Práce popisuje spojitost vládních výdajů a veřejných financí. Zabývá se členěním vládních výdajů, analýzou jejich efektivnosti a faktory působícími na jejich růst.Práce také obsahuje část teorie ekonomie veřejného sektoru, kdy se zaměřuje na efektivnost veřejného sektoru a problématice rovnosti a efektivnosti v ekonomice. Představuje také některé teoretické koncepty rovnosti, jako utilitarismus a rawlismus.V praktické části se zabývá problematikou ubytovacích stipendií na vysokých školách po změně systému jejich vyplácení v říjnu roku 2005. Ukazuje problematiku rovnosti a efektivnosti na příkladu ubytovacích stipendií v praxi.

This paper demonstrates that the distribution of county level agricultural land size in the United States is best described by a power-law distribution, a distribution that displays extremely heavy tails. This indicates that the majority of farmland exists in the upper tail. Our analysis indicates that the top 5% of agricultural counties account for about 25% of agricultural land between 1997-2012. The power-law distribution of farm size has important implications for the design of more efficient regional and national agricultural policies as counties close to the mean account for little of the cumulative distribution of total agricultural land. This has consequences for more efficient management and government oversight as a disruption in one of the counties containing a large amount of farmland (due to natural disasters, for instance) could have nationwide consequences for agricultural production and prices. In particular, the policy makers and government agencies can monitor about 25% of total agricultural land by overseeing just 5% of counties.

Evolutionary Structural Optimisation (ESO) is a relatively new design tool used to improve and optimise the design of structures. It is a heuristic method where a few elements of an initial design domain of finite elements are iteratively removed. Such a process is carried out repeatedly until an optimum design is achieved, or until a desired given area or volume is reached. There have been many contributions to the ESO procedure since its conception back in 1992. For example, a provision known as Bi-Directional ESO (BESO) has now been incorporated where elements may not only be removed, but added. Also, rather than deal with elements where they are either present or not, the designer now has the option to change the element's properties in a progressive fashion. This includes the modulus of elasticity, the density of the material and the thickness of plate elements, and is known as Morphing ESO. In addition to the algorithmic aspects of ESO, a large preference exists to optimise a structure based on a selection of criteria for various physical processes. Such examples include stress minimisation, buckling and electromagnetic problems. In a changing world that demands the enhancement of design tools and methods that incorporate optimisation, the development of methods like ESO to accommodate this demand is called for. It is this demand that this thesis seeks to satisfy. This thesis develops and examines the concept of multicriteria optimisation in the ESO process. Taking into account the optimisation of numerous criteria simultaneously, Multicriteria ESO allows a more realistic and accurate approach to optimising a model in any given environment. Two traditional methods � the Weighting method and the Global Criterion (Min-max) method have been used, as has two unconventional methods � the Logical AND method and the Logical OR method. These four methods have been examined for different combinations of Finite Element Analysis (FEA) solver types. This has included linear static FEA solver, the natural frequency FEA solver and a recently developed inertia FE solver. Mean compliance minimisation (stiffness maximisation), frequency maximisation and moment of inertia maximisation are an assortment of the specific objectives incorporated. Such a study has provided a platform to use many other criteria and multiple combinations of criteria. In extending the features of ESO, and hence its practical capabilities as a design tool, the creation of another optimisation method based on ESO has been ushered in. This method concerns the betterment of the bending and rotational performance of cross-sectional areas and is known as Evolutionary Moment of Inertia Optimisation (EMIO). Again founded upon a domain of finite elements, the EMIO method seeks to either minimise or maximise the rectangular, product and polar moments of inertia. This dissertation then goes one step further to include the EMIO method as one of the objectives considered in Multicriteria ESO as mentioned above. Most structures, (if not all) in reality are not homogenous as assumed by many structural optimisation methods. In fact, many structures (particularly biological ones) are composed of different materials or the same material with continually varying properties. In this thesis, a new feature called Constant Width Layer (CWL) ESO is developed, in which a distinct layer of material evolves with the developing boundary. During the optimisation process, the width of the outer surrounding material remains constant and is defined by the user. Finally, in verifying its usefulness to the practical aspect of design, the work presented herein applies the CWL ESO and the ESO methods to two dental case studies. They concern the optimisation of an anterior (front of the mouth) ceramic dental bridge and the optimisation of a posterior (back of the mouth) ceramic dental bridge. Comparisons of these optimised models are then made to those developed by other methods.

L'objectif de cette thèse est de construire un modèle statistique de la distribution des tailles des gisements d'hydrocarbures qui existent dans le sous-sol d'un bassin sédimentaire, ainsi que de celle des découvertes déjà effectuées. L'estimation des paramètres de ce modèle via l'estimation de la densité des observations par sélection de modèles de polynômes par morceaux par maximum de vraisemblance pénalisé nous permet de déduire des estimations du nombre de gisements restant à découvrir, par classe de taille. Nous supposons que l'ensemble des tailles des champs qui existent dans la nature est un échantillon d'effectif inconnu d'une loi de Lévy-Pareto de paramètre lui-même inconnu. Les champs déjà découverts en représentent un sous échantillon sans remise biaisé par un "effet taille", dont les probabilités d'inclusion sont à estimer. Nous montrons que la densité des observations est le produit de la densité sous-jacente et d'une fonction de pondération inconnue représentant le biais dans le tirage. Une partition arbitraire de l'intervalle des tailles étant fixée (un modèle), les solutions analytiques des programmes de maximisation de la vraisemblance permettent d'estimer le paramètre de la loi sous-jacente ainsi que la fonction de pondération supposée en escalier et basée sur la partition. Nous ajoutons éventuellement une contrainte de monotonie sur cette dernière, rendant compte du fait que plus un objet est grande taille et plus sa probabilité d'avoir été découvert est importante. Des estimateurs de type Horvitz-Thompson permettent alors de conclure. Nous faisons ensuite varier les partitions au sein de différentes classes et démontrons un théorème de sélection de modèles permettant de choisir la partition la mieux adaptée, en terme de risques Hellinger et Kullback de l'estimateur associé. Nous concluons par des simulations, ainsi que plusieurs applications à des données réelles de bassins sédimentaires de quatre continents pour illustrer les aspects tant théoriques que pratiques de notre modélisation.

In this study, two new Multi-Objective Optimization (MOO) techniques are developed. The two new techniques, the Objective-Based Gradient Algorithm (OBGA) and the Principal Component Grid Algorithm (PCGA), were developed with the goals of improving the accuracy and efficiency of the Pareto domain approximation relative to current MOO techniques. Both methods were compared to current MOO techniques using several test problems. It was found that both the OBGA and PCGA systematically produced a more accurate Pareto domain than current MOO techniques used for comparison, for all problems studied. The OBGA requires less computation time than the current MOO methods for relatively simple problems whereas for more complex objective functions, the computation time was larger. On the other hand, the efficiency of the PCGA was higher than the current MOO techniques for all problems tested. The new techniques were also applied to complex chemical engineering problems. The OBGA was applied to an industrial reactor producing ethylene oxide from ethylene. The optimization varied four of the reactor input parameters, and the selectivity, productivity and a safety factor related to the presence of oxygen in the reactor were maximized. From the optimization results, recommendations were made based on the ideal reactor operating conditions, and the control of key reactor parameters. The PCGA was applied to a PI controller model to develop new tuning methods based on the Pareto domain. The developed controller tuning methods were compared to several previously developed controller correlations. It was found that all previously developed controller correlations showed equal or worse performance than that based on the Pareto domain. The tuning methods were applied to a fourth order process and a process with a disturbance, and demonstrated excellent performance.

In this study, two new Multi-Objective Optimization (MOO) techniques are developed. The two new techniques, the Objective-Based Gradient Algorithm (OBGA) and the Principal Component Grid Algorithm (PCGA), were developed with the goals of improving the accuracy and efficiency of the Pareto domain approximation relative to current MOO techniques. Both methods were compared to current MOO techniques using several test problems. It was found that both the OBGA and PCGA systematically produced a more accurate Pareto domain than current MOO techniques used for comparison, for all problems studied. The OBGA requires less computation time than the current MOO methods for relatively simple problems whereas for more complex objective functions, the computation time was larger. On the other hand, the efficiency of the PCGA was higher than the current MOO techniques for all problems tested. The new techniques were also applied to complex chemical engineering problems. The OBGA was applied to an industrial reactor producing ethylene oxide from ethylene. The optimization varied four of the reactor input parameters, and the selectivity, productivity and a safety factor related to the presence of oxygen in the reactor were maximized. From the optimization results, recommendations were made based on the ideal reactor operating conditions, and the control of key reactor parameters. The PCGA was applied to a PI controller model to develop new tuning methods based on the Pareto domain. The developed controller tuning methods were compared to several previously developed controller correlations. It was found that all previously developed controller correlations showed equal or worse performance than that based on the Pareto domain. The tuning methods were applied to a fourth order process and a process with a disturbance, and demonstrated excellent performance.

This thesis work concerns about the Performance evolution of peer to peer networks, where we used different distribution technique’s of peer distribution like Weibull, Lognormal and Pareto distribution process. Then we used a network simulator to evaluate the performance of these three distribution techniques.During the last decade the Internet has expanded into a world-wide network connecting millions of hosts and users and providing services for everyone. Many emerging applications are bandwidth-intensive in their nature; the size of downloaded files including music and videos can be huge, from ten megabits to many gigabits. The efficient use of network resources is thus crucial for the survivability of the Internet. Traffic engineering (TE) covers a range of mechanisms for optimizing operational networks from the traffic perspective. The time scale in traffic engineering varies from the short-term network control to network planning over a longer time period.Here in this thesis work we considered the peer distribution technique in-order to minimise the peer arrival and service process with three different techniques, where we calculated the congestion parameters like blocking time for each peer before entering into the service process, waiting time for a peers while the other peer has been served in the service block and the delay time for each peer. Then calculated the average of each process and graphs have been plotted using Matlab to analyse the results

In this thesis, the problem of quantifying the Pareto optimal boundary of the achievable rate region is considered over multiple-input single-output(MISO)interference channels, where the problem boils down to solving a sequence of convex feasibility problems after certain transformations. The feasibility problem is solved by two new distributed optimal beam forming algorithms, where the first one is to parallelize the computation based on the method of alternating projections, and the second one is to localize the computation based on the method of cyclic projections. Convergence proofs are established for both algorithms.

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The thesis examines the establishment size distribution over time and across groups of regions, using data on Swedish establishments during period 1994-2009. The size distribution of establishments is highly skewed and approximates the Pareto distribution. The shape of size distribution is invariant over time and across groups of regions. The distribution of total number of establishments and incumbent distribution are found to rise from the same distribution. Moreover, the invariance of establishment size distribution is highly determined by the invariance of distribution of incumbents, entry and exit distributions. Larger establishments have more chances to survive and higher probability to remain in current size group comparing to smaller ones, whereas higher probabilities of growth would be attached to smaller establishments.