Abstract
Healthcare organisations, especially in public sector, have been adopting Lean management practices with increasing outcomes’ evidences in several parts of the world, since the beginning of this century. However, Lean deployment in Healthcare services has been addressed in the literature in a surgical way by an array of case reports addressing the “hard” side of Lean deployment, sometimes with no result’s consistency or even follow-up analysis. This thesis seek to add to the operational side of Lean deployment in Healthcare, a complementary understanding of Lean deployment approaches, addressing both “hard” and “soft” sides, identifying the real constraints of Lean in Healthcare sector and the sustainability factors. Supported by two main literature reviews and a multi-case approach, a deep research on the eligible Portuguese cases was conducted answering the questions: (i) What are the different outcomes from Lean deployment in Healthcare?; (ii) What are the barriers to Lean implementation in Healthcare?; (iii) What enables Lean implementation in Healthcare?; (iv) What are the risks of Lean in Healthcare?; (v) How to measure Lean achievements in Healthcare services?; and (vi) How to develop a sustainable Lean culture? This contribution to the academic debate on Lean deployment in Healthcare creates clarity on what can be called Lean practices in Healthcare settings under the light of the concept’s founders; what pattern of a Lean deployment journey was followed by Healthcare organisations; and how different cultural (organisational and national) contexts can influence the pace in pursuing that pattern.;As organizações de saúde, nomeadamente públicas, têm vindo a adoptar práticas de gestão Lean com crescente evidência de resultados em várias partes do mundo, desde o início deste século. Contudo, a aplicação do Lean em serviços de saúde tem tido um tratamento cirúrgico na literatura, recaindo apenas nos aspectos “hard” e sem grande consistência ou seguimento de resultados . Esta tese pretende acrescentar aos aspectos “hard” do Lean, um entendimento complementar juntando os aspectos “hard” e “soft”, identificando as restrições e factores de sustentabilidade da aplicação do Lean no sector da saúde. Tendo por base duas revisões bibliográficas primordiais e uma abordagem empírica multi-caso a partir de casos portugueses elegíveis, esta tese fornece respostas às questões: (i) Quais os diferentes resultados da aplicação do Lean na Saúde?; (ii) Quais as barreiras à aplicação do Lean na Saúde?; (iii) Quais os facilitadores da implementação do Lean na Saúde?; (iv) Quais os riscos do Lean na Saúde?; (v) Como medir a implementação do Lean na Saúde; e (vi) como desenvolver uma cultura Lean sustentável? Este contributo para o debate académico sobre a aplicação do Lean na Saúde introduz clareza sobre o que pode ou não ser chamado de práticas Lean na Saúde tendo como referência os conceitos dos fundadores; que padrão de implementação é seguido pelas organizações; e de que forma diferentes contextos culturais (nacionais e organizacionais) influenciam o ritmo desse padrão de implementação.

Abstract
Direct methods are becoming the most used technique to solve nonlinear optimal control problems. Regular time meshes having equidistant spacing are frequently used. However, in some cases these meshes cannot cope accurately with nonlinear behavior. One way to improve the solution is to select a new mesh with a greater number of nodes. Another way, involves adaptive mesh refinement. In this case, the mesh nodes have non equidistant spacing which allow a non uniform nodes collocation. In the method presented in this paper, a time mesh refinement strategy based on the local error is developed. After computing a solution in a coarse mesh, the local error is evaluated, which gives information about the subintervals of time domain where refinement is needed. This procedure is repeated until the local error reaches a user-specified threshold. The technique is applied to solve the car-like vehicle problem aiming minimum consumption. The approach developed in this paper leads to results with greater accuracy and yet with lower overall computational time as compared to using a time meshes having equidistant spacing.

Abstract
Maximizing the performance of cooperative perception of a tracked target by a team of mobile robots while maintaining the team's formation is the core problem addressed in this work. We propose a solution by integrating the controller and the estimator modules in a formation control loop. The controller module is a distributed non-linear model predictive controller and the estimator module is based on a particle filter for cooperative target tracking. A formal description of the integration followed by simulation and real robot results on two different teams of homogeneous robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target's cooperative estimate while complying with the performance criteria such as keeping a pre-set distance between the team-mates and/or the target and obstacle avoidance.

Abstract
In our previous work, we proposed to use a vehicle network for data gathering, i.e. as an urban sensor. In this paper, we aim at understanding the theoretical limits of data gathering in a time slotted wireless network in terms of maximum service rate per node and end to end packet delivery ratio. The capacity of wireless networks has been widely studied and boundaries for that capacity expressed in Bachmann-Landau notation [ 1]. But these asymptotic limits do not clarify the numeric limits on data packets that can be carried by a wireless network. In this paper, we calculate the maximum data that each node can generate before saturating the network. The expected number of collision and its effect of the PDR% and service rate are investigated. The results quantify the trade off between packet delivery rate and service rate. Finally, we verify our analytical results by simulating the same scenario.

Abstract
For optimal control problems involving ordinary differential equations and functional inequality state constraints, the maximum principle may degenerate, producing no useful information about minimizers. This is known as the degeneracy phenomenon. Several non-degenerate forms of the maximum principle, valid under different constraint qualifications, have been proposed in the literature. In this paper we propose a new constraint qualification under which a nondegenerate maximum principle is validated. In contrast with existing results, our constraint qualification is of an integral type. An advantage of the proposed constraint qualification is that it is verified on a larger class of problems with nonsmooth data and convex velocity sets.

Abstract
The smart city is a sustainable and efficient urban center that provides high quality of life to its inhabitants with an optimal management of its resources, where clean and cost effective energy generation is a key issue. Under this setting, distributed generation can provide an adequate tool to deal with energy reliability and to successfully implement renewable sources; nevertheless, selection and scaling of energy systems, considering location, is not a trivial task. Frequently, the stakeholders analyze only one or two 'popular' generation systems, and then calculate the output and return of investment in a simplified and approximated approach. This practice could lead the stakeholder to an inadequate technology mix. To tackle this problem, this paper reviews and models most common energy sources for distributed generation in a smart city context. Then, a technical economic analysis is developed for 2 cases, a household and a district, considering not only renewable sources but also efficient non-renewable technologies. The results of the numerical analysis help to assess the more adequate generation systems for a given application, energetic demand, and geographical characteristics. A well-developed analysis is essential for a better understanding of the available technologies and their synergies; as a result, the investors can choose the appropriate solutions, maximizing overall benefits. © 2013 IEEE.