The purpose of this thesis is to develop some new algorithms based on optimization techniques for solving some problems in some areas of telecommunications and the Internet. There are two main parts to this thesis. In the first part we discuss optimization based stochastic and queueing models in telecommunications network corrective maintenance. In the second part we develop optimization based clustering (OBC) algorithms for network evolution and multicast routing. The most typical scenario encountered during mathematical optimization modelling in telecommunications, for example, is to minimize the cost of establishment and maintenance of the networks subject to the performance constraints of the networks and the reliability constraints of the networks as well. Most of these optimization problems are global optimization, that is, they have many local minima and most of these local minima do not provide any useful information for solving these problems. Therefore, the development of effective methods for solving such global optimization problems is important. To run the telecommunications networks with cost-effective network maintenance,we need to establish a practical maintenance model and optimize it. In the first part of the thesis, we solve a known stochastic programming maintenance optimization model with a direct method and then develop some new models. After that we introduce queue programming models in telecommunications network maintenance optimization. The ideas of profit, loss, and penalty will help telecommunications companies have a good view of their maintenance policies and help them improve their service. In the second part of this thesis we propose the use of optimization based clustering (OBC) algorithms to determine level-constrained hierarchical trees for network evolution and multicast routing. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artifiicial and real-world databases are reported. / Doctor of Philosophy

The purpose of this thesis is to develop some new algorithms based on optimization techniques for solving some problems in some areas of telecommunications and the Internet. There are two main parts to this thesis. In the first part we discuss optimization based stochastic and queueing models in telecommunications network corrective maintenance. In the second part we develop optimization based clustering (OBC) algorithms for network evolution and multicast routing. The most typical scenario encountered during mathematical optimization modelling in telecommunications, for example, is to minimize the cost of establishment and maintenance of the networks subject to the performance constraints of the networks and the reliability constraints of the networks as well. Most of these optimization problems are global optimization, that is, they have many local minima and most of these local minima do not provide any useful information for solving these problems. Therefore, the development of effective methods for solving such global optimization problems is important. To run the telecommunications networks with cost-effective network maintenance,we need to establish a practical maintenance model and optimize it. In the first part of the thesis, we solve a known stochastic programming maintenance optimization model with a direct method and then develop some new models. After that we introduce queue programming models in telecommunications network maintenance optimization. The ideas of profit, loss, and penalty will help telecommunications companies have a good view of their maintenance policies and help them improve their service. In the second part of this thesis we propose the use of optimization based clustering (OBC) algorithms to determine level-constrained hierarchical trees for network evolution and multicast routing. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artifiicial and real-world databases are reported. / Doctor of Philosophy

[Formulae and special characters can only be approximated here. Please see the pdf version of the abstract for an accurate reproduction.] This thesis is a new proposal for analysing spatial point patterns in spatial statistics using the outputs of popular techniques of (classical, non-spatial, multivariate) cluster analysis. The outputs of a chosen hierarchical algorithm, named fusion distances, are applied to investigate important spatial characteristics of a given point pattern. The fusion distances may be regarded as a missing link between the fields of spatial statistics and multivariate cluster analysis. Up to now, these two fields have remained rather separate because of fundamental differences in approach. It is shown that fusion distances are very good at discriminating different types of spatial point patterns. A detailed study on the power of the Monte Carlo test under the null hypothesis of Complete Spatial Randomness (the benchmark of spatial statistics) against chosen alternative models is also conducted. For instance, the test (based on the fusion distance) is very powerful for some arbitrary values of the parameters of the alternative. A new general approach is developed for analysing a given point pattern using several graphical techniques for exploratory data analysis and inference. The new strategy is applied to univariate and multivariate point patterns. A new extension of a popular strategy in spatial statistics, named the analysis of the local configuration, is also developed. This new extension uses the fusion distances, and analyses a localised neighbourhood of a given point of the point pattern. New spatial summary function and statistics, named the fusion distance function H(t), area statistic A, statistic S, and spatial Rg index, are introduced, and proven to be useful tools for identifying relevant features of spatial point patterns. In conclusion, the new methodology using the outputs of hierarchical clustering algorithms can be considered as an essential complement to the existing approaches in spatial statistics literature.

Franchising is a form of business arrangement which has been claimed to offer a high possibility of business success. The reason for its growing support may be due to the benefits franchising offers to the individuals (franchisees), the organisations (franchisors), the public and the whole economy. The notion that franchising offers a successful guarantee for business operations warrants investigation into what factors contribute to likely success. The characteristics of franchise firms have been identified as a significant factor in ensuring franchise success. In this study, franchise firms were classified into four groups using hierarchical cluster analysis: the beginners, the developers, the growers and the matures. The profiles were tested against reported performance measures, indicating that franchise firms in the growing group outperform firms in other groups. However, this is only one component which may influence a successful franchise development. Relationship management is a second, critical area in building a strong franchise network. Some significant relationship building factors are explored in this thesis. Control, influence strategy, franchisee information asymmetry and communication strategy were found to be significant factors which lead to franchise competitive advantage. The results show that control and coercive influence strategy negatively influence franchise outcomes, while noncoercive influence strategy, information asymmetry and communication strategy positively enhance the franchise relationship. The results of multiple regression analyses also indicate that the combined effects of these franchise strategies have a significant impact on franchise outcomes; namely, financial performance, franchise goal congruence and franchisee satisfaction. The findings suggest that franchise firms can adjust their strategies to enhance their business success. Control, influence, information and information strategies can be used to maintain and induce healthy franchise relationships within the franchise dyad. The study also significantly extends the existing understanding of franchise heterogeneity theories, key criteria of franchise business competitiveness, the unique business relationship between franchisor and franchisee and franchise factors contributing to franchise success in the current franchising literature.

Thesis (M.S.)--University of Nevada, Reno, 2007. / "May, 2007." Includes bibliographical references (leaves 62-64). Online version available on the World Wide Web. Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2007]. 1 microfilm reel ; 35 mm.

Thesis (Ph. D.)--Washington State University, December 2007. / Includes bibliographical references (p. 156-164).

Diss. (sammanfattning)--Linköping : Linköpings universitet, 2006. / Härtill 4 uppsatser.

Thesis(M.S.)--Auburn University, 2006. / Abstract. Vita. Includes bibliographic references (p.56-59).

Thesis (Ph. D.)--Carleton University, 2004. / Includes bibliographical references (p. 175-179). Also available in electronic format on the Internet.

Thesis (M. Sc.)--Carleton University, 2004. / Includes bibliographical references (p. 95-99). Also available in electronic format on the Internet.