Os investimentos no setor de água potável no Brasil, apesar de significativos, ainda não apresentam os resultados esperados na melhoria da saúde e da qualidade de vida da população, especialmente nas pequenas comunidades do país. A aplicação de recursos continuará limitada enquanto não forem fortalecidos os aspectos técnicos, econômicos, institucionais, ambientais, sociais e culturais que permitam a seleção de obras sanitárias eficientes e sustentáveis. Neste contexto, foi desenvolvido um modelo conceitual de seleção de tecnologias de tratamento de água constituído por 17 sub-níveis que progressivamente \"filtram\" as opções tecnológicas aplicáveis em comunidades brasileiras inferiores a 20.000 habitantes. Os aspectos envolvidos no modelo se relacionam: i) ao risco presente na fonte de abastecimento superficial; ii) à eficiência das tecnologias para eliminar ou reduzir o risco a valores de acordo à Portaria no 518 (2004); iii) ao tratamento, aproveitamento e disposição dos resíduos gerados e iv) aos custos dos sistemas com vazões de projeto de 10 a 40 L/s. As principais conclusões da pesquisa foram: i) a aplicabilidade do modelo está restrita à estações de tratamento de água - ETAs que cumprem todos seus requisitos de domínio, ii) os valores-limite das variáveis de risco podem conduzir o engenheiro a uma seleção preliminar das possíveis alternativas de tratamento; entretanto, somente a partir de estudos de tratabilidade da água e de testes em instalação piloto será possível definir a ETA mais conveniente, iii) as seleções das tecnologias para tratamento, aproveitamento e disposição do resíduo não devem ser avaliadas de forma independente às empregadas nas ETA; iv) Os custos calculados pelo modelo dificilmente podem ser comparados com sistemas já existentes; v) os resultados do modelo variam em função dos dados de entrada; assim, o usuário deve ter consciência da qualidade da informação fornecida para obter resultados satisfatórios. / Despite being significant the investments in the brazilian drinking water sector still have not presented the expected results regarding the improvement of both health and population\'s life quality, especially in the small communities of the country. The application of resources will continue limited while the technical, economical, institutional, environmental, social and cultural aspects that allow for the selection of efficient and maintainable sanitary works are not strengthened. In this context, a conceptual model to select technologies for water treatment has been developed. It consists of 17 sub-levels that progressively \"filter\" the applicable technological options in brazilian communities of under 20.000 inhabitants. The aspects involved are related to: i) the risk present in the source of superficial supply; ii) the efficiency of the technologies to lither eliminate or reduce the risk to the values according law no 518 (2004); iii) the treatment, use and disposition of the generated residues and iv) the costs of the systems with project flows from 10 L/s to 40L/s. The main conclusions of the research were: i) the applicability of the model is restricted to water treatment plants - WTPs that accomplish all their domain requirements, ii) the limit values of the risk variables can lead the engineer to a preliminary selection of the possible treatment alternatives; however, only from studies of water treatment and tests in pilot installations it will be possible to define the most convenient WTP, iii) the selections of the technologies for treatment, use and disposition of residues should not be appraised in an independent way of the ones employed in WTPs, iv) the costs calculated by the model can hardly be compared with systems already existent; v) the results of the model vary in function of the input data; therefore, the user should be aware of the quality of the information supplied to obtain satisfactory results.

This master‘s thesis deals with the solution of the production of the given part "APZ13". The structural analysis of the component is followed by the choice of the available manufacturing technology. The following is a theoretical description of the chosen technology and injection mold. Its solution is practically described in the following chapter and then the creation of TPV documentation of the whole project. The conclusion of this master’s thesis is a technical-economic evaluation of the chosen technology and discussion.

No description available.

Several techniques were studied to select and prioritize technologies for a complex system. Based on the findings, a method called Pareto Optimization and Selection of Technologies (POST) was formulated to efficiently explore the combinatorial technology space. A knapsack problem was selected as a benchmark problem to test-run various algorithms and techniques of POST. A Monte Carlo simulation using the surrogate models was used for uncertainty quantification. The concepts of graph theory were used to model and analyze compatibility constraints among technologies. A probabilistic Pareto optimization, based on the concepts of Strength Pareto Evolutionary Algorithm II (SPEA2), was formulated for Pareto optimization in an uncertain objective space. As a result, multiple Pareto hyper-surfaces were obtained in a multi-dimensional objective space; each hyper-surface representing a specific probability level. These Pareto layers enabled the probabilistic comparison of various non-dominated technology combinations. POST was implemented on a technology exploration problem for a 300 passenger commercial aircraft. The problem had 29 identified technologies with uncertainties in their impacts on the system. The distributions for these uncertainties were defined using beta distributions. Surrogate system models in the form of Response Surface Equations (RSE) were used to map the technology impacts on the system responses. Computational complexity of technology graph was evaluated and it was decided to use evolutionary algorithm for probabilistic Pareto optimization. The dimensionality of the objective space was reduced using a dominance structure preserving approach. Probabilistic Pareto optimization was implemented with reduced number of objectives. Most of the technologies were found to be active on the Pareto layers. These layers were exported to a dynamic visualization environment enabled by a statistical analysis and visualization software called JMP. The technology combinations on these Pareto layers are explored using various visualization tools and one combination is selected. The main outcome of this research is a method based on consistent analytical foundation to create a dynamic tradeoff environment in which decision makers can interactively explore and select technology combinations.

This thesis investigates the database model selection process in VSEs, looking into how priorities and needs differ compared to what is proposed by existing theory in the area.  The study was conducted as a case study of a two-person company engaged in developing various applications and performing consulting tasks. Data was collected through two semi-structured interviews. The first interview aimed to understand the company's process for selecting a database model, while the second interview focused on obtaining their perspective on any differences in their selection process compared to the theoretical recommendations and suggested methodology. The purpose was to investigate the important factors involved in the process and explore why and how they deviated from what the theory proposes. The study concludes that VSEs have different priorities compared to larger enterprises. Factors like transaction amount does not have to be considered much at the scale of a VSE. It is more important to look into the total cost of the database solution, including making sure that the selected technology is sufficiently efficient to use in development and relatively easy to maintain. Regarding selection methodology it was concluded that the time investment required to decide what is the best available database solution can be better spent elsewhere in the enterprise, and finding a good enough solution to get the wheels of the ground is likely a more profitable aim.

The following report consists of a master thesis (30 credits) within product development. The thesis is written by Philip Ragnartz and Axel Staffanson, both studying mechanical engineering at Mälardalens University. Developing new components for a production line is costly and time consuming as they must be made from manual measurements and must go through all the conventional manufacturing (CM) steps. Eventual design mistakes will be discovered after the component have been manufactured and tested. To fix the design a completely new component must be designed and therefore double the overall lead time. The purpose of this thesis is to establish how additive manufacturing (AM) can best be used to minimize the cost and lead time in the development of new components. The study was performed by looking at the current product development process in the automotive industry at a large company, here by referred to as company A. 56 components already manufactured at company A´s own tools department was examined and compared to different AM methods. The aim of this was to get a larger pool of data to get an average on production time and cost and see how this differ to the different AM methods. Additionally, two work holders were more closely examined in a case study. Work holder one is a component in the production line that occasionally must be remanufactured. It was examined if this problem could be solved with a desktop plastic printer to hold up for a production batch. Work holder two was the development of a new component, this was to examine the use of printing the component in an early stage impact the development process. The findings from this study is that AM can today not be used in a cost efficient way in manufacturing or development of simple components. This is due to the cost of a metal 3D-printer is still very high, and the building material even higher. This results in components that gets very expensive to make compared to producing them with CM. For design evaluation to be cost efficient there will have to be a design fault in over 12 % of the newly design components for it to be cost effective to print the design for validation before sending it to be manufactured. There are however a lot bigger potential savings in the lead time. Producing the end product with a metal 3D-printer can cut down the lead time up to 85 %. This is thanks to the fact that the printer will produce the component all in one step and therefore not get stuck in between different manufacturing processes. The same goes for design evaluation with printing the component in plastic to confirm the design and not risk having to wait for the component to be manufactured twice. Despite the facts that it is not cost efficient to use AM there are other factors that play an important role. To know that the designed components will work will create a certainty and allow the development process to continue. In some cases it will also allow the designer to improve the design to function better even if the first design would have worked. As AM is expanding machines and build materials will become cheaper. Eventually it will become cheaper to 3D-print even simple components compared to CM. When this occurs, a company cannot simply buy a 3D-printer and make it profitable. There is a learning curve with AM that will take time for the designers to adapt to. Therefore, it is good to start implementing it as soon as possible as it allows for more intricate designs and require experience to do so.

Pour faire face à la croissance rapide du trafic mobile, différentes technologies d'accès radio (par exemple, HSPA, LTE, WiFi, et WiMAX) sont intégrées et gérées conjointement. Dans ce contexte, la sélection de TAR est une fonction clé pour améliorer les performances du réseau et l'expérience de l'utilisateur. Elle consiste à décider quelle TAR est la plus appropriée aux mobiles. Quand l'intelligence est poussée à la périphérie du réseau, les mobiles décident de manière autonome de leur meilleur TAR. Ils cherchent à maximiser égoïstement leur utilité. Toutefois, puisque les mobiles ne disposent d'aucune information sur les conditions de charge du réseau, leurs décisions peuvent conduire à une inefficacité de la performance. En outre, déléguer les décisions au réseau optimise la performance globale, mais au prix d'une augmentation de la complexité du réseau, des charges de signalisation et de traitement. Dans cette thèse, au lieu de favoriser une de ces deux approches décisionnelles, nous proposons un cadre de décision hybride: le réseau fournit des informations pour les mobiles pour mieux décider de leur TAR. Plus précisément, les utilisateurs mobiles choisissent leur TAR en fonction de leurs besoins et préférences individuelles, ainsi que des paramètres de coût monétaire et de QoS signalés par le réseau. En ajustant convenablement les informations du réseau, les décisions des utilisateurs répondent globalement aux objectifs de l'opérateur. Nous introduisons d'abord notre cadre de décision hybride. Afin de maximiser l'expérience de l'utilisateur, nous présentons une méthode de décision multicritère (MDMC) basée sur la satisfaction. Outre leurs conditions radio, les utilisateurs mobiles tiennent compte des paramètres de coût et de QoS, signalées par le réseau, pour évaluer les TAR disponibles. En comparaison avec les solutions existantes, notre algorithme répond aux besoins de l'utilisateur (par exemple, les demandes en débit, la tolérance de coût, la classe de trafic), et évite les décisions inadéquates. Une attention particulière est ensuite portée au réseau pour s'assurer qu'il diffuse des informations décisionnelles appropriées, afin de mieux exploiter ses ressources radio alors que les mobiles maximisent leur propre utilité. Nous présentons deux méthodes heuristiques pour dériver dynamiquement quoi signaler aux mobiles. Puisque les paramètres de QoS sont modulées en fonction des conditions de charge, l'exploitation des ressources radio s'est avérée efficace. Aussi, nous nous concentrons sur l'optimisation de l'information du réseau. La dérivation des paramètres de QoS est formulée comme un processus de décision semi-markovien, et les stratégies optimales sont calculées en utilisant l'algorithme de Policy Iteration. En outre, et puisque les paramètres du réseau ne peuvent pas être facilement obtenues, une approche par apprentissage par renforcement est introduite pour dériver quoi signaler aux mobiles. / To cope with the rapid growth of mobile broadband traffic, various radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) are being integrated and jointly managed. Radio Access Technology (RAT) selection, devoted to decide to what RAT mobiles should connect, is a key functionality to improve network performance and user experience. When intelligence is pushed to the network edge, mobiles make autonomous decisions regarding selection of their most appropriate RAT. They aim to selfishly maximize their utility. However, because mobiles have no information on network load conditions, their decisions may lead to performance inefficiency. Moreover, delegating decisions to the network optimizes overall performance, but at the cost of increased network complexity, signaling, and processing load. In this thesis, instead of favoring either of these decision-making approaches, we propose a hybrid decision framework: the network provides information for the mobiles to make robust RAT selections. More precisely, mobile users select their RAT depending on their individual needs and preferences, as well as on the monetary cost and QoS parameters signaled by the network. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. We first introduce our hybrid decision framework. Decision makings, on the network and user sides, are investigated. To maximize user experience, we present a satisfaction-based Multi-Criteria Decision-Making (MCDM) method. In addition to their radio conditions, mobile users consider the cost and QoS parameters, signaled by the network, to evaluate serving RATs. In comparison with existing MCDM solutions, our algorithm meets user needs (e.g., traffic class, throughput demand, cost tolerance), avoiding inadequate decisions. A particular attention is then addressed to the network to make sure it broadcasts suitable decisional information, so as to better exploit its radio resources while mobiles maximize their own utility. We present two heuristic methods to dynamically derive what to signal to mobiles. While QoS parameters are modulated as a function of the load conditions, radio resources are shown to be efficiently exploited. Moreover, we focus on optimizing network information. Deriving QoS parameters is formulated as a semi-Markov decision process, and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies are analyzed. When thresholds are pertinently set, our heuristic method provides performance very close to the optimal solution. Moreover, although lower performances are observed, our learning-based algorithm has the crucial advantage of requiring no prior parameterization.

The new Industry 4.0 technologies have the potential to increase the operational performance of lean-driven manufacturing organizations, however, there is a lack of guidance on how to select and implement Industry 4.0 technologies which often leads to digital waste. In essence, these organizations want to preserve the value of their current lean manufacturing system, without missing out on improved operational performance potential from exploiting new I4.0 technologies. Furthermore, recent research suggests that there is a complementary effect between lean manufacturing and Industry 4.0 where higher use of both domains in a factory results in higher operational performance. However, in theory, it has not been established what influencing factors lead to the increase in operational performance when the two domains, lean manufacturing and Industry 4.0, are in high concurrent use. The purpose of this study is to identify the influencing factors in the implementation process of Industry 4.0 technologies that facilitate higher Industry 4.0 maturity within factories. In this case study, a qualitative method was used to obtain the empirical data by conducting a series of interviews with individuals working in a multi-national lean-driven manufacturing organization. From a thematic analysis, seven influencing factors were identified that contributed to higher Industry 4.0 maturity: three in the governance structure, two in the selection of Industry 4.0 technologies, and two in the implementation of Industry 4.0 technologies. In the governance structure, a shared digital vision, following a roadmap, and a standardized way of working with digitalization in place facilitated a more successful implementation process. In the selection, a demand-based selection and identification of a business case around the Industry 4.0 technology increased the possibility of the technology selected reaching an implementation stage. Further, in the implementation, having local cross-functional teams and actively developing the employees' knowledge, and providing training in digitalization resulted in more successful implementations. Further, a ripple effect was seen between the influencing factors: having an effective governance structure led to more demand-based selections and thereby more successful implementations that resulted in higher Industry 4.0 maturity. / De nya industri 4.0 teknologierna har potentialen att öka de operativa resultaten inom lean-drivna industriföretag, men det saknas vägledning för hur man väljer och implementerar industri 4.0 teknologier vilket oftast resulterar i digitalt slöseri. Industriföretagen vill bevara värdet av de lean-drivna tillverkningssystemen utan att gå miste om förbättrade resultat med hjälp av industri 4.0. Dessutom tyder den senaste forskningen på att det finns en kompletterande effekt mellan lean produktion och industri 4.0 teknologier, där högre användning av båda domänerna i en fabrik resulterar i bättre operativa resultat. I teorin har det dock inte fastställts vilka de påverkande faktorerna är som leder till ett förbättrat operativt resultat när de två domänerna, lean-produktion och industri 4.0, används i hög grad samtidigt. Syftet med studien är att identifiera vilka de påverkande faktorerna är i implementeringsprocessen av industri 4.0 teknologier som främjar en högre mognadsgrad av industri 4.0 i tillverkningsfabriker. I fallstudien användes en kvalitativ metod för att erhålla empirin genom att utföra en serie av intervjuer med individer som arbetar på ett multinationellt lean-drivet industriföretag. Utifrån en tematisk analys identifierades sju påverkande faktorer som bidrog till en högre mognadsgrad av industri 4.0: tre i styrningsstrukturen, två i valet av industri 4.0 teknologier och två i implementeringen av industri 4.0 teknologier. I styrningsstrukturen återfanns att en delad vision, att följa en roadmap och att ha ett standardiserat arbetssätt för digitalisering främjade en mer framgångsrik implementeringsprocess. Inom valet av teknologier återfanns att ett val baserat på efterfrågan och generering av ett business case för industri 4.0 teknologier ökade möjligheten för den valda teknologin att nå en implementeringsfas. Till sist, i implementeringsfasen visade det sig att lokala tvärfunktionella team och att aktivt utveckla medarbetarnas kunskaper, samt att ge utbildning i digitalisering, resulterade i mer framgångsrika implementeringar. Slutligen sågs en kedjeeffekt mellan de påverkande faktorerna, nämligen att ha en effektiv styrningsstruktur ledde till fler val baserade på efterfrågan och därmed fler framgångsrika implementeringar vilket resulterade i en högre mognadsgrad av industri 4.0.