Le système de santé français est confronté au défi d’augmentation permanente de la demande en soins, sous une forte pression financière. Dans la stratégie nationale de santé, une des grandes orientations est de développer une base de coopération impliquant l’ensemble des acteurs et de leur engagement. Ces enjeux demandent au génie hospitalier de rechercher une efficience dans une échelle encore plus globale, ce qui demande d’intégrer les problèmes locaux et leurs outils d’optimisation qui présentent en général un haut degré de fragmentation, afin de contribuer à l’amélioration globale du système. Dans ce contexte-là, initialisé par un projet de conception du système de soins en réseau avec ressource de production mutualisée, nous proposons à travers ce mémoire de thèse une méthode générique pour résoudre le problème d’optimisation multi-niveaux dans lequel les décisions interdépendantes doivent être prises à différents niveaux dans une structure hiérarchique, ou aux étapes successives. Les décisions faites sont souvent corrélées, surtout pour une topologie de décisions enchaînées en hiérarchique que nous définissons sous le terme de « sous-structure optimale feedback ». La résolution de ce type de problème doit s’adapter pour prendre en compte autant que possible les implications liées aux décisions corrélées. La méthode proposée est basée sur la méta-heuristique PSO, elle utilise une procédure récursive pour définir le transfert des paramètres des sous-problèmes descendant et des évaluations ascendant à travers de multiples espaces de recherche, en assurant la cohérence de la convergence du problème global. Les applications et les analyses ont montrées que la méthode est assez générique et capable de produire la performance et la qualité de résolution proche de celles de la littérature / French healthcare system confronts the challenges of permanent increase in demand for healthcare, under heavy financial pressure. In the national healthcare strategy, a key focus is to develop a cooperation framework involving all organizations and units. These challenges require healthcare engineering to find efficiency in a more global scale, which means to integrate local optimization problems and decision tools that have generally a high degree of fragmentation in order to contribute to the overall improvement of the system. In this thesis, initiated by a shared unit-dose drug distribution system design project, a generic method was developed to solve the multi-level optimization problem in which interdependent decisions are made at different levels in a hierarchical structure, or at successive stages. The decisions made are often correlated, particularly for decisions in hierarchical topologies that we define by the term "optimal substructure with feedback". The resolution of this problem must be adapted to take into account all implications for correlated decisions. The proposed method is based on the meta-heuristic PSO, it uses a recursive procedure to define the top-down transfer of parameters and the bottom-up feedback of fitness through multiple search spaces, and ensures the consistency of global problem convergence. Our applications and analyzes have shown that this method is generic and is able to provide similar resolution performance and quality compared to the literature references

La production d’électricité par voie solaire apparait comme la solution la plus prometteuse pour l’avenir, tant en termes de coûts que de pollution. Cependant, afin d’atteindre le niveau de technologie requis pour envisager l’implémentation de telles centrales à grande échelle, plusieurs verrous technologiques et scientifiques sont encore à lever.Dans cette optique, les récepteurs/absorbeurs volumiques pourraient permettre d’atteindre de plus hautes températures que les récepteurs surfaciques (technologie actuellement utilisée dans les tours solaires à concentration), permettant l’usage de cycles thermodynamiques à haute rendement, tels que les cycles Brayton. Via le projet ANR-OPTISOL, la thèse présentée ici veut répondre en partie à ces problématiques par l’étude des absorbeurs solaires volumiques :- Une étude expérimentale des mousses céramiques utilisées comme absorbeur solaire volumique haute température a été menée au laboratoire CNRS-PROMES (UPR 8521). Une expérience a été conçue afin de tester des échantillons de 5cm de diamètre soumis au flux solaire concentré en conditions quasi-1D au foyer d’un four solaire à axe vertical ;- Un code de calcul des transferts thermiques couplés en milieu poreux a été développé utilisant l’hypothèse de « milieu homogène équivalent », puis validé sur les campagnes expérimentales ;- Finalement, un algorithme d’optimisation par essaim de particules a été utilisé afin de déterminer les propriétés géométriques optimales de mousses céramiques maximisant l’efficacité de conversion thermosolaire. / Solar-to-electricity power plants appear to be the most promises way for large electricity production in the future, in terms of costs as well as environmental impacts. Thus, reaching the required technology level still requires research and innovations in order to implement such power plants at large scale.In this context, volumetric solar receivers/absorbers could allow us to reach higher temperatures in comparison to surface receivers (actual concentrating solar power technology used in solar towers), leading to high efficiency thermodynamical cycles such as Brayton cycles. With the ANR-OPTISOL project, this thesis tends to give new answers on volumetric solar absorbers using ceramic foams:- Experimental studies of open pores ceramic foams used as high temperature volumetric solar absorber have been conducted at CNRS-PROMES laboratory (UPR 8521), with designed of a dedicated experiment for 5cm diameter samples operating under quasi-1D conditions submitted to concentrated solar power at the focal point of a vertical axis solar furnace;- A numerical code has been developed in order to solve coupled heat transfers in porous medium using the “equivalent homogeneous medium” hypothesis, then validated on the experimental campaigns;- Finally, an optimization algorithm has been used (“particle swarm optimization”) aiming the identification of the optimal geometrical characteristics maximizing the solar-to-thermal efficiency of ceramic foams.

The maintenance schedule of generators in power plants needs to match the electricity demand and needs to ensure the reliability of the power plant at a minimum cost of operation. In this study, a comparison is made between the modified generator maintenance scheduling model and the classic generator maintenance scheduling model using the reliability objective functions. Both models are applied to a 21-unit test system, and the results show that the modified generator maintenance scheduling model gives better and more reliable solutions than the regular generator maintenance scheduling model. The better results of the modified generator maintenance scheduling model are due the modified and additional constraints in the modified generator maintenance scheduling model. Due to the reliable results of the modified generator maintenance scheduling model, a robust model is formulated using the economic cost objective function. The model includes modified crew and maintenance window constraints, with some additional constraints such as the relationship constraints among the variables. To illustrate the robustness of the formulated GMS model, the maintenance of the Arnot power plant in South Africa is scheduled with open-loop and closed-loop controllers. Both controllers satisfy all the constraints but the closed-loop results are better than the open-loop results. AFRIKAANS : Die onderhoudskedule vir kragopwekkers (OSK) in kragstasies moet kan voorsien in die vraag na elektrisiteit en moet die betroubaarheid van die kragstasie teen ’n minimum operasiekoste verseker. In hierdie studie word die betroubaarheidsdoelwitfunksie gebruik om ’n gewysigde onderhoudskeduleringsmodel vir kragopwekkers te vergelyk met die konvensionele onderhoudskeduleringsmodel. Beide modelle word toegepas op 'n 21-eenheid-toetsstelsel, en die resultate toon dat die gewysigde model ’n beter en meer betroubare oplossing bied as die konvensionele model. Die beter resultate van die gewysigde model is die gevolg van die gewysigde en bykomende beperkings in die gewysigde model. As gevolg van die betroubare resultate van die gewysigde onderhoudskeduleringsmodel word die koste-ekonomie-doelwitfunksie gebruik om ’n robuuste model te formuleer. Die model sluit gewysigde bemanning- en onderhoudvensterbeperkings in, met ’n paar bykomende beperkings soos die verhoudingsbeperkings tussen die veranderlikes. Om die robuustheid van die geformuleerde OSK-model te illustreer word die instandhouding van die Arnot kragstasie in Suid-Afrika geskeduleer met oop- en geslotelus-beheerders. Beide beheerders voldoen aan al die beperkinge, maar die geslotelusresultate is beter as die ooplusresultate. / Dissertation (MSc)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / Unrestricted

Les travaux de thèse présentés s’intéressent à l’optimisation des systèmes hospitaliers. Une solution existante est la mutualisation de ressources au sein d’un même territoire. Cela peut passer par différentes formes de coopération dont la Communauté Hospitalière de Territoire. Différents problèmes sont définis en fonction du niveau de décision : stratégique, tactique ou opérationnel ; et du niveau de modélisation : macroscopique, mesoscopique et microscopique. Des problèmes de dimensionnement, de planification et d’ordonnancement peuvent être considérés. Nous définissons notamment le problème de planification d’activités avec affectation de ressources. Plusieurs cas sont dissociés : soit les ressources humaines sont à capacité infinie, soit elles sont à capacité limitée et leur affectation sur site est une donnée, soit elles sont à capacité limitée et leur affectation sur site est une variable. Ces problèmes sont spécifiés et formalisés mathématiquement. Tous ces problèmes sont comparés à un problème de bin packing : le problème du bin packing de base pour le problème où les ressources humaines sont à capacité infinie, le problème du bin packing avec interdépendances dans les deux autres cas. Le problème du bin packing avec incompatibilités est ainsi défini. De nombreuses méthodes de résolution ont déjà été proposées pour le problème du bin packing. Nous faisons plusieurs propositions dont un couplage hiérarchique entre une heuristique et une métaheuristique. Des métaheuristiques basées individu et une métaheuristique basée population, l’optimisation par essaim particulaire, sont utilisées. Cette proposition nécessite un nouveau codage inspiré des problèmes de permutation d’ordonnancement. Cette méthode donne de très bons résultats sur les instances du problème du bin packing. Elle est simple à appliquer : elle couple des méthodes déjà connues. Grâce au couplage proposé, les nouvelles contraintes à considérer nécessitent d’être intégrées uniquement au niveau de l’heuristique. Le fonctionnement de la métaheuristique reste le même. Ainsi, notre méthode est facilement adaptable au problème de planification d’activités avec affectation de ressources. Pour les instances de grande taille, le solveur utilisé comme référence ne donne qu’un intervalle de solutions. Les résultats de notre méthode sont une fois encore très prometteurs : les solutions obtenues sont meilleures que la borne supérieure retournée par le solveur. Il est envisageable d’adapter notre méthode sur d’autres problèmes plus complexes par intégration dans l’heuristique des nouvelles contraintes à considérer. Il serait notamment intéressant de tester ces méthodes sur de réelles instances hospitalières afin d’évaluer leur portée. / The presented work is about optimization of the hospital system. An existing solution is the pooling of resources within the same territory. This may involve different forms of cooperation between several hospitals. Various problems are defined at the decision level : strategic, tactical or operational ; and at the modeling level : macroscopic, mesoscopic and microscopic. Problems of sizing, planning and scheduling may be considered. We define the problem of activities planning with resource allocation. Several cases are dissociated : either human resources are under infinite capacity, or they are under limited capacity and their assignment on a place is given, or they are under limited capacity and their assignment is a variable. These problems are specified and mathematically formalized. All thes problems are compared to a bin packing problem : the classical problem of bin packing is used for the problem where human resources are under infinite capacity, the bin packing problem with interdependencies is used in the two other cases. The bin packing problem with incompatibilities is defined. Many resolution methods have been proposed for the bin packing problem. We make several propositions including a hierarchical coupling between heuristic and metaheuristic. Single based metaheuristics and a population based metaheuristic, the particle swarm optimization, are used. This proposition requires a new encoding inspired by permutation problems. This method gives very good results to solve instances of the bin packing problem. It is easy to apply : it combines already known methods. With the proposed coupling, the new constraints to be considered need to be integrated only on the heuristic level. The running of the metaheuristic is the same. Thus, our method is easily adaptable to the problem of activities planning with resource allocation. For big instances, the solver used as a reference returns only an interval of solutions. The results of our method are once again very promising : the obtained solutions are better than the upper limit returned by the solver. It is possible to adapt our method on more complex issues through integration into the heuristic of the new constraints to consider. It would be particularly interesting to test these methods on real hospital authorities to assess their significance.

Choice of the constitutive model and determination of input parameters are necessary for a successful application of numerical methods in geotechnical engineering. Higher complexity of modern constitutive models results in an increase of the number of input parameters and time requirements for their calibration. Optimization methods are a possible solution for this problem. An application in which metaheuristic optimization method Particle swarm optimization (PSO) is involved is presented in this thesis. Critical review and testing of various PSO alternatives was performed in the first part of this thesis. The most efective PSO alternatives were chosen. In the second part connection between PSO algorithm and finite element solver was prepared. Automatization of determination of constitutive models input parameters was performed on three boundary value problems: laboratory test (oedometer), in-situ test (pressuremeter) and geotechical construction (retaining wall). Three types of constitutive models are used. Linear elastic-perfectly plastic Mohr-Coulomb model, elastoplastic non-linear Hardening soil model and Hardening soil - small strain model.

This diploma thesis focuses on meta-heuristic algorithms and their ability to solve difficult optimization problems in polynomial time. The thesis describes different kinds of meta-heuristic algorithms such as genetic algorithm, particle swarm optimization or ant colony optimization. The implemented application was written in Java and contains ant colony optimization for capacitated vehicle routing problem and particle swarm optimization which finds the best possible parameters for ant colonies.

This diploma thesis deals with the path planning by swarm intelligence. In the theoretical part it describes the best known methods of swarm intelligence (Ant Colony Optimization, Bee Swarm Optimization, Firefly Swarm Optimization and Particle Swarm Optimization) and their application for path planning. In the practical part particle swarm optimization is selected for the design and implementation of path planning in the C#.

The diploma thesis deals with the image registration using evolutionary algorithms from metaheuristic optimization techniques which are considered recent and widely used. The frst part of the thesis contains theoretical description of components in image registration and later focuses on the ultrasound images. In that part, the thesis explains chosen evolutionary algorithms. Three optimization methods have been implemented, in particular genetic algorithm, particle swarm optimization and frey algorithm. Chosen similarity metrics for optimization are sum of squared dierences, cosine similarity and correlation coefcient. The main part of thesis includes testing of proposed methods with the evaluation of obtained results. These parameters are later used for optimization of real ultrasound sequences obtained by the contrast imaging.

<div>In this thesis work, a novel approach for the design of a fractional order proportional integral</div><div>derivative (FOPID) controller is proposed. This design introduces a new time-varying FOPID controller</div><div>to mitigate a voltage spike at the controller output whenever a sudden change to the setpoint occurs. The</div><div>voltage spike exists at the output of the proportional integral derivative (PID) and FOPID controllers when a</div><div>derivative control element is involved. Such a voltage spike may cause a serious damage to the plant if it is</div><div>left uncontrolled. The proposed new FOPID controller applies a time function to force the derivative gain to</div><div>take effect gradually, leading to a time-varying derivative FOPID (TVD-FOPID) controller, which maintains</div><div>a fast system response and signi?cantly reduces the voltage spike at the controller output. The time-varying</div><div>FOPID controller is optimally designed using the particle swarm optimization (PSO) or genetic algorithm</div><div>(GA) to ?nd the optimum constants and time-varying parameters. The improved control performance is</div><div>validated through controlling the closed-loop DC motor speed via comparisons between the TVD-FOPID</div><div>controller, traditional FOPID controller, and time-varying FOPID (TV-FOPID) controller which is created</div><div>for comparison with all three PID gain constants replaced by the optimized time functions. The simulation</div><div>results demonstrate that the proposed TVD-FOPID controller not only can achieve 80% reduction of voltage</div><div>spike at the controller output but also is also able to keep approximately the same characteristics of the system</div><div>response in comparison with the regular FOPID controller. The TVD-FOPID controller using a saturation</div><div>block between the controller output and the plant still performs best according to system overshoot, rise time,</div><div>and settling time.</div>

The PID controller is regarded as a dependable and reliable controller for process industry systems. Many researchers have devoted time and attention to PID controller tuning and they all agree that PID controllers are very important for control systems. A PID equation is very sensitive; its parameters must always be varied following the specific application to increase performance, such as by increasing the system’s responsiveness. PID controllers still have many problems despite their importance for control systems in industries. The problem of big overshoot on the conventional gain tuning is one of the serious problems. Researchers use the PSO algorithm to try and overcome those problems. The tuning of the MIMO PID controller based on the PSO algorithm shows many disadvantages such as high-quality control with a short settle time, steady-state error, and periodical step response. The traditional PSO algorithm is very sensitive and it sometimes affects the quality of good PID controller tuning. This research has proposed a new equation for improving the PSO algorithm. The proposed algorithm is the combination of linearly decreasing inertia weight and chaotic inertia weight, after which a control factor was introduced as an exponential factor. This was very useful for simulations as it is adjustable. The Matlab simulation results of the experiments show that the simulations as it is adjustable. The Matlab simulation results of the experiments show that the new proposed equation converges faster and it gives the best fitness compared to linear inertia weight and oscillating inertia weight and other old equations. The MIMO PID controller system that consists of four plants was tuned based on the new proposed equation for the PSO algorithm (LCPSO). The optimized results show the best rise time, settling time, time delays, and steady-state compared to the systems that are tuned using the old equations. The exploration was directed at considering the impact of using the PSO calculation as an instrument for MIMO PID tuning. The results obtained in the examination reveal that the PSO tuning output improved reactions and can be applied to various system models in the measure control industry. The results for the MIMO PID controller tuned using PSO were assessed using integral square error (ISE), integral absolute error (IAE), and the integral of time expanded by absolute error (ITAE). The five well-known benchmark functions were also used to endorse the feasibility of the improved PSO and excellent results in terms of convergence and best fitness were attained. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)