since it will allow us to ascertain whether some paths exist in some instance. In Chapter 2, we explore flows and cuts in networks. We define the basic concepts of source, sink, intermediate vertices, capacity, costs and lower-bounds. Feasible flows are defined, as well as the value of a flow. Cuts in capacitated networks are explored and further theory relating the value of a flow and cuts is given. We considered the problem of determining a maximal flow. In particular, we consider augmentations of the flow—this allows us to give a characterization of a maximal flow. The important Max-flow Min-cut theorem is also considered. After having explored the relevant theory, we move on to methods of finding a maximal flow for a given s-t network that has a capacity on each of its arcs. Firstly, we consider zero-one and unit-capacity networks since these play a role in the applications of maximal flows in Chapter 4. We, then, compile the relevant theory and algorithms in order to implement two augmenting path finding algorithms.
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24

Singh, Vidya Bhushan. "User Modeling and Optimization for Environmental Planning System Design." Thesis, 2014. http://hdl.handle.net/1805/6114.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Environmental planning is very cumbersome work for environmentalists, government agencies like USDA and NRCS, and farmers. There are a number of conflicts and issues involved in such a decision making process. This research is based on the work to provide a common platform for environmental planning called WRESTORE (Watershed Restoration using Spatio-Temporal Optimization of Resources). We have designed a system that can be used to provide the best management practices for environmental planning. A distributed system was designed to combine high performance computing power of clusters/supercomputers in running various environmental model simulations. The system is designed to be a multi-user system just like a multi-user operating system. A number of stakeholders can log-on and run environmental model simulations simultaneously, seamlessly collaborate, and make collective judgments by visualizing their landscapes. In the research, we identified challenges in running such a system and proposed various solutions. One challenge was the lack of fast optimization algorithm. In our research, several algorithms are utilized such as Genetic Algorithm (GA) and Learning Automaton (LA). However, the criticism is that LA has a slow rate of convergence and that both LA and GA have the problem of getting stuck in local optima. We tried to solve the multi-objective problems using LA in batch mode to make the learning faster and accurate. The problems where the evaluation of the fitness functions for optimization is a bottleneck, like running environmental model simulation, evaluation of a number of such models in parallel can give considerable speed-up. In the multi-objective LA, different weight pair solutions were evaluated independently. We created their parallel versions to make them practically faster in computation. Additionally, we extended the parallelism concept with the batch mode learning. Another challenge we faced was in User Modeling. There are a number of User Modeling techniques available. Selection of the best user modeling technique is a hard problem. In this research, we modeled user's preferences and search criteria using an ANN (Artificial Neural Network). Training an ANN with limited data is not always feasible. There are many situations where a simple modeling technique works better if the learning data set is small. We formulated ways to fine tune the ANN in case of limited data and also introduced the concept of Deep Learning in User Modeling for environmental planning system.
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