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

A useful measure of quality of a global optimisation algorithm such as simulated annealing is the length of time it must be run to reach a global optimum within a certain accuracy. Such a performance measure assists in choosing and tuning algorithms. This thesis proposes an approach to obtaining such a measure through successive approximation of a generic stochastic global optimisation algorithm with a sequence of stochastic processes culminating in backtracking adaptive search. The overall approach is to approximate the progress of an optimisation algorithm with that of a model process, backtracking adaptive search. The known convergence rate of the model then provides an estimator of the unknown convergence rate of the original algorithm. Parameters specifying this model are chosen based on observation of the optimisation algorithm. The optimisation algorithm may first be approximated with a time-inhomogeneous Markovian process defined on the problem range. The distribution of the number of iterations to convergence for this averaged range process is shown to be identical with that of the original process. This process is itself approximated by a time-homogeneous Markov process in the range, the asymptotic averaged range process. This approximation is defined for all Markovian optimisation algorithms and a weak condition under which its convergence time closely matches that of the original algorithm is developed. The asymptotic averaged range process is of the same form as backtracking adaptive search, the final stage of approximation. Backtracking adaptive search is an optimisation algorithm which generalises pure adaptive search and hesitant adaptive search. In this thesis the distribution of the number of iterations for which the algorithm runs in order to reach a sufficiently extreme objective function level is derived. Several examples of backtracking adaptive search on finite problems are also presented, including special cases that have received attention in the literature. Computational results of the entire approximation framework are reported for several examples. The method can be applied to any optimisation algorithm to obtain an estimate of the time required to obtain solutions of a certain quality. Directions for further work in order to improve the accuracy of such estimates are also indicated.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 205-208). Also available via the Internet.

Thesis (Ph. D.)--University of California, San Diego, 2004. / Vita. Includes bibliographical references (leaves 144-154).

Thesis (M.S.)--Rochester Institute of Technology, 2005. / Typescript. Includes bibliographical references (p. 153-157).

Thesis (Ph. D.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains vi, 109 p. : ill., maps. Includes abstract. Includes bibliographical references (p. 103-109).

Thesis (Ph.D.)--McMaster University, 2001 / Includes bibliographical references (leaves 144-156). Also available via World Wide Web.

Thesis (Ph.D.) - University of Queensland, 2006. / Includes bibliography.

R. Paul Wiley and Robert R. Tenney. / Caption title. / Bibliography: leaf [6]. / Office of Naval Research grant ONR/N00014-77-C-0532 (NR 041-519) ONR/N00014-84-K-0519 (NR 649-003)

Understanding the energy input and emissions resulting from the development of biofuels is important to quantify the overall benefit of the biofuel. As part of the On-Farm Biomass Processing project, a life cycle assessment (LCA) was conducted on the process to harvest and transport agricultural crop residues ready for processing into biofuel. A Microsoft Excel model was developed that inventories the entire life cycle of the process, including incorporation of stochastic analysis within the model. The LCA results of the agricultural equipment manufacture are presented, along with the results of each step of the process, including fertilizer addition, single pass harvest, double pass harvest, and transport from the field to processing facility. Various methods of analyzing co-products are also presented for the single pass harvesting step, in which comparisons between market based, mass based and process-purpose based allocation methods are reviewed. The process-purpose based method of fuel consumption difference between combine operation in conventional harvest versus single pass harvest is determined to be the most realistic of the process. A detailed comparison of the energy and emission differences between single pass and double pass harvesting is given, along with the total LCA results of harvesting and transporting the biomass.