Les problèmes d'ordonnancement peuvent être rencontrés dans plusieurs situations de la vie courante. Organiser des activités quotidiennes, planifier un itinéraire de voyage sont autant d'exemples de petits problèmes d'optimisation que nous tentons de résoudre tous les jours sans nous en rendre compte. Mais quand ces problèmes prennent des proportions plus grandes, il devient difficile au cerveau humain de gérer tous ces paramètres et le recours à une solution informatique s'impose. Les problèmes d'ordonnancement en contexte industriel sont nombreux et celui qui retient particulièrement notre attention dans le cadre de ce mémoire est le problème d'ordonnancement de commandes sur machine unique avec temps de réglages dépendant de la séquence. Ce problème fait partie de la classe de problèmes NP-Difficiles. Etant donnée sa complexité, ce problème ne peut être résolu par une méthode exacte. Les métaheuristiques représentent ainsi une bonne alternative pour obtenir des solutions de bonne qualité dans des délais très courts. Les algorithmes génétiques, qui font partie des algorithmes évolutionnaires, sont utilisés dans ce travail pour résoudre ce problème d'ordonnancement. La prolifération des architectures parallèles a ouvert la voie à un nouvel éventail d'approches pour optimiser les algorithmes et plus spécialement les métaheuristiques. Ce mémoire propose une stratégie de parallélisation de l'algorithme génétique pour en étudier les bénéfices. Le premier algorithme génétique proposé est implémenté sur le modèle d'un algorithme de la littérature. Cet algorithme ne s'est pas avéré performant pour toute la série de problèmes test et, pour cette raison, des modifications de paramètres ont été rendues nécessaires. Ces modifications ont donné naissance à une deuxième version séquentielle dont les résultats se sont avérés satisfaisants. Une troisième version a ensuite été implémentée avec une optique d'exécution parallèle selon un modèle en îlot et une topologie en anneau unidirectionnel. Un plan d'expérience a ensuite été mis au point selon plusieurs variables et vise à identifier les meilleures configurations de l'algorithme tant sur le plan de la qualité des résultats que sur le plan de l'accélération. Les résultats obtenus dans ce mémoire montrent que l'introduction de la parallélisation dans un algorithme génétique est bénéfique à ce dernier tant sur le plan qualité des résultats que sur le plan accélération. Dans un premier temps, la version sans communications n'a pas amélioré une grande partie des problèmes mais a pu atteindre des accélérations linéaires. Par la suite, l'introduction des échanges a nettement influé sur la qualité des résultats. En effet, en adoptant une stratégie de division de la taille de la population par le nombre de processeurs, l'algorithme génétique parallèle parvient à donner des résultats équivalents voire meilleurs que la version séquentielle, et ceci pour plusieurs fréquences d'échanges entre les populations.

Metric spaces, which generalise the properties of commonly-encountered physical and abstract spaces into a mathematical framework, frequently occur in computer science applications. Three major kinds of questions about metric spaces are considered here: the intrinsic dimensionality of a distribution, the maximum number of distance permutations, and the difficulty of reverse similarity search. Intrinsic dimensionality measures the tendency for points to be equidistant, which is diagnostic of high-dimensional spaces. Distance permutations describe the order in which a set of fixed sites appears while moving away from a chosen point; the number of distinct permutations determines the amount of storage space required by some kinds of indexing data structure. Reverse similarity search problems are constraint satisfaction problems derived from distance-based index structures. Their difficulty reveals details of the structure of the space. Theoretical and experimental results are given for these three questions in a wide range of metric spaces, with commentary on the consequences for computer science applications and additional related results where appropriate.

This thesis will address several problems in discrete optimization. These problems are considered hard to solve. However, good approximation algorithms for these problems may be helpful in approximating problems in computational biology and computer science. Given an undirected graph G=(V,E) and a family of subsets of vertices S, the minimum crossing spanning tree is a spanning tree where the maximum number of edges crossing any single set in S is minimized, where an edge crosses a set if it has exactly one endpoint in the set. This thesis will present two algorithms for special cases of minimum crossing spanning trees. The first algorithm is for the case where the sets of S are pairwise disjoint. It gives a spanning tree with the maximum crossing of a set being 2OPT+2, where OPT is the maximum crossing for a minimum crossing spanning tree. The second algorithm is for the case where the sets of S form a laminar family. Let b_i be a bound for each S_i in S. If there exists a spanning tree where each set S_i is crossed at most b_i times, the algorithm finds a spanning tree where each set S_i is crossed O(b_i log n) times. From this algorithm, one can get a spanning tree with maximum crossing O(OPT log n). Given an undirected graph G=(V,E), and a family of subsets of vertices S, the minimum crossing perfect matching is a perfect matching where the maximum number of edges crossing any set in S is minimized. A proof will be presented showing that finding a minimum crossing perfect matching is NP-hard, even when the graph is bipartite and the sets of S are pairwise disjoint.

Metric spaces, which generalise the properties of commonly-encountered physical and abstract spaces into a mathematical framework, frequently occur in computer science applications. Three major kinds of questions about metric spaces are considered here: the intrinsic dimensionality of a distribution, the maximum number of distance permutations, and the difficulty of reverse similarity search. Intrinsic dimensionality measures the tendency for points to be equidistant, which is diagnostic of high-dimensional spaces. Distance permutations describe the order in which a set of fixed sites appears while moving away from a chosen point; the number of distinct permutations determines the amount of storage space required by some kinds of indexing data structure. Reverse similarity search problems are constraint satisfaction problems derived from distance-based index structures. Their difficulty reveals details of the structure of the space. Theoretical and experimental results are given for these three questions in a wide range of metric spaces, with commentary on the consequences for computer science applications and additional related results where appropriate.

NP-hard problems have numerous applications in various fields such as networks, computer systems, circuit design, etc. However, no efficient algorithms have been found for NP-hard problems. It has been commonly believed that no efficient algorithms for NP-hard problems exist, i.e., that P6=NP. Recently, it has been observed that there are parameters much smaller than input sizes in many instances of NP-hard problems in the real world. In the last twenty years, researchers have been interested in developing efficient algorithms, i.e., fixed-parameter tractable algorithms, for those instances with small parameters. Fixed-parameter tractable algorithms can practically find exact solutions to problem instances with small parameters, though those problems are considered intractable in traditional computational theory. In this dissertation, we propose a new approach of algorithm design and analysis: discovering better measures for problems. In particular we use two measures instead of the traditional single measure?input size to design algorithms and analyze their time complexity. For several classical NP-hard problems, we present improved algorithms designed and analyzed with this new approach, First we show that the new approach is extremely powerful for designing fixedparameter tractable algorithms by presenting improved fixed-parameter tractable algorithms for the 3D-matching and 3D-packing problems, the multiway cut problem, the feedback vertex set problems on both directed and undirected graph and the max-leaf problems on both directed and undirected graphs. Most of our algorithms are practical for problem instances with small parameters. Moreover, we show that this new approach is also good for designing exact algorithms (with no parameters) for NP-hard problems by presenting an improved exact algorithm for the well-known satisfiability problem. Our results demonstrate the power of this new approach to algorithm design and analysis for NP-hard problems. In the end, we discuss possible future directions on this new approach and other approaches to algorithm design and analysis.

No description available.

In this thesis, we proposed a simple yet novel multicast scheduling scheme for IEEE 802.16e wireless metropolitan area networks. Specifically, we want to solve the problem that how the base station schedules data messages in a multicast superframe such that mobile stations can receive their required multicast data and the total awake time of mobile stations is minimal. We first prove that this problem is NP-complete, and then propose a greedy k-approximation algorithm, named G-EEMS, whose running time is , where n is the total number of multicast data messages and k is the size of MBS (multicast and broadcast service) zone in a frame. Simulation results show that, in terms of energy throughput, G-EEMS significantly outperforms the existing scheme, called SMBC-D.

published_or_final_version / abstract / toc / Mathematics / Master / Master of Philosophy

Nurse practitioners (NPs) have been undergoing a rapid transition in their entry-level degree, from Master of Science in Nursing (MSN) to Doctor of Nursing Practice (DNP). At this time, it is important to establish research evidence on the effects of doctoral education on NP practice. Therefore, a qualitative study of practicing NPs that have returned for the DNP degree was conducted. The purpose was to describe NPs' perceptions of their DNP education, and particularly its influence on their professionalism and patient care. A literature review and evidence synthesis process showed that the available body of research provides little insight into the question of how DNP education affects NP practice; therefore, qualitative description methodology was used to describe this phenomenon. The research questions that guided the study were: 1) What changes do practicing NPs describe about their clinical practice after the experience of completing a DNP?; and, 2) What are the NPs' perceptions of and concerns about the influences of their DNP educational experience on their clinical practice? Two published models and the DNP Essentials (AACN, 2006) informed and guided the data collection and analysis process. Purposive sampling and analyses continued concurrently until data saturation was achieved. Ten DNP prepared NPs were interviewed, and there was wide variation in the sample. The overarching theme Growth into DNP Practice summarizes the participants' perceptions of the changes that have occurred as a result of their DNP educational experience. Four major themes that support the overarching theme are: (a) Broader Thinking and Work Focus; (b) New Knowledge and Interests; (c) New Opportunities; and, (d) "Doctor" Title an Asset. Conceptual categories under each major theme are described. Participants were overwhelmingly positive about the influences of their DNP education on their practice, but the role of the DNP graduate in knowledge translation has yet to be fully operationalized.

Given graphs G,H, is it possible to find a matching which, when deleted from G, destroys all copies of H? The answer is obvious for some inputs—notably, when G is a large complete graph the answer is “no”—but in general this can be a very difficult question. In this thesis, we study this decision problem when H is a fixed tree or cycle; our aim is to identify those H for which it can be solved efficiently. The H-transverse matching problem, TM(H) for short, asks whether an input graph admits a matching M such that no subgraph of G − M is isomorphic to H. The main goal of this thesis is the following dichotomy. When H is a triangle or one of a few small-diameter trees, there is a polynomial-time algorithm to find an H-transverse matching if one exists. However, TM(H) is NP-complete when H is any longer cycle or a tree of diameter ≥ 4. In addition, we study the restriction of these problems to structured graph classes. / Graduate