Energy efficiency is of the utmost importance in modern high-performance embedded processor design. As the number of transistors on a chip continues to increase each year, and processor logic becomes ever more complex, the dynamic switching power cost of running such processors increases. The continual progression in fabrication processes brings a reduction in the feature size of the transistor structures on chips with each new technology generation. This reduction in size increases the significance of leakage power (a constant drain that is proportional to the number of transistors). Particularly in embedded devices, the proportion of an electronic product’s power budget accounted for by the CPU is significant (often as much as 50%). Dynamic branch prediction is a hardware mechanism used to forecast the direction, and target address, of branch instructions. This is essential to high performance pipelined and superscalar processors, where the direction and target of branches is not computed until several stages into the pipeline. Accurate branch prediction also acts to increase energy efficiency by reducing the amount of time spent executing mis-speculated instructions. ‘Stalling’ is no longer a sensible option when the significance of static power dissipation is considered. Dynamic branch prediction logic typically accounts for over 10% of a processor’s global power dissipation, making it an obvious target for energy optimisation. Previous approaches at increasing the energy efficiency of dynamic branch prediction logic has focused on either fully dynamic or fully static techniques. Dynamic techniques include the introduction of a new cache-like structure that can decide whether branch prediction logic should be accessed for a given branch, and static techniques tend to focus on scheduling around branch instructions so that a prediction is not needed (or the branch is removed completely). This dissertation explores a method of combining static techniques and profiling information with simple hardware support in order to reduce the number of accesses made to a branch predictor. The local delay region is used on unconditional absolute branches to avoid prediction, and, for most other branches, Adaptive Branch Bias Measurement (through profiling) is used to assign a static prediction that is as accurate as a dynamic prediction for that branch. This information is represented as two hint-bits in branch instructions, and then interpreted by simple hardware logic that bypasses both the lookup and update phases for appropriate branches. The global processor power saving that can be achieved by this Combined Algorithm is around 6% on the experimental architectures shown. These architectures are based upon real contemporary embedded architecture specifications. The introduction of the Combined Algorithm also significantly reduces the execution time of programs on Multiple Instruction Issue processors. This is attributed to the increase achieved in global prediction accuracy.

This thesis describes a stochastic ship routing problem with inventory management. The problem involves finding a set of least costs routes for a fleet of ships transporting a single commodity when the demand for the commodity is uncertain. Storage at consumption and supply ports is limited and inventory levels are monitored in the model. Consumer demands are at a constant rate within each time period in the deterministic problem, and in the stochastic problem, the demand rate for a period is not known until the beginning of that period. The demand situation in each time period can be described by a scenario tree with corresponding probabilities. Several possible solution approaches for solving the problem are studied in the thesis. This problem can be formulated as a mixed integer programming (MIP) model. However solving the problem this way is very time consuming even for a deterministic problem with small problem size. In order to solve the stochastic problem, we develop a decomposition formulation and solve it using a Branch and Price framework. A master problem (set partitioning with extra inventory constraints) is built, and the subproblems, one for each ship, involve solving stochastic dynamic programming problems to generate columns for the master problem. Each column corresponds to one possible tree of schedules for one ship giving the schedule for the ship for all demand scenarios. In each branch-and-bound node, the node problem is solved by iterating between the master problem and the subproblems. Dual variables can be obtained solving the master problem and are used in the subproblems to generate the most promising columns for the master problem. Computational results are given showing that medium sized problems can be solved successfully. Several extensions to the original model are developed, including a variable speed model, a diverting model, and a model which allows ships to do extra tasks in return for a bonus. Possible solution approaches for solving the variable speed and the diverting model are presented and computational results are given.

Ces dernières années, la phase de roulage au sol des avions a été mise en avant dans l'étude des retards aériens sur les grands aéroports. Cependant, le lien entre cette phase et l'optimisation des séquences d'avions sur les pistes reste encore peu étudié. L'objectif de réaliser des séquences optimales sur les pistes doit pourtant permettre de mieux gérer le trafic au sol, pour respecter les créneaux de décollage imposés tout en réduisant les retards des avions : dans cette thèse, un algorithme de calcul de séquences optimales est mis en place et intégré à la gestion du trafic au sol, modélisée comme un problème de résolution de conflits entre avions. Deux méthodes d'optimisation sont alors comparées : une méthode déterministe (utilisant un algorithme de type branch and bound) et une méthode stochastique (utilisant un algorithme génétique). Chacune des deux méthodes pouvant fonctionner avec et sans considération des séquences optimales sur les pistes. Les simulations effectuées montrent qu'une réduction significative des retards peut être espérée lorsque les séquences sont optimisées et anticipées. La méthode stochastique trouve de meilleures solutions, notamment en ce qui concerne la gestion des arrivées, mais la méthode déterministe reste intéressante, grâce à son temps de calcul bien plus rapide. / In the last few years, many studies concerning air traffic delays have focused on ground traffic management at busy airports. However, the link between the aircraft taxiing stage and runway scheduling optimisation is still rarely considered. Performing optimal aircraft sequences on runways should allow us to enhance the taxiing stage, while applying calculated take-off slots and reducing globally the aircraft mean delay. In this thesis, an algorithm is first defined to compute optimal aircraft schedules on runways. It is then integrated into the ground traffic management process, modeled as a conflict resolution problem between aircraft. A deterministic method (using a branch and bound algorithm) and a stochastic method (using a genetic algorithm) are both used to try and solve this problem. Each of these methods can work with and without the consideration of optimal runway scheduling. The simulations carried out show that the anticipation of the optimal runway schedules can yield a significant delay reduction for airport ground traffic. The stochastic method provides the best solutions, especially for arriving aircraft, while the deterministic method remains a considerable option because of its very fast running time.

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

Cette thèse développe les deux approches locales et globales basées sur la programmation DC et DCA pour l'optimisation combinatoire en variables mixtes 0-1 et leurs applications à la résolution de nombreux problèmes en planification opérationnelle. Plus particulièrement, cette thèse adresse à: l'amélioration de l'algorithme d'approximation extérieure basée sur DCA (appelé DCACUT) introduit par Nguyen V.V. et Le Thi pour la programmation linéaire en variables mixtes 0-1, les combinaisons des algorithmes globaux et DCA et l'étude numérique comparative de ces approches pour la programmation linéaire en variables mixtes 0-1, l'utilisation de DCA à la résolution de la programmation DC en variables mixtes 0-1 en utilisant la pénalité exacte, la mise en œuvre des algorithmes développés à la résolution des problèmes de grande taille en planification opérationnelle comme les problèmes dans le réseau de télécommunication sans fils, les problèmes d’ordonnancement ainsi que le problème d'affectation de tâches des véhicules aériens non pilotés ou bien le problème des tournées de véhicules dans une chaîne d'approvisionnement / This thesis develops two local and global approaches based on DC programming and DCA for mixed 0-1 combinatorial optimization and their applications to many problems in operational planning. More particularly, this thesis consists of: the improvement of the outer approximation algorithm based on DCA (called DCACUT) introduced by Nguyen V.V and Le Thi for mixed 0-1 linear programming, the combinations of global algorithms and DCA and the comparative numerical study of these approaches for mixed 0-1 linear programming, the use of DCA for solving mixed 0-1 programming via an exact penalty technique, the implementation of the algorithms developed for solving large scale problems in operational planning: two problems in wireless telecommunication network, two scheduling problems, an UAV task assignment problem and an inventory routing problem in supply chains

There is a wealth of literature that provides faculty with effective teaching practices for educating returning adult students. Most of the literature focuses on online classes/institutions, community colleges, or the main campuses of four-year institutions. There is little to no literature, which explains what is actually taking place in the classroom on satellite campuses (also known as branch campuses, which consist almost entirely of returning adult students) of four-year institutions. With the projection of a continued increase in the number of returning adult students, it was important that this problem was addressed. Through in-depth interviews, the following was explored with seventeen Behavioral Science faculty: the unique challenges faced by faculty on satellite campuses, perceptions of returning adult students, whether faculty changed their teaching approaches with returning adult students, the teaching approaches utilized and if those teaching approaches were aligned with adult learning principles. The findings indicated that the satellite campus environment is unique as far as the student population and skills required to navigate the various difficulties one might encounter as a faculty member. Faculty recognize the differences between traditional and returning adult students. They are conscious of the challenges that their returning adult students face and modify their teaching approaches in order to meet their needs. And regardless of how faculty learn how to adapt to the needs of the returning adult students and no matter what principles guide their thinking, faculty try to meet their students’ needs. Additionally, whether faculty utilize formal course evaluations or gather feedback informally, feedback from students in any form hold value and can assist faculty in determining how to best modify their teaching approaches.

THE ABSTRACT Topic: Problems of activity of foreign foundations and endowment funds in Czech Republic. This thesis is primary aimed to clarify problems regarding to activity of foreign foundation and endowment funds in Czech Republic. The purpose of this work is to explain related problems and provide possible solutions for such issues. In the early chapters, the author familiarizes readers with history of foundations and endowment funds in Czech Republic and explains the terminology and meaning of foundations and endowment funds as a legal person. Secondly, the author analyses legal situation in Czech law and presents various options for activity of foreign foundations and endowment funds in Czech Republic. Furthermore, the author deals with the description of the solution consisting in the establishment of branch in Czech Republic. This part of the thesis is the largest content because the author provides possible solutions for various problems arising from establishing of branch. The thesis also describes foreign legislation relating to foundations and endowment funds. And finally, the author states possible changes in Czech law, which will lead to the better foundation's legislation in Czech Republic and attaches drafts of documents usable for the thesis topic.

L'algorithme Branch-and-Bound (B&B) est une méthode de recherche arborescente fréquemment utilisé pour la résolution exacte de problèmes d'optimisation combinatoire (POC). Néanmoins, seules des petites instances peuvent être effectivement résolues sur une machine séquentielle, le nombre de sous-problèmes à évaluer étant souvent très grand. Visant la resolution de POC de grande taille, nous réexaminons la conception et l'implémentation d'algorithmes B&B massivement parallèles sur de larges plateformes hétérogènes de calcul, intégrant des processeurs multi-coeurs, many-cores et et processeurs graphiques (GPUs). Pour une représentation compacte en mémoire des sous-problèmes une structure de données originale (IVM), dédiée aux problèmes de permutation est utilisée. En raison de la forte irrégularité de l'arbre de recherche, l'équilibrage de charge dynamique entre processus d'exploration parallèles occupe une place centrale dans cette thèse. Basés sur un encodage compact de l'espace de recherche sous forme d'intervalles, des stratégies de vol de tâches sont proposées pour processeurs multi-core et GPU, ainsi une approche hiérarchique pour l'équilibrage de charge dans les systèmes multi-GPU et multi-CPU à mémoire distribuée. Trois problèmes d'optimisation définis sur l'ensemble des permutations, le problème d'ordonnancement Flow-Shop (FSP), d'affectation quadratique (QAP) et le problème des n-dames sont utilisés comme cas d'étude. La resolution en 9 heures d'une instance du FSP dont le temps de résolution séquentiel est estimé à 22 ans demontre la capacité de passage à l'échelle des algorithmes proposés sur une grappe de calcul composé de 36 GPUs. / Branch-and-Bound (B&B) is a frequently used tree-search exploratory method for the exact resolution of combinatorial optimization problems (COPs). However, in practice, only small problem instances can be solved on a sequential computer, as B&B generates often generates a huge amount of subproblems to be evaluated. In order to solve large COPs, we revisit the design and implementation of massively parallel B&B on top of large heterogeneous clusters, integrating multi-core CPUs, many-core processors and GPUs. For the efficient storage and management of subproblems an original data structure (IVM) dedicated to permutation problems is used. Because of the highly irregular and unpredictable shape of the B&B tree, dynamic load balancing between parallel exploration processes is one of the main issues addressed in this thesis. Based on a compact encoding of the search space in the form of intervals, work stealing strategies for multi-core and GPU are proposed, as well as hierarchical approaches for load balancing in distributed memory multi-CPU/multi-GPU systems. Three permutation problems, the Flowshop Scheduling Problem (FSP), the Quadratic Assignment Problem (QAP) and the n-Queens puzzle problem are used as test-cases. The resolution, in 9 hours, of a FSP instance with an estimated sequential execution time of 22 years demonstrates the scalability of the proposed algorithms on a cluster composed of 36 GPUs.

Previous research about undergraduate students with nontraditional college experiences has focused primarily on students' demographic characteristics, their deficits compared to residential students, and their risk for attrition. This case study conducted at a university branch campus in the Midwest examined undergraduate college experiences at a campus where nontraditional college experiences are typical. Case study findings revealed that college students were defined by their rich and detailed biography of prior life experiences, successes and challenges for student transition, and experiences with academic and social engagement. Findings also revealed students' strong sense of motivation and ability to thrive and succeed in college.

This study addressed a need to identify the effectiveness of in-branch, employee administered training programs. A comparison of various training methods and a ranking of the effectiveness of those methodologies would enable managers to make informed training design and purchase decisions. The purpose of this study was to determine the differences between four training methods as evaluated by post-training assessment scores. This study also sought to determine the following: 1) did all four methods significantly impact short term material recall?, and 2) did gender impact training effectiveness? The training methods studied were: video; study guide; video plus study guide; video and study guide plus reinforcement meeting. A control group was used to obtain a pre-training baseline. One hundred eighty two employees who worked at one of twenty randomly selected Portland, Oregon branches of a large, regional bank participated in the study. Each branch utilized one of the five randomly assigned methods in a scheduled staff meeting. After completing the training, each participant completed a post training assessment testing material recall. The control group completed the assessment without participating in training. Analysis of Variance tests were used to determine if significant differences existed between: 1) the mean scores of the control group and the training methods, 2) the mean scores of the training methods themselves, and 3) the mean scores of males and females. A significant difference was found at the .05 level between the mean scores of the control group and every training method except video. There were no significant differences between the mean scores of the four training methods. There was not a significant difference between the mean scores of males and females. There was also no significant difference in method effectiveness based on gender.