Mobile cloud computing is a way of delegating complex algorithms from a mobile device to the cloud to complete the tasks quickly and save energy on the mobile device. However, the cloud may not be available or suitable for helping all the time. For example, in a battlefield scenario, the cloud may not be reachable. This work considers neighbouring devices as alternatives to the cloud for offloading computation and presents three key contributions, namely a comprehensive investigation of the trade-off between computation and communication, Multi-Objective Optimisation based approach to offloading, and Queuing Theory based algorithms that present the benefits of offloading to neighbours. Initially, the states of neighbouring devices are considered to be known and the decision of computation offloading is proposed as a multi-objective optimisation problem. Novel Pareto optimal solutions are proposed. The results on a simulated dataset show up to 30% increment in performance even when cloud computing is not available. However, information about the environment is seldom known completely. In Chapter 5, a realistic environment is considered such as delayed node state information and partially connected sensors. The network of sensors is modelled as a network of queues (Open Jackson network). The offloading problem is posed as minimum cost problem and solved using Linear solvers. In addition to the simulated dataset, the proposed solution is tested on a real computer vision dataset. The experiments on the random waypoint dataset showed up to 33% boost on performance whereas in the real dataset, exploiting the temporal and spatial distribution of the targets, a significantly higher increment in performance is achieved.

Due to an increasing awareness of the importance of sustainable energy use, multi-objective optimization problems for upper-level energy systems are continually being developed and improved. This paper focuses on the modeling and optimization of the Helsinki district heating system and establishing the basis for modeling the Finnish power network. The optimization of the district heating system is conducted for a twenty four hour winter demand period. Partial load behavior of the generators is included by introducing non-linear functions for costs, emissions, and the exergetic efficiency. A fuel cost sensitivity analysis is conducted on the system by considering ten combinations of fuel costs based on high, medium, and low prices for each fuel. The solution sets, called Pareto fronts, are evaluated by post-processing techniques in order to determine the best solution from the optimal set. Because units between some of objective functions are non-commensurable, objective values are normalized and weighted. The results indicate that for today\'s fuel prices the best solution includes a dominating usage of natural gas technologies, while if the price of natural gas is higher than other fuels, natural gas technologies are often not included in the best solution. All of the necessary costs, emissions, and operating information is provided for the the Finnish power network in order to employ a multi-objective optimization on the system. / Master of Science

This study is conducted with the aims to develop a new ranking method for multi-criteria decision making problem with conflicting criteria. Such a problem has a set of Pareto solutions, where the act of improving a value of one solution will result in depreciating some of the others. Thus, in this type of problem, there is no unique solution. However, out of many available options, the Decision Maker eventually has to choose only one solution. With this problem as the motivation, the current study develops a compromise ranking algorithm, namely a trade-off ranking method. The trade-off ranking method able to give a trade-off solution with the least compromise compared to other choices as the best solution. The properties of the algorithm are studied in the thesis on several test cases. The proposed method is compared against several multi-criteria decision making methods with ranking based on the distance measure, which are the TOPSIS, relative distance and VIKOR. The sensitivity analysis and uncertainty test are carried out to examine the methods robustness. A critical criteria analysis is also done to test for the most critical criterion in a multi-criteria problem. The decision making method is considered further in a fuzzy environment problem where the fuzzy trade-off ranking is developed and compared against existing fuzzy decision making methods.

Made available in DSpace on 2014-06-11T19:30:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-11-23Bitstream added on 2014-06-13T21:01:26Z : No. of bitstreams: 1 ochoapizzali_lf_dr_ilha.pdf: 1694440 bytes, checksum: e159d13557d3d0a89139b7565f849244 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Alban / Fundação de Ensino Pesquisa e Extensão de Ilha Solteira (FEPISA) / Neste trabalho, é apresentada uma análise em regime permanente que considera a avaliação de impactos técnicos tais como perdas elétricas, queda de tensão e níveis de curto-circuito, entre outros; utilizando dados de demanda e geração variáveis no tempo ao longo de um horizonte determinado. O objetivo é encontrar um conjunto de arranjos da GD (configurações) que levem ao melhor desempenho da rede de distribuição analisada, minimizando ou maximizando cada aspecto técnico segundo o interesse da empresa de distribuição. Dada a natureza combinatória deste problema, que requer uma ferramenta de otimização capaz de manipular múltiplos objetivos, os impactos técnicos serão avaliados simultaneamente utilizando uma metodologia baseada no conceito do Non-dominated Sorting Genetic Algorithm (NSGA), conduzindo a soluções mais reais e diversificadas para a tomada de decisões, conhecidas como soluções ótimas de Pareto. / In this work a steady-state analysis considering the assessment of technical impacts such as losses, voltage drop and short-circuit levels, among others; utilizing time-variant loads and generation within a specified horizon. The objective is to find a set of configurations that lead to the best performance of the distribution network under analysis, minimizing or maximizing each technical aspect according to the utility's concerns. Given the combinatorial nature of this problem, which requires an optimization tool able to handle multiple objectives, technical impacts will be assessed simultaneously through a methodology based on the non-dominated sorting genetic algorithm (NSGA). This approach leads to a more realistic and diversified set of solutions for taking decisions, known as Pareto-optimal solutions.

Orientador: Antonio Padilha Feltrin / Banca: Rubén Augusto Romero Lázaro / Banca: Dionízio Paschoareli Júnior / Banca: Gareth Harrison / Banca: Carmen Lucia Tancredo Borges / Resumo: Neste trabalho, é apresentada uma análise em regime permanente que considera a avaliação de impactos técnicos tais como perdas elétricas, queda de tensão e níveis de curto-circuito, entre outros; utilizando dados de demanda e geração variáveis no tempo ao longo de um horizonte determinado. O objetivo é encontrar um conjunto de arranjos da GD (configurações) que levem ao melhor desempenho da rede de distribuição analisada, minimizando ou maximizando cada aspecto técnico segundo o interesse da empresa de distribuição. Dada a natureza combinatória deste problema, que requer uma ferramenta de otimização capaz de manipular múltiplos objetivos, os impactos técnicos serão avaliados simultaneamente utilizando uma metodologia baseada no conceito do Non-dominated Sorting Genetic Algorithm (NSGA), conduzindo a soluções mais reais e diversificadas para a tomada de decisões, conhecidas como soluções ótimas de Pareto. / Abstract: In this work a steady-state analysis considering the assessment of technical impacts such as losses, voltage drop and short-circuit levels, among others; utilizing time-variant loads and generation within a specified horizon. The objective is to find a set of configurations that lead to the best performance of the distribution network under analysis, minimizing or maximizing each technical aspect according to the utility's concerns. Given the combinatorial nature of this problem, which requires an optimization tool able to handle multiple objectives, technical impacts will be assessed simultaneously through a methodology based on the non-dominated sorting genetic algorithm (NSGA). This approach leads to a more realistic and diversified set of solutions for taking decisions, known as Pareto-optimal solutions. / Doutor

Face aux exigences concurrentielles et économiques actuelles dans le secteur automobile, l'emboutissage a l'avantage, comme étant un procédé de mise en forme par grande déformation, de produire, en grandes cadences, des pièces de meilleure qualité géométrique par rapport aux autres procédés de fabrication mécanique. Cependant, il présente des difficultés de mise en œuvre, cette dernière s'effectue généralement dans les entreprises par la méthode classique d'essai-erreur, une méthode longue et très coûteuse. Dans la recherche, le recours à la simulation du procédé par la méthode des éléments finis est une alternative. Elle est actuellement une des innovations technologiques qui cherche à réduire le coût de production et de réalisation des outillages et facilite l'analyse et la résolution des problèmes liés au procédé. Dans le cadre de cette thèse, l'objectif est de prédire et de prévenir, particulièrement, le retour élastique et la rupture. Ces deux problèmes sont les plus répandus en emboutissage et présentent une difficulté en optimisation puisqu'ils sont antagonistes. Une pièce mise en forme par emboutissage à l'aide d'un poinçon sous forme de croix a fait l'objet de l'étude. Nous avons envisagé, d'abord, d'analyser la sensibilité des deux phénomènes concernés par rapport à deux paramètres caractéristiques du procédé d'emboutissage (l'épaisseur du flan initial et de la vitesse du poinçon), puis par rapport à quatre (l'épaisseur du flan initial, de la vitesse du poinçon, l'effort du serre flan et le coefficient du frottement) et finalement par rapport à la forme du contour du flan. Le recours à des méta-modèles pour optimiser les deux critères était nécessaire. / The processing of sheet metal forming is of vital importance to a large range of industries as production of car bodies, cans, appliances, etc. It generates complex and precise parts. Although, it is an involved technology combining elastic-plastic bending and stretch deformation of the workpiece. These deformations can lead to undesirable problems in the desired shape and performance of the stamped. To perform a successful stamping process and avoid shape deviations such as springback and failure defects, process variables should be optimized.In the present work, the objective is the prediction and the prevention of, especially, springback and failure. These two phenomena are the most common problems in stamping process that present much difficulties in optimization since they are two conflicting objectives. The forming test studied in this thesis concern the stamping of an industrial workpiece stamped with a cross punch. To solve this optimization problem, the approach chosen was based on the hybridization of an heuristic and a direct descent method. This hybridization is designed to take advantage from both disciplines, stochastic and deterministic, in order to improve the robustness and the efficiency of the hybrid algorithm. For the multi-objective problem, we adopt methods based on the identification of Pareto front. To have a compromise between the convergence towards the front and the manner in which the solutions are distributed, we choose two appropriate methods. This methods have the capability to capture the Pareto front and have the advantage of generating a set of Pareto-optimal solutions uniformly spaced. The last property can be of important and practical.

Getinge AB is a global medical technology company. This master’s thesis is based on the outflow of capital equipments from Getinge’s factory in Växjö to four different sales and service units. The purpose of this thesis is to give Getinge a deeper insight of why the customers and the own organization do not know when they can expect their products. This makes most requests urgent and thus prohibits them from using the best environmental and cost efficient modes of transportation. Two sub-problems have been created in order to investigate this. Sub-problem 1 originates from an organizational perspective. The aim of this problem is to examine the possibilities to achieve less urgent transportations by improving the communication between sales and service units, factories and logistics services. This is evaluated based on semi-structured interviews containing both qualitative and quantitative questions with employees rep- resenting the different functions at the company. It appeared that different phrases, explaining the same thing, were used internally leading to confu- sion. Further, the different functions have harmonized follow-up sessions but do not share the information between each other. The resulting information vacuum creates trust issues and unnecessary time margins and buffers. Sub-problem 2 concerns the trade-off between the economic and environmen- tal impacts in relation to the Greenhouse Gas Protocol Scope 3. This trade- off is evaluated by a multi-objective optimization model, where emissions are priced based on the EU ETS market valuation. Current research argues that the choice of transportation mode is the simplest emissions abatement option in terms of implementation. This study indicates that it is possible for Getinge, in the short-term, to decrease costs and emissions by just chang- ing between current transportation modes. However, a long-term strategy should include evaluation of consolidations, alternative fuels and electrified vehicles since the cost of decreasing one kilogram of emissions by changing between current transportation modes will increase. Finally, increased transparency and communication between sales and ser- vice units, factory and logistics services could be achieved via a one point of contact solution. This could avoid unnecessary time margins and buffers and hence open up the possibility of better over all lead time utilization. This could make it easier to use more environmental friendly transportation modes and thus lower emissions and costs, while still satisfying the customers.

The aim of this master thesis is to study algorithms for automatically tuning antenna parameters to improve the performance of the radio access part of a telecommunication network and user experience. There are four dierent optimization algorithms, Stepwise Minimization Algorithm, Random Search Algorithm, Modied Steepest Descent Algorithm and Multi-Objective Genetic Algorithm to be applied to a model of a radio access network. The performances of all algorithms will be evaluated in this thesis. Moreover, a graphical user interface which is developed to facilitate the antenna tuning simulations will also be presented in the appendix of the report.